OpenAI 正通过多项举措推进科学计算和 AI 安全。该公司发布了一个新的基准 GeneBench-Pro，以评估 AI 代理处理复杂生物数据的能力。OpenAI 还在欧洲为可信赖 AI 的共享标准制定做出贡献，并与伦敦证券交易所集团等组织合作，将 AI 整合到业务运营中。与此同时，Google DeepMind 正在向多智能体 AI 安全研究投资 1000 万美元，旨在理解和减轻与交互式 AI 系统相关的风险。
AI
影响
专注于用于科学发现的 AI 代理和多智能体安全研究，突显了未来 AI 发展和风险缓解的关键领域。
Introducing Muse Glimmer, an open-weight 30B-parameter model optimized for local, always-on agent workflows.
Muse Glimmer delivers strong performance on key agentic use cases and benchmarks compared with leading models in its size category, and is designed to run entirely on htt…
A new field report shows how scientists use AI coding agents to modernize scientific computing, accelerating software development and discovery in genomics and beyond.
We’re introducing GeneBench-Pro, a research-level benchmark for a harder kind of AI progress: how well agents can navigate messy biological data, choose the right analysis path, and make judgment calls that real computational research depends on.
https://t.co/AsilnnSxnE
OpenAI helps build shared standards for advanced AI, supporting evaluation frameworks, safety practices, and global cooperation through the Appia Foundation.
X — Google DeepMind
TIER_1English(EN)·GoogleDeepMind·
When millions of AI agents interact with each other, new collective behaviors can emerge. 🌐
Together with @schmidtsciences, @coop_ai, @ARIA_research and supported by @GoogleOrg, we’re launching a $10M research fund to help understand how AI systems behave as a group. → https://t…
OpenAI supports the EU Code of Practice on AI content transparency, advancing provenance standards and tools to help people understand AI-generated content.
See how LSEG uses OpenAI to scale trusted AI across its global business, accelerating insights, shrinking release cycles, and empowering 4,000 employees.
Learn how Endava is using AI agents, ChatGPT Enterprise, and Codex to accelerate software delivery, automate workflows, and build an AI-native culture across the enterprise.
Serving a wide range of AI models on a global scale, while maintaining the lowest possible costs, is one of the most demanding infrastructure challenges in the industry.
Today, we’re releasing new tools to help developers go from prototype to production faster: AgentKit, expanded evals capabilities, and reinforcement fine-tuning for agents.
New AI agent evolves algorithms for math and practical applications in computing by combining the creativity of large language models with automated evaluators
<p>Computer-use AI agents struggle with multi-step workflows like email and customer support. Echoverse trains agents in realistic environments rather than simply providing more training tasks, helping them improve as the tasks, tests, and environments evolve.</p> <p>The post <a …
The web is complex, open-ended, and constantly changing, making it challenging to scale training data for visual web agents. Existing data collection attempts remain limited to offline trajectories for supervised fine-tuning or a handful of simulated environments for RL training,…
<p>AI agents often fail because their instructions, or skills, are manually modified with no guarantee of improvement. Learn how SkillOpt turns skill editing into a training process, making agent behavior more reliable without changing model weights.</p> <p>The post <a href="http…
<p>Understanding AI as an extension of human intelligence—not a replacement for it—offers a more grounded path for building trustworthy AI systems.</p> <p>The post <a href="https://www.microsoft.com/en-us/research/blog/extending-human-intelligence-through-ai/">Extending Human Int…
<p>MagenticLite is an agentic system for small models that works across the browser and local file system in a single workflow. It combines specialized models and orchestration to support efficient agentic performance on everyday tasks.</p> <p>The post <a href="https://www.micros…
Today we introduce Qwen3.7-Max, our latest proprietary model designed for the agent era. Qwen3.7-Max is built to be a versatile agent foundation — equally capable of writing and debugging code, automating office workflows, and sustaining autonomous execution across hundreds or th…
Following the release of the Qwen3.5 series in February, we are thrilled to announce the official launch of Qwen3.6-Plus. Available immediately via our API, this release represents a massive capability upgrade over its predecessor. Most notably, we have drastically enhanced the m…
arXiv:2608.10333v1 Announce Type: new Abstract: LLM agents execute heterogeneous sequences of model calls within a single task: some invocations require careful reasoning, while others are structured steps such as formatting or tool-argument construction. Prior routing methods ex…
arXiv cs.AI
TIER_1English(EN)·Giuseppe Destefanis, Daniel Graziotin, Matteo Vaccargiu, Marco Ortu·
arXiv:2608.10906v1 Announce Type: cross Abstract: An agent skill is a folder containing a SKILL.md file with instructions for a language-model agent, optionally accompanied by scripts and reference files. The agent loads the skill when it judges that a task matches the skill desc…
arXiv:2608.10216v1 Announce Type: cross Abstract: Agent frameworks ship quality gates that compare text blocks by embedding-cosine similarity and decide at a fixed cutoff. Deduplication filters, semantic caches, drift guards, and answer grader gates deploy to answer the question:…
arXiv cs.AI
TIER_1English(EN)·Hanrong Zhang (Steve), Shicheng Fan (Steve), Henry Peng Zou (Steve), Yankai Chen (Steve), Zhenting Wang (Steve), Jiayu Zhou (Steve), Chengze Li (Steve), Wei-Chieh Huang (Steve), Yifei Yao (Steve), Kening Zheng (Steve), Xue (Steve), Liu, Xiaoxiao Li, Ph…·
arXiv:2604.01687v3 Announce Type: replace Abstract: Anthropic proposes the concept of skills for LLM agents to tackle multi-step professional tasks that simple tool invocations cannot address. A tool is a single, self-contained function, whereas a skill is a structured bundle of …
arXiv:2604.16706v2 Announce Type: replace Abstract: Automated evaluation of tool-using large language model (LLM) agents is widely assumed to be reliable, yet this assumption is rarely validated against human annotation. We present AgentProp-Bench, a diagnostic benchmark of 14,75…
arXiv cs.LG
TIER_1English(EN)·Shuo Hao, You Lu, Bihuan Chen, Xin Peng·
arXiv:2608.10039v1 Announce Type: new Abstract: Agentic workflows have become an important abstraction for building reliable LLM-based automation systems by organizing large language models (LLMs), tools, and control logic into explicit execution structures. However, constructing…
arXiv cs.AI
TIER_1English(EN)·Charles L. Wang, Keir Dorchen, Peter Jin·
arXiv:2510.04399v3 Announce Type: replace Abstract: We develop a learning-theoretic framework for analyzing self-improving agents by decomposing self-modification into five axes. Within this framework, we prove a sharp boundary: under standard i.i.d. assumptions, distribution-fre…
arXiv cs.LG
TIER_1English(EN)·Yanyu Ren, Xizheng Wang, Xiao Liu, Bowen Lv, Hanchen Zhang, Shudan Zhang, Hanyu Lai, Shuai Wang, Li Chen, Dan Li, Jie Tang·
arXiv:2608.10402v1 Announce Type: new Abstract: Reinforcement learning (RL) for large language models is moving toward multi-turn agentic workloads, where rollout tasks repeatedly pause for external environments, resume with growing contexts, and finish at highly variable times. …
arXiv:2608.10366v1 Announce Type: new Abstract: Real-world data science involves long-horizon workflows that span data wrangling, exploration, modeling, visualization, and validation, and require coordinated use of tools such as notebooks, IDEs, terminals, browsers, and databases…
arXiv cs.AI
TIER_1English(EN)·Au Kwok Chun, Abhigyan Acherjee, Amrutha Rao, Zaiqian Chen, Kazem Meidani, C. Bayan Bruss, Micah Goldblum·
arXiv:2608.10424v1 Announce Type: new Abstract: A slew of recent works develop agents for solving research problems end-to-end, a paradigm increasingly referred to as autoresearch. Such agents have inspired large industry investment, motivated by their potential to automate time-…
arXiv:2608.10504v1 Announce Type: new Abstract: As coding agents increasingly handle implementation, the central challenge shifts from building individual agents to building an infrastructure that systematically improves them. Current approaches optimize agent systems without acc…
arXiv cs.AI
TIER_1English(EN)·Fanqi Zhou, Qiaosheng Chen, Zixian Huang, Gong Cheng·
arXiv:2608.10579v1 Announce Type: new Abstract: Although existing instruction data selection methods have introduced various metrics, the inherent complexity of real-world datasets makes it impractical for any single metric to generalize across all scenarios. Developers are thus …
arXiv:2608.10299v1 Announce Type: new Abstract: Agentic systems are increasingly expected to improve after deployment, yet single-entity self-evolution is often bounded by a static learning context, such as fixed tasks and feedback. This survey focuses on co-evolution in agentic …
arXiv cs.AI
TIER_1English(EN)·Zixing Chen, Xingyuan Liu, Jie Zhu, Huaixia Dou, Shuo Jiang, Junhui Li, Lifan Guo, Feng Chen, Chi Zhang·
arXiv:2608.10669v1 Announce Type: new Abstract: Large language model (LLM) agents combine language-based reasoning with external tools to perform complex tasks. Adversarial inputs can exploit interactions between the agent and its environment, causing the agent to violate safety …
arXiv:2608.11079v1 Announce Type: new Abstract: Self-evolving agents accumulate reusable skills by appending successful procedures and failure fixes. Over time, the same requirement is often restated in several branches, examples, and warnings, while common action sequences are c…
Failure attribution in LLM-based multi-agent systems (MAS) aims to answer who caused failures, when they occurred, and why by identifying responsible targets including faulty agents, erroneous steps, and failure modes. Existing methods have primarily focused on developing dedicat…
arXiv cs.AI
TIER_1English(EN)·Dongyi Lv, Fushun E, Aichen Cai, Liang Huang, Ya Zhang, Qiuyu Ding, Canhui Wu, Zhi Wang, Yuesong Zhang, Jiaqi Wang, Nan Duan·
arXiv:2608.08570v1 Announce Type: new Abstract: Rejection sampling fine-tuning (RFT) is widely used to train code agents by generating trajectories on verifiable software engineering tasks, retaining those that pass the tests, and fine-tuning on the successful rollouts. However, …
arXiv:2608.08392v1 Announce Type: new Abstract: Large language models are increasingly deployed as autonomous agents that interact with the web through browsers. While recent progress has been driven by benchmarks that evaluate end-to-end task success, these evaluations largely o…
arXiv:2608.08264v1 Announce Type: new Abstract: Large language model agents are becoming operational interfaces to files, memories, registries, and external tools. This deployment shift creates a new skill revocation problem: after a skill is removed from an explicit registry, an…
arXiv cs.AI
TIER_1English(EN)·Xinle Jiang, Remy Xie, Ming Tang·
arXiv:2608.08037v1 Announce Type: new Abstract: LLM-based agent frameworks now act as personal assistants for multi-step tasks. Existing agent frameworks such as OpenClaw commonly follow the Cloud Agent depolyment mode using closed-source cloud LLMs as backbone model, which may e…
arXiv:2608.08453v1 Announce Type: new Abstract: Under the current standard, Agent Skills are SKILL.md files that combine instructions with supporting resources, enabling Large Language Model (LLM) agents to reuse procedures beyond a single conversation. Yet many public skills app…
arXiv cs.AI
TIER_1English(EN)·Donghong Jiang, Endian Lin, Luoping Cui, Hanqing Liu, Mingjie Liu, Fan Yang, Hong Wang, Zhao Yang, Chuang Zhu·
arXiv:2608.08640v1 Announce Type: new Abstract: Large language model agents increasingly rely on reusable skills to extend their capabilities beyond parametric knowl- edge. However, retrieving the appropriate skill from a large- scale library remains challenging because realistic…
arXiv:2608.08654v1 Announce Type: new Abstract: How much an AI coding agent costs to run can depend more on the agent scaffolding that drives it than on the interface through which it reaches its tools. We set out to measure the cost of tool use over the Model Context Protocol (M…
arXiv cs.AI
TIER_1English(EN)·Siqi Wang, Xinlin Li, Zhenglin Li, Li Li·
arXiv:2608.09380v1 Announce Type: new Abstract: Long-horizon complex tasks require agents to repeatedly observe states, formulate plans, invoke tools, verify results, and recover from failures in continuously changing environments. However, such control experience often remains c…
arXiv:2608.09485v1 Announce Type: new Abstract: Cooperative capabilities in language models are dual-use. The same social reasoning that supports civic deliberation can also enable strategic omission, false consensus, and manipulative framing. We argue that Cooperative AI evaluat…
arXiv:2608.09629v1 Announce Type: new Abstract: Self-evolving agents are usually built around prescribed optimization pipelines: the framework decides how to gather evidence, revise a persistent artifact, select candidates, and stop. We ask whether this task-specific procedure re…
arXiv:2608.08466v1 Announce Type: new Abstract: Modern LLM agents are often improved by modifying prompts, tools, or workflows manually, while the executable scaffold surrounding the model---the \emph{harness}---is typically treated as a fixed artifact after deployment. This work…
arXiv:2608.09130v1 Announce Type: cross Abstract: Allocating limited computation among concurrent learning tasks is difficult when each task must reach a target loss before a deadline but its required training effort is unknown. Existing approaches combine online loss prediction …
arXiv:2503.22122v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) have demonstrated remarkable capabilities in robotic planning, particularly for long-horizon tasks that require a holistic understanding of the environment for task decomposition. Existing met…
arXiv cs.AI
TIER_1English(EN)·Xinze Chen, Chi Zhang, Ping Ji, Yimin Liu·
arXiv:2608.09251v1 Announce Type: cross Abstract: Large language model-based multi-agent systems have recently shown strong potential for complex, long-horizon tasks. However, existing methods mainly rely on coarse prompt-level differentiation without parameter adaptation for div…
arXiv:2601.22758v2 Announce Type: replace Abstract: Large language model agents repeatedly encounter related tasks, yet systems that learn from trajectories commit every lesson to one predefined artifact form. A local constraint, a reusable procedure, and a delegated objective re…
arXiv cs.AI
TIER_1English(EN)·Jiaru Bai, Abdulrahman Aldossary, Thomas Swanick, Marcel M\"uller, Yeonghun Kang, Changhyeok Choi, Naruki Yoshikawa, Zijian Zhang, Jin Won Lee, Tsz Wai Ko, Aiwei Yin, Mohammad Ghazi Vakili, Chris Crebolder, Varinia Bernales, Al\'an Aspuru-Guzik·
arXiv:2602.17902v2 Announce Type: replace Abstract: Large language models (LLMs) can plan scientific workflows and generate code, but these capabilities do not specify how scientific state is validated, transferred and recorded across heterogeneous computational and experimental …
arXiv:2604.08377v2 Announce Type: replace Abstract: Large language model (LLM) agents such as OpenClaw rely on reusable skills to perform complex tasks, yet these skills remain largely static after deployment. As a result, similar workflows, tool usage patterns, and failure modes…
arXiv:2604.11741v2 Announce Type: replace Abstract: Vision-language models (VLMs) have shown impressive capabilities in perceptual tasks, yet they degrade in complex multi-hop reasoning under multiplayer game settings with imperfect and deceptive information. In this paper, we st…
arXiv:2604.05164v3 Announce Type: replace-cross Abstract: As LLM reasoning performance plateaus, improving inference-time compute efficiency is crucial to mitigate overthinking and long thinking traces even for simple queries. Prior approaches including length regularization, ada…
arXiv:2608.08793v1 Announce Type: new Abstract: Agent Skills package reusable instructions and assets for tool-using language-model agents. Progressive loading creates failure boundaries poorly represented by session-, model-, or tool-centric traces: a Skill can be discovered but…
arXiv:2608.09044v1 Announce Type: new Abstract: Continual self-evolution requires LLM agents to transform environmental interactions into reliable and reusable experience. Existing methods typically refine individual trajectories or abstract shared knowledge from related trajecto…
arXiv cs.CL
TIER_1English(EN)·Lisheng Huang, Chen Yang, Hao Zhou, Huatong Song, Zongchao Chen, Ran Le, Yang Song, Wayne Xin Zhao, Tao Zhang·
arXiv:2608.09096v1 Announce Type: new Abstract: Large Language Models (LLMs) have driven rapid progress in autonomous agents, yet standard evaluations remain confined to static task solving. An emerging frontier is harness evolution---the agent's capacity to autonomously optimize…
arXiv:2608.09524v1 Announce Type: cross Abstract: Incident response planning is critical for restoring compromised software systems after cyberattacks. Common practice relies on expert-driven playbooks that encode fixed response procedures, but these static workflows struggle to …
DSAgentBench evaluates autonomous agents on complete, multi-tool data-science workflows in real computing environments and reveals major performance gaps.
SkillZip compresses self-evolving agent skills by finding a minimal faithful structural explanation that shares repeated rules and procedures while preserving rare exceptions, without requiring evaluation rollouts.
Large language model-based multi-agent systems have recently shown strong potential for complex, long-horizon tasks. However, existing methods mainly rely on coarse prompt-level differentiation without parameter adaptation for diverse subtasks, resulting in insufficient inter-age…
Large language model-based multi-agent systems have recently shown strong potential for complex, long-horizon tasks. However, existing methods mainly rely on coarse prompt-level differentiation without parameter adaptation for diverse subtasks, resulting in insufficient inter-age…
arXiv cs.AI
TIER_1English(EN)·Daniel Koh Ji Yang, Yannic Noller, Corina S. Pasareanu, Youcheng Sun·
arXiv:2608.06397v1 Announce Type: cross Abstract: Symbolic execution seeks to explore feasible program paths, yet a practical run may exhaust its resources while much program behaviour remains unreached. We investigate a complementary way of extending its practical reach by reaso…
arXiv:2608.07449v1 Announce Type: new Abstract: LLM agents increasingly adapt to recurring tasks by accumulating procedural knowledge in skills. These skills are lightweight, reusable textual artifacts that are loaded into the agent's context without weight updates. Recent method…
arXiv:2608.07196v1 Announce Type: new Abstract: Many methods for automated multi-agent system design optimize prompts and topologies during an initial design stage and then deploy the resulting system unchanged on subsequent samples. Experience from these samples is rarely consol…
arXiv:2608.07346v1 Announce Type: new Abstract: With the rapid advancement of large language models (LLMs), harnesses have become essential infrastructure for deploying agents across a wide range of domains. The fast-evolving harness ecosystem has also made rigorous capability ev…
arXiv:2608.06984v1 Announce Type: cross Abstract: Modern agent harnesses persist state across tasks and sessions through persistent carriers like memory, skills, tools, and shared artifacts. However, this capability creates delayed safety risks: attacker-influenced content can cr…
arXiv cs.AI
TIER_1English(EN)·Lekang Jiang, Bohan Tang, Stephan Goetz, Yiwen Guo·
arXiv:2608.06410v1 Announce Type: new Abstract: Automated agent design improves agent harnesses through iterative revision, evaluation, and feedback summarization. Existing methods are largely candidate-centric: cross-round experience is organized around candidate agents, which l…
arXiv cs.AI
TIER_1English(EN)·Jing Chen, Yang Sun, Li Zhang, Lin Xu, Jie Shi·
arXiv:2608.06909v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly operate through long-horizon trajectories involving user instructions, tool use, external observations, and memory. Existing benchmarks primarily evaluate behavioral outcomes but provid…
arXiv cs.AI
TIER_1English(EN)·Shuyang Liu, Saman Dehghan, Ji Young Kim, Jatin Ganhotra, Martin Hirzel, Reyhaneh Jabbarvand·
arXiv:2608.06701v1 Announce Type: cross Abstract: Fixing GitHub issues in large-scale projects is a long-horizon task, especially when a fix requires changes across multiple locations or the issue description lacks the information needed to localize and repair it. As a result, ag…
arXiv:2608.06871v1 Announce Type: new Abstract: Complex systems, core objects of study in artificial life, model diverse phenomena through nonlinear, feedback-driven interactions that produce emergent behavior, with applications from population dynamics and biology to economic po…
arXiv:2608.06891v1 Announce Type: new Abstract: Agent skills provide reusable procedural knowledge that helps agents solve specialized tasks. As their use expands, evaluating skill quality becomes increasingly important. Existing evaluations often measure skill quality by testing…
arXiv:2608.06714v1 Announce Type: new Abstract: Recent systems for optimizing prompts, programs, and ML workflows typically rely on explicit outer-loop controllers such as evolutionary search, bandits, or textual-gradient methods. We ask a fundamentally different question: how mu…
Large Language Models (LLMs) have driven rapid progress in autonomous agents, yet standard evaluations remain confined to static task solving. An emerging frontier is harness evolution---the agent's capacity to autonomously optimize its own operating harness. However, systematica…
Large Language Models (LLMs) have driven rapid progress in autonomous agents, yet standard evaluations remain confined to static task solving. An emerging frontier is harness evolution---the agent's capacity to autonomously optimize its own operating harness. However, systematica…
Agentic systems can achieve open-ended improvement through multi-component co-evolution that progressively removes fixed human constraints across agents, environments, and evolution mechanisms.
arXiv cs.AI
TIER_1English(EN)·Xi Wang, Kun Li, Xianyao Ling, Gang Yin, Liang Zhang, Jiang Wu, Wenbo Lei, Jun Xu, Annie Wang, Fu Zhang, Weizhe Wang·
arXiv:2608.05159v1 Announce Type: new Abstract: Enterprise operations extensively rely on multiple heterogeneous business systems and information applications, which also result in severe data silos and process fragmentation. Enterprises have invested considerable financial and m…
arXiv:2608.06196v1 Announce Type: new Abstract: Agents backed by large skill libraries must decide which skills to load and in what order. Loading the entire library into context is expensive and provides no structure for autonomous sequencing. We study two systems for this probl…
arXiv:2608.06144v1 Announce Type: new Abstract: Most agent benchmarks evaluate tasks independently and cannot measure whether experience from one task helps with later tasks. Existing self-evolution benchmarks do not jointly cover professional workflows, open-ended deliverables, …
arXiv:2608.05790v1 Announce Type: new Abstract: General-purpose large language model agents have achieved strong performance on tool-augmented tasks, yet they rely on assumptions break down in blockchain environments. On-chain execution is stateful, adversarial, and economically …
arXiv cs.AI
TIER_1English(EN)·Weihong Lin, Lin Sun, Xiangzheng Zhang·
arXiv:2608.05778v1 Announce Type: new Abstract: Prompt-side playbooks can improve tool-using language agents without retraining, but their portability beyond the source setting is unclear. We study frozen playbook transfer under a shared distill--validate--transfer protocol. On A…
arXiv:2608.05729v1 Announce Type: new Abstract: As capabilities rapidly increase, AI agents can move from running inside one app to acting across a user's devices over time. Yet existing agent systems still fall short in this scenario. This is because observations are scattered a…
arXiv:2608.05628v1 Announce Type: new Abstract: Although agent skills equip LLMs with reusable procedural knowledge, manual maintenance suffers from high costs, unscalability, and misalignment. Real-world deployments thus require autonomous, on-demand skill evolution at test time…
arXiv cs.AI
TIER_1English(EN)·Zhi Han, Chenxi Zeng, Liuhaichen Yang, Zihan Guo, Ming Zhou, Yang Li·
arXiv:2608.05573v1 Announce Type: new Abstract: LLM agents increasingly execute long-horizon tasks through tool use and environment interaction, shifting evaluation from final-response scoring to verification of complete executions. For skill-augmented agents, verification additi…
arXiv:2608.05263v1 Announce Type: new Abstract: Multi-agent orchestration frameworks are moving from demos to production, yet benchmarks typically report task accuracy without diagnosing why a pipeline failed, where a cascade began, or which routing decision caused the breakdown.…
arXiv:2608.05212v1 Announce Type: new Abstract: Deep search agents tackle challenging questions through long-horizon web interactions, a process that is both complex and fragile: small reasoning errors may propagate through long, noisy trajectories into fluent but incorrect answe…
arXiv:2604.09297v3 Announce Type: replace-cross Abstract: Agent skills are increasingly used to configure coding agents for software engineering (SE) tasks, yet current practice treats them as static, hand-crafted assets, or evolved on pass rate alone. This is insufficient: a ski…
arXiv cs.CL
TIER_1English(EN)·Jiaming Wei, Zekun Wu, Adriano Koshiyama, Maria Perez-Ortiz·
arXiv:2608.06171v1 Announce Type: new Abstract: Web agents observe a browser through text, pixels, or both, and the choice is usually fixed once for all tasks. We measure six observation modes across eight site-model combinations (cells) on VisualWebArena and WebArena and ask wha…
arXiv:2606.21140v2 Announce Type: replace-cross Abstract: Rapid advances in large language models have improved the task-solving capabilities of command-line-interface (CLI)-based agents, whose CLIs determine how models invoke tools, maintain interaction history, and recover from…
arXiv:2608.06362v1 Announce Type: cross Abstract: Deciding which of two agents is stronger means playing games until skill outweighs luck, and every game costs money, model inference, or expert time. Since the number of games needed is unknown, fixed-budget evaluations either kee…
arXiv:2608.05563v1 Announce Type: cross Abstract: Self-evolving skill (SES) systems distill agent trajectories into persistent skills, allowing untrusted experience to become trusted instruction. We introduce PoisonedEvolution, a trajectory-poisoning attack on this promotion proc…
arXiv cs.AI
TIER_1English(EN)·Vanessa Sochat, Daniel Milroy·
arXiv:2608.05332v1 Announce Type: cross Abstract: Agentic science is transforming the landscape of computational work, extending to scientific pipelines and workload managers. The workloads require specialized hardware within and across institutions. If assessing workload needs a…
arXiv:2608.06346v1 Announce Type: new Abstract: LLM-based agentic systems have shown remarkable capabilities in complex domains, while suffering from cascading errors and difficulty in debugging. Critical error detection aims to locate the earliest error step in a failed trajecto…
Recent systems for optimizing prompts, programs, and ML workflows typically rely on explicit outer-loop controllers such as evolutionary search, bandits, or textual-gradient methods. We ask a fundamentally different question: how much of this search policy can be internalized by …
With the rapid advancement of large language models (LLMs), harnesses have become essential infrastructure for deploying agents across a wide range of domains. The fast-evolving harness ecosystem has also made rigorous capability evaluation increasingly important. However, effici…
Recent systems for optimizing prompts, programs, and ML workflows typically rely on explicit outer-loop controllers such as evolutionary search, bandits, or textual-gradient methods. We ask a fundamentally different question: how much of this search policy can be internalized by …
Deciding which of two agents is stronger means playing games until skill outweighs luck, and every game costs money, model inference, or expert time. Since the number of games needed is unknown, fixed-budget evaluations either keep paying after the result is settled or stop befor…
AI-agent workflows often involve remote calls to models, memory stores, and tools distributed across a network. As execution progresses, these dependency calls collectively form an agentic service graph (ASG). Unlike traditional service requests, many dependency calls are reveale…
Orchestrated collectives of large language model (LLM) agents that debate and vote are an emerging form of computational intelligence: the intelligent behaviour resides in the \emph{interaction}, not in any single agent. They improve task accuracy, yet remain black boxes at the s…
General-purpose large language model agents have achieved strong performance on tool-augmented tasks, yet they rely on assumptions break down in blockchain environments. On-chain execution is stateful, adversarial, and economically irreversible, exposing three fundamental gaps: R…
Although agent skills equip LLMs with reusable procedural knowledge, manual maintenance suffers from high costs, unscalability, and misalignment. Real-world deployments thus require autonomous, on-demand skill evolution at test time, constrained by limited interaction budgets and…
arXiv:2608.04738v1 Announce Type: new Abstract: Autonomous research agents can generate hypotheses, execute experiments, and draft manuscripts, yet their outputs often contain unsupported claims and inconsistencies between research questions, experiments, results, and conclusions…
arXiv:2608.05144v1 Announce Type: new Abstract: Long-horizon reasoning requires an agentic runtime that can persist when evidence supports its current approach and pivot when measurements reveal failure, hidden constraints, or a misspecified objective. We present Argus, a persist…
arXiv:2608.04066v1 Announce Type: new Abstract: How do you verify a long-horizon agent when its own state and self-reports are exactly what you cannot trust? We present an agent instrument built so that verification is structural rather than post-hoc. A deterministic Executive ow…
arXiv cs.LG
TIER_1English(EN)·Arnab Phani, Elias Strauss, Sebastian Schelter·
arXiv:2603.03589v3 Announce Type: replace-cross Abstract: Recent advances in large language models (LLMs) transform how machine learning (ML) pipelines are developed and evaluated. LLMs enable a new type of workload, agentic pipeline search, in which autonomous or semi-autonomous…
arXiv:2608.05013v1 Announce Type: cross Abstract: LLM agents are increasingly applied to open-ended everyday requests that span work, study, and life. These tasks are long-horizon, cross-environment, and multimodal, forcing the agent to preserve goals and constraints across many …
arXiv cs.LG
TIER_1English(EN)·Jun Nie, Yonggang Zhang, Qianshu Cai, Yiu-ming Cheung, Xinmei Tian, Bo Han·
arXiv:2608.04968v1 Announce Type: new Abstract: The capabilities of an LLM agent depend not only on its model but on the harness: the executable program that constructs context, invokes tools, verifies results, and recovers from failure. Recent work shows that evolving the harnes…
LLM agents increasingly execute long-horizon tasks through tool use and environment interaction, shifting evaluation from final-response scoring to verification of complete executions. For skill-augmented agents, verification additionally requires the procedural knowledge encoded…
CLI-based software-engineering agents have matured rapidly, yet the open ecosystem has converged on a single training environment: trajectory datasets used to fine-tune open models are collected almost exclusively under OpenHands. Models fine-tuned on this data score well under O…
arXiv:2608.03764v1 Announce Type: new Abstract: Agent self-evolution updates an agent's persistent state from prior experience and reuses it to solve related tasks more effectively. Evaluating self-evolution is difficult: existing benchmarks provide limited coverage of economical…
arXiv cs.LG
TIER_1(AF)·Paimon Goulart, Liang Wu, Kelly Wan, Evangelos E. Papalexakis, Liangjie Hong·
arXiv:2608.02880v1 Announce Type: cross Abstract: As lifelong learning agents accumulate lifelong growing skill banks, retrieving the correct skill becomes an increasingly important bottleneck. Most current skill retrieval methods treat each skill as one flat document by concaten…
arXiv cs.LG
TIER_1Deutsch(DE)·Dong Yan, Jian Liang, Dapeng Hu, Ran He, Nicholas Jing Yuan, Qi Zhang, Tieniu Tan·
arXiv:2608.00155v1 Announce Type: cross Abstract: Large language model (LLM) agents can self-evolve by continually improving from their own accumulated experience. However, existing studies predominantly adopt independent evaluation. Consequently, the behavior of self-evolving ag…
arXiv:2608.02636v1 Announce Type: cross Abstract: Self-evolving skill systems promise to improve agents by turning execution feedback into persistent skill updates without changing the underlying model. Yet it remains unclear when further evolution helps, how successful and faile…
arXiv cs.AI
TIER_1English(EN)·Sebasti\'an Andr\'es Cajas Ord\'o\~nez, Agastya Munnangi, Aldo Marzullo, Felipe Ocampo Osorio, Quang Bui, Mohammad Shahin, Armaan Grewal, Emmanuel Paul Kwesiga, Anqi Peter Li, Josephine Nanyonjo, Aaditya Panchal, Arshnoor Bhutani, Nikhil Jaiswal, Milit S…·
arXiv:2608.03744v1 Announce Type: new Abstract: Clinical decision support is moving toward committees of language-model agents deliberating on a shared workspace. We ask whether such committees can be gamed by shortcuts, cues a benchmark rewards but a clinician would ignore. Acro…
arXiv:2608.03499v1 Announce Type: new Abstract: Recent advances in persistent personal-agent frameworks are making human-centered agent networks realistic deployment targets: each user can be served by an AI agent that acts on the user's behalf, maintains state, and communicates …
arXiv:2608.03403v1 Announce Type: new Abstract: The performance bottleneck of agents is increasingly shifting from model capability to the robustness of their execution processes. Tools play a central role as the primary interface through which agents interact with external envir…
arXiv:2608.03222v1 Announce Type: cross Abstract: Software engineering (SWE) agents resolve repository-level issues through long trajectories that grow increasingly expensive as context accumulates. Failed runs tend to be longer and exhibit redundant exploration or looping, sugge…
arXiv cs.AI
TIER_1English(EN)·Ankur Sharma, Deep Shah·
arXiv:2608.03214v1 Announce Type: new Abstract: Large language models have transformed artificial intelligence from isolated prediction services into components of long-running, distributed systems that reason, invoke tools, retrieve external state, delegate tasks, and act on beh…
arXiv:2608.02878v1 Announce Type: new Abstract: Large language models have shown promise for automated Verilog RTL generation, yet state-of-the-art multi-agent systems plateau at ~95% accuracy on standard benchmarks. We trace this ceiling to an incomplete debugging action space: …
arXiv cs.AI
TIER_1English(EN)·Qi Liu, Ruochen Hao, Can Li, Wanjing Ma·
arXiv:2602.13769v3 Announce Type: replace Abstract: Automating heuristic design in complex, experiment-driven domains requires more than iterative mutation of solution algorithms. Current LLM-based evolutionary methods often rely on stochastic mutation loops that lack long-term s…
arXiv cs.AI
TIER_1English(EN)·Salma El Yadouni (EPFL), Guanyi Li (Binome Technologies)·
LLM agents are increasingly applied to open-ended everyday requests that span work, study, and life. These tasks are long-horizon, cross-environment, and multimodal, forcing the agent to preserve goals and constraints across many steps while navigating heterogeneous tools and att…
Recent advances in persistent personal-agent frameworks are making human-centered agent networks realistic deployment targets: each user can be served by an AI agent that acts on the user's behalf, maintains state, and communicates with other agents through social and task relati…
The performance bottleneck of agents is increasingly shifting from model capability to the robustness of their execution processes. Tools play a central role as the primary interface through which agents interact with external environments, yet existing methods rarely focus on en…
Software engineering (SWE) agents resolve repository-level issues through long trajectories that grow increasingly expensive as context accumulates. Failed runs tend to be longer and exhibit redundant exploration or looping, suggesting that some failures may be detectable before …
arXiv:2608.00106v1 Announce Type: new Abstract: Agentic systems must decide not only what answer to produce, but which reasoning and execution operations should precede it. A controller may answer directly, decompose a request, retrieve evidence, execute code, delegate to a speci…
arXiv:2608.00355v1 Announce Type: new Abstract: Progress in large language models is often summarized using a single scalar measure, such as a time horizon, a latent ability estimate, or an aggregate benchmark score. These summaries capture the overall performance, but they do no…
arXiv:2603.17445v5 Announce Type: replace-cross Abstract: When a multi-agent system produces an incorrect or harmful answer, who is accountable if execution logs and agent identifiers are unavailable? In practice, generated content is often detached from its execution environment…
arXiv cs.CL
TIER_1English(EN)·Yunhao Chen, Xin Wang, Yixu Wang, Yi Liu, Jie Li, Yan Teng, Xingjun Ma, Xia Hu, Yu-Gang Jiang·
arXiv:2608.00677v1 Announce Type: new Abstract: AI agents operate in persistent environments where early state changes can influence decisions far into the future. Unlike conventional language-model interactions, agent behavior is mediated through a shared state that is repeatedl…
arXiv cs.CL
TIER_1English(EN)·Qiyong Zhong, Mao Zheng, Mingyang Song, Xin Lin, Jie Sun, Houcheng Jiang, Xiang Wang, Junfeng Fang·
arXiv:2605.07725v2 Announce Type: replace Abstract: Tool-integrated reasoning (TIR) is difficult to scale to small language models due to instability in long-horizon tool interactions and limited model capacity. While reinforcement learning methods like group relative policy opti…
arXiv:2608.00107v1 Announce Type: new Abstract: Agentic systems must repeatedly decide whether to answer directly, decompose a task, invoke a tool, execute code, delegate to a specialist, verify an intermediate result, or recover from failure. These meta-decisions affect not only…
arXiv:2608.01913v1 Announce Type: cross Abstract: Deep search agents answer difficult information-seeking questions by iteratively issuing search queries to gather supporting evidence, but it remains unclear whether and how greater search effort leads to better answers. We study …
arXiv:2608.01918v1 Announce Type: cross Abstract: Harness design plays a critical role in agent performance by shaping how large language models (LLMs) perceive, reason over, and act within executable environments. Recent work has proposed automatic harness evolution, which itera…
arXiv:2608.01056v1 Announce Type: cross Abstract: Tool-using language-model agents are governed not only by task prompts but also by persistent system-side instructions that specify tools, arguments, policies, execution protocols, and recovery. Compressing these agent control con…
arXiv:2608.01042v1 Announce Type: cross Abstract: Enterprise AI agents act across many apps whose data changes continuously, so an answer is correct only relative to what data existed and who could see it at the moment it was asked. Offline evaluation today grades against a singl…
arXiv:2608.01428v1 Announce Type: cross Abstract: Embodied agents replan frequently to recover from execution drift, partial observability, and coordination hazards, but each LLM-based replanning call can consume an accumulated textual context that grows over time and across agen…
arXiv:2608.02302v1 Announce Type: cross Abstract: Long-horizon coding-agent trajectories are poorly matched to the credit units available to train on: a single action has no stable value, an episode label merges productive exploration with abandoned directions, and a fixed window…
arXiv:2608.02358v1 Announce Type: new Abstract: To operate robustly in open-world environments, autonomous agents should be able to infer the behavior of unfamiliar systems through interaction alone, even in the absence of documentation. However, existing tool-use benchmarks expo…
arXiv:2606.21337v2 Announce Type: replace Abstract: Raw multimodal streams are abundant but noisy, redundant, and unaligned with any particular training objective. Turning them into supervision today means either brittle heuristics or repeatedly querying a proprietary vision-lang…
arXiv cs.CL
TIER_1English(EN)·Donghyeok Koh, Gyuwan Kim, Jinyeong Bak, Seung-Hoon Na, Tao Yang, Haneol Jang, Cheoneum Park·
arXiv:2608.02353v1 Announce Type: new Abstract: Recent advances in agentic workflow optimization automate workflow design through task-specific workflow search or input-conditioned architecture selection. However, they determine the workflow before execution and cannot adapt fail…
arXiv cs.LG
TIER_1English(EN)·Hao Mark Chen, Jinnan Guo, Wayne Luk, Hongxiang Fan·
arXiv:2608.00881v1 Announce Type: new Abstract: Large language model agents increasingly act through stateful tools, yet model generation and environment execution remain serialized at every step. As decoding accelerates, tool execution becomes a growing bottleneck. Existing acti…
arXiv:2608.01269v1 Announce Type: new Abstract: Hierarchical Graph Retrieval-Augmented Generation (GraphRAG) organizes corpus knowledge at multiple levels of granularity, yet fixed context construction may fail to translate these multi-resolution representations into a context su…
Agent self-evolution updates an agent's persistent state from prior experience and reuses it to solve related tasks more effectively. Evaluating self-evolution is difficult: existing benchmarks provide limited coverage of economically valuable task domains, do not always design t…
WeClawArena is an auditable benchmark and sandbox for evaluating multi-party agent collaboration across personal workspaces, measuring both task utility and security attack success.
Self-evolving agents increasingly convert interaction histories into reusable skills that persist beyond individual tasks. While prior work studies memory and retrieval poisoning, such attacks only affect agents when poisoned records are retrieved as context. We uncover a new and…
LLM agents are increasingly applied to open-ended everyday requests that span work, study, and life. These tasks are long-horizon, cross-environment, and multimodal, forcing the agent to preserve goals and constraints across many steps while navigating heterogeneous tools and att…
Automated agent design improves agent harnesses through iterative revision, evaluation, and feedback summarization. Existing methods are largely candidate-centric: cross-round experience is organized around candidate agents, which leaves the repair progress implicit. This causes …
As lifelong learning agents accumulate lifelong growing skill banks, retrieving the correct skill becomes an increasingly important bottleneck. Most current skill retrieval methods treat each skill as one flat document by concatenating fields such as the name, description, and bo…
As lifelong learning agents accumulate lifelong growing skill banks, retrieving the correct skill becomes an increasingly important bottleneck. Most current skill retrieval methods treat each skill as one flat document by concatenating fields such as the name, description, and bo…
Deep search agents answer difficult information-seeking questions by iteratively issuing search queries to gather supporting evidence, but it remains unclear whether and how greater search effort leads to better answers. We study these questions through a trajectory-level diagnos…
arXiv cs.AI
TIER_1English(EN)·Blaise Delattre, Cong Wang, Yang Cao·
arXiv:2607.29190v1 Announce Type: new Abstract: Tool-using LLM agents act on typed tool returns, records pairing provenance and categorical fields with numerical values. Runtime permission gates generally authorize the observed return and action, leaving the decision unprotected …
arXiv:2607.28990v1 Announce Type: new Abstract: Large language model agents have shown promising capabilities in data-driven scientific discovery tasks, where an agent interacts with an execution environment and produces a statistical claim. Long-horizon scientific analysis remai…
arXiv cs.AI
TIER_1English(EN)·Roy Zhao (Paul G. Allen School of Computer Science & Engineering, University of Washington), Zhenyu Zhao (Independent Researcher)·
arXiv:2607.28691v1 Announce Type: cross Abstract: Personalized AI agents are often configurable without giving users control over the artifacts that determine their future behavior. We present OurArk, an architecture for persistent personal agents centered on an agent-owned softw…
arXiv:2607.28685v1 Announce Type: new Abstract: Agent-safety benchmarks measure different behaviors, and their scores get quoted interchangeably as an agent's safety. We treat four of them (R-Judge, InjecAgent, AgentHarm, AgentDojo) as measurements to be validated, running each u…
arXiv:2603.20075v2 Announce Type: replace-cross Abstract: Compilers are critical to modern computing, yet fixing compiler bugs is difficult. While recent large language model (LLM) advancements enable automated bug repair, compiler bugs pose unique challenges due to their complex…
arXiv:2607.29422v1 Announce Type: cross Abstract: Automated vulnerability repair aims to reduce the time and effort required to patch security flaws from a vulnerability triage report. Recent agentic AI approaches have shown promising results in automated program repair. However,…
arXiv:2607.28802v1 Announce Type: new Abstract: Existing evaluations often reduce agent failures to system-level outcomes, obscuring where the fault originated and which intervention would improve the agent system. This creates a repair-assignment problem: the same visible failur…
To operate robustly in open-world environments, autonomous agents should be able to infer the behavior of unfamiliar systems through interaction alone, even in the absence of documentation. However, existing tool-use benchmarks expose semantic tool schemas in static environments,…
Large language model (LLM) agents increasingly undertake long-horizon tasks that require sustained reasoning, tool use, and revision across many interdependent steps. However, existing agent harnesses maintain task execution, task state, and completion assessment within a growing…
Tool-using language-model agents are governed not only by task prompts but also by persistent system-side instructions that specify tools, arguments, policies, execution protocols, and recovery. Compressing these agent control contexts (ACCs) can reduce input cost and context use…
arXiv cs.AI
TIER_1English(EN)·Lang Cao, Yuhao Shen, Tianyang Luo, Simo Du, Hao Peng, Yue Guo·
arXiv:2607.26160v1 Announce Type: new Abstract: Clinical practice guidelines (CPGs) encode diagnostic criteria, but LLM systems typically retrieve guideline text or absorb it through training rather than execute its rules. We introduce GuideSkill, an external reasoning layer that…
arXiv:2607.26661v1 Announce Type: new Abstract: Ascend C operator optimization is critical for NPU (Neural Processing Unit) inference performance but requires deep hardware expertise.While large language models (LLMs) have shown promise in automated CUDA kernel generation, the fu…
arXiv:2607.28074v1 Announce Type: cross Abstract: Computer-use agents learn from what their actions change, so training one needs applications it can act on, break and reset. The applications that matter most are login-gated and stateful, so synthetic environments stand in for th…
arXiv:2607.28399v1 Announce Type: new Abstract: Computer-use agents often fail on transient GUI events because they produce the correct action only after the relevant window has already closed. We identify the main cause as expensive autoregressive decoding on the decision-time c…
arXiv:2607.28037v1 Announce Type: new Abstract: As LLM-based agents are deployed in complex, multi-step workflows, a critical evaluation gap has emerged: most existing benchmarks judge only final outcomes, unable to distinguish reliable reasoning from lucky success or attribute f…
arXiv:2510.25333v2 Announce Type: replace Abstract: Recent years have witnessed the rapid development of LLM-based agents, which shed light on using language agents to solve complex real-world problems. A prominent application lies in business agents, which interact with database…
Large language model (LLM) agents can self-evolve by continually improving from their own accumulated experience. However, existing studies predominantly adopt independent evaluation. Consequently, the behavior of self-evolving agents in realistic streaming settings, where agents…
Computer-use agents often fail on transient GUI events because they produce the correct action only after the relevant window has already closed. We identify the main cause as expensive autoregressive decoding on the decision-time critical path. We propose Adaptive Anticipatory P…
Computer-use agents learn from what their actions change, so training one needs applications it can act on, break and reset. The applications that matter most are login-gated and stateful, so synthetic environments stand in for them. Recent pipelines generate such environments in…
Agent-based models (ABMs) are difficult to reproduce: their behavior is spread across prose narratives, platform-specific code, and implicit assumptions, so that two readers routinely reconstruct different models from the same documentation. We present VISA, a structured, symbol-…
Vision-Language-Action (VLA) models have attracted growing interest as a scalable approach to robotic manipulation. While these models are effective action predictors, deploying them as robotic agents exposes critical gaps: no mechanism for failure recovery, inconsistent executio…
arXiv cs.CL
TIER_1English(EN)·Yihao Chen, Shi Chang, Feng Lin, Khaled Chawa, Boyuan Chen, Shaowei Wang, Ahmed E. Hassan·
arXiv:2607.27167v1 Announce Type: cross Abstract: LLM-based agents excel at software engineering tasks where an existing codebase provides context, but constructing a program from scratch remains fundamentally harder. Recent benchmarks such as ProgramBench quantify this gap: give…
arXiv cs.CL
TIER_1English(EN)·Xuan Zhao, Jiwoong Sohn, Qinyue Zheng, Michael Moor·
arXiv:2607.26300v1 Announce Type: new Abstract: AI agents are increasingly adept at tackling complex, long-running tasks. With the rapid surge of autonomous capabilities, human oversight is systematically lagging behind due to limited human-centered interfacing. Aiming to address…
arXiv:2607.26998v1 Announce Type: cross Abstract: Large language model (LLM) agents automate penetration testing through an observation-action loop, selecting actions based on observations returned by tools. This dependence allows defenders to inject deceptive observations that c…
arXiv cs.CL
TIER_1English(EN)·Lingyang Zeng, Guangze Chen, Kaichen Yu, Zhicheng Pan, Siyang Weng, Zirui Hu, Xiangyun Du, Hailin He, Rong Zhang, Chengcheng Yang, Kai Huang, Xuan Zhou·
arXiv:2607.27056v1 Announce Type: cross Abstract: Personalized agents are increasingly applied to assist users across a wide range of tasks. Effective personalized assistance requires not only retrieving explicit facts from past interactions stored in agent memory, but also infer…
arXiv:2607.27155v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly expected to assist users in completing tasks. However, existing benchmarks provide limited support for evaluating whether agents can carry out office-suite workflows at a reasonab…
arXiv cs.LG
TIER_1English(EN)·Jingbo Cui, Jitao Zhao, Di Jin, Dongxiao He·
arXiv:2607.26533v1 Announce Type: new Abstract: Graph Foundation Models (GFMs) aim to learn transferable knowledge from multi-domain graphs and adapt to unseen scenarios. As a fundamental source of relational semantics in graphs, the transferability of topological patterns has lo…
Agent-safety benchmarks measure different behaviors, and their scores get quoted interchangeably as an agent's safety. We treat four of them (R-Judge, InjecAgent, AgentHarm, AgentDojo) as measurements to be validated, running each under its official implementation and author-prov…
Large language model agents increasingly plan, execute, and interpret biological analyses, yet fluent responses, successful tool calls, and benchmark performance alone do not establish scientific credibility. Existing reviews primarily organize biological agents by application, a…
Existing evaluations often reduce agent failures to system-level outcomes, obscuring where the fault originated and which intervention would improve the agent system. This creates a repair-assignment problem: the same visible failure may call for model post-training, harness engi…
GUI agents have the potential to become a general purpose executor over existing digital devices. To advance them toward real-world use, we envision agents that operate reliably on real devices, execute workflows across platforms, combine GUI interaction with CLI execution, compl…
Computer-use agents learn from what their actions change, so training one needs applications it can act on, break and reset. The applications that matter most are login-gated and stateful, so synthetic environments stand in for them. Recent pipelines generate such environments in…
Reusable skills are becoming a standard interface for extending language agents with task procedures. Yet evaluators usually infer skill use from visible reasoning or the agent's own attribution. These signals show what the agent appears to use, not whether the skill changed its …
LLM-based agents excel at software engineering tasks where an existing codebase provides context, but constructing a program from scratch remains fundamentally harder. Recent benchmarks such as ProgramBench quantify this gap: given only natural-language documentation and an execu…
Large language model (LLM) agents are increasingly expected to assist users in completing tasks. However, existing benchmarks provide limited support for evaluating whether agents can carry out office-suite workflows at a reasonable cost. We introduce OmegaUse-OfficeVal, a benchm…
arXiv:2607.25283v1 Announce Type: new Abstract: This paper presents ContractHIL-HLS, a contract-aligned multi-agent workflow for practical high-level synthesis (HLS) engineering. The workflow makes three contributions. First, it introduces a structured contract as the semantic-al…
arXiv cs.AI
TIER_1English(EN)·Azizul Zahid, Subrata Biswas, Bashima Islam, Sai Swaminathan·
arXiv:2607.24770v1 Announce Type: new Abstract: Procedural tasks such as furniture assembly and home repair impose substantial cognitive demands because users must interpret instructions, track task progress, reason about spatial state, and recover from errors while performing ph…
arXiv:2607.25398v1 Announce Type: new Abstract: Language-model agents are increasingly deployed under standing instructions: a system prompt, a policy file, or a skills document is placed in context, and the agent is trusted to let it govern every action that follows. Existing be…
arXiv cs.AI
TIER_1English(EN)·Jincheng Wang, Min Zheng, Tao Wei·
arXiv:2607.25400v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly entrusted with natural-language workflow instructions (e.g., retail-payment policies) that specify not only what outcome to achieve, but also which steps, branches, and tool interac…
arXiv:2607.25853v1 Announce Type: new Abstract: Skills have become an important abstraction for enabling large language model (LLM) agents to reuse past experience in long-horizon interactive tasks. However, existing trajectory-to-skill methods often produce flat collections of h…
arXiv cs.AI
TIER_1English(EN)·Hyundoo Park, Byungho Choi·
arXiv:2607.25152v1 Announce Type: new Abstract: Long-running autonomous agents plan, act, and judge their own completion without human intervention. When an agent grades its own work, self-evaluation bias takes hold: plausible changes are accepted as progress while real-world out…
arXiv:2607.25446v1 Announce Type: new Abstract: Multi-agent frameworks built on large language models (LLMs) routinely entangle three logically distinct concerns: who is on the team (organization), how members align (coordination), and which algorithm fuses their work (collaborat…
arXiv:2607.25076v1 Announce Type: new Abstract: Every major wave of platform software follows the same arc: an initial period of experimentation with competing frameworks and ad-hoc implementations, followed by the articulation of a small set of stable abstractions with well-defi…
arXiv cs.AI
TIER_1Norsk(NO)·Alireza Saleh Abadi, Leen-Kiat Soh, Daniel Alan Redder, Adam Eck, Prashant Doshi·
arXiv:2607.25082v1 Announce Type: new Abstract: Open agent systems (OASYS) are increasingly prevalent in real-world domains where the sets of agents and tasks change unpredictably over time. Such openness, including agent openness (AO) and task openness (TO), poses a fundamental …
arXiv:2607.25090v1 Announce Type: new Abstract: Machine learning engineering (MLE) tasks require long-horizon decision making over iterative solution debugging and refinement, under expensive and feedback-driven environment interactions. Developing and training a monolithic agent…
arXiv:2607.25656v1 Announce Type: new Abstract: Complex tasks often decompose into parallelizable yet interdependent subtasks, making orchestration critical to the performance of multi-agent systems (MAS). Existing evaluations typically rely on end-to-end execution, which conflat…
arXiv cs.AI
TIER_1English(EN)·Jiabao Ji, Yujian Liu, Li An, Rohit Jain, Gungor Polatkan, Siyu Zhu, Shiyu Chang·
arXiv:2607.25816v1 Announce Type: new Abstract: Large language model agents often spend substantial wall-clock time waiting for tool call results. Tool-call speculation can hide this latency by predicting and pre-executing an agent's next tool call if the prediction matches the a…
arXiv:2607.25914v1 Announce Type: new Abstract: Autonomous Network Levels 4-5 require AI agents to invoke tools across vendor boundaries without human oversight, yet existing management standards lack a standardized mechanism for cross-vendor trust visibility. When a tool from Ve…
arXiv:2607.25765v1 Announce Type: new Abstract: Enterprise agents often need to integrate heterogeneous knowledge sources: documents for narrative facts, tables for computation, and dependency graphs for file relationships. Existing benchmarks typically evaluate retrieval or tool…
arXiv:2607.25308v1 Announce Type: cross Abstract: Training large language models (LLMs) to act in long-horizon games is a promising step toward generalist decision-making, yet reinforcement learning with verifiable rewards (RLVR) relies on sparse final rewards that reveal little …
arXiv:2607.25032v1 Announce Type: cross Abstract: Agent Skills are an emerging way to extend large language model agents with reusable procedural knowledge that the agent loads on demand. Anthropic introduced Agent Skills and published the format as an open specification supporte…
arXiv cs.AI
TIER_1English(EN)·Mishca de Costa, Muhammad Saleh Anwar, Dave Mercier, Issam Hammad·
arXiv:2607.24791v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) is the dominant paradigm for applying large language models (LLMs) to enterprise document corpora, yet naive implementations encounter hard limits as corpus scale and query complexity grow. Thi…
arXiv cs.AI
TIER_1English(EN)·Stefan Krsteski, Charlotte Meyer, Guillaume Allegre, Tony O'Halloran, Alexandre Sallinen·
arXiv:2607.25891v1 Announce Type: new Abstract: Evaluating AI agents in interactive environments is hindered by fragmented tasks, scaffolds, verifiers, and scoring rules. Existing efforts focus on narrow settings, remain limited in scale, or require costly reruns, leaving much of…
LLM-based agents excel at software engineering tasks where an existing codebase provides context, but constructing a program from scratch remains fundamentally harder. Recent benchmarks such as ProgramBench quantify this gap: given only natural-language documentation and an execu…
Large language model (LLM) agents are increasingly expected to assist users in completing tasks. However, existing benchmarks provide limited support for evaluating whether agents can carry out office-suite workflows at a reasonable cost. We introduce OmegaUse-OfficeVal, a benchm…
Agentic cloud management is emerging as a practice to automate laborious operations, minimize toil, and improve responsiveness. Despite the rapid development of autonomous management agents, we argue that the fundamental missing piece is a systems foundation to enable safe, effec…
Verifying building compliance requires validating thousands of rules against large Building Information Modeling (BIM) designs, which is laborious, capital-intensive, and unscalable. Existing Automated Compliance Checkers (ACCs) are often difficult to generalize across different …
arXiv cs.AI
TIER_1English(EN)·Erik Imgrund, Anna Wimbauer, Klim Kireev, Konrad Rieck·
arXiv:2607.23147v1 Announce Type: cross Abstract: Large language models now power autonomous agents capable of complex, multi-step tasks in different environments. Accurate and reliable execution of these tasks requires the agent to predict the results of its actions. Recent rese…
arXiv cs.LG
TIER_1English(EN)·Daniel Wang, Andrew Xu·
arXiv:2607.23332v1 Announce Type: new Abstract: Creating a reusable tool is an investment: an agent pays a fixed cost now in exchange for the potential of future reuse. Therefore, a user should prefer an agent that creates a small number of highly reusable tools, rather than many…
arXiv:2607.23670v1 Announce Type: cross Abstract: Plan Modes have become standard features in agentic programming tools, allowing users to gain transparency and control by working with the agent to develop a plan before task execution. However, it remains unclear whether the bene…
arXiv:2607.24162v1 Announce Type: new Abstract: Optimizing agentic workflows, such as retrieval-augmented generation (RAG) pipelines, requires navigating a combinatorial space of discrete component choices under tight evaluation budgets. Existing approaches - heuristic search, bl…
arXiv:2607.22643v1 Announce Type: new Abstract: Multimodal retrieval-augmented generation (mRAG) aims to answer image-text queries with external knowledge, but most existing systems still retrieve directly from raw multimodal input over a flat evidence space. This design often st…
arXiv:2607.22688v1 Announce Type: new Abstract: Post-training agents for automated AI research requires optimizing not only model parameters, but also the runtime harness that shapes how research trajectories are generated, evaluated, and learned from. Existing pipelines typicall…
arXiv:2607.22689v1 Announce Type: new Abstract: Graphical user interface (GUI) agents are systems powered by large multimodal models (LMMs). They perceive screen state and execute user instructions through GUI actions such as clicking, typing, and scrolling on desktops and mobile…
arXiv:2607.22868v1 Announce Type: new Abstract: Runtime guardrails act before irreversible tool calls, but their guarantees depend on what policy state is representable, what a judge observes, and whether intervention changes future behavior. We separate three questions. First, r…
arXiv cs.AI
TIER_1English(EN)·Summer Sun (Shaqiu Community)·
arXiv:2607.23123v1 Announce Type: new Abstract: Existing evaluations of large language models cover knowledge, reasoning, coding, and tool use, but they rarely treat a verifiable deliverable produced within a constrained workflow as the unit of evaluation. We introduce SQBench, a…
arXiv:2607.23124v1 Announce Type: new Abstract: Large language model agents have advanced rapidly, yet progress remains fragmented across domains, capabilities, task difficulty, and interaction settings. We frame this as full-scenario agentic scaling and present AgentOmnia, a fra…
arXiv:2607.23678v1 Announce Type: new Abstract: Large language models (LLMs) enable autonomous agents for reasoning, planning, and tool use. Recent systems increasingly organize these agents as graphs of specialized, interconnected nodes. Although graph-based orchestration suppor…
arXiv cs.AI
TIER_1English(EN)·Weihuang Zheng, Tianyuan Zou, Eileen Ye, Alphet Liu, Youyong Kong, Ya-Qin Zhang, Duran Zheng, Maxm Pan·
arXiv:2607.23722v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed as agents that interact with stateful environments over multiple steps: gathering hidden information, composing tool calls, and committing state changes. We refer to this capabi…
arXiv cs.AI
TIER_1English(EN)·Xiaochuan Li, Ryan Ming, Meng Chu, Shuai Shao, Rong Jin, Chenyan Xiong·
arXiv:2607.23809v1 Announce Type: new Abstract: Agentic tasks are inherently long-horizon and multi-turn, constantly accumulating context through interactions with the environment. Existing context compression methods inevitably incur information loss and are triggered by rigid h…
arXiv:2607.24112v1 Announce Type: new Abstract: We introduce State Transition Pretraining (STP) as a new scaling axis for GUI agents. During the STP stage, we continually pretrain a unified multimodal model on visual state transitions by jointly optimizing inverse dynamics (predi…
arXiv:2607.24167v1 Announce Type: new Abstract: Long-horizon web agents often go off track before final failure: a trajectory can remain locally plausible even after the current state, reused skill, or plan assumption no longer supports the user instruction. Existing agents can p…
arXiv:2607.24280v1 Announce Type: new Abstract: Agentic search enables large language models to solve knowledge-intensive tasks by interleaving multi-step reasoning with retrieval, yet optimizing this with outcome-based reinforcement learning (RL) provides only sparse supervision…
Training large language models (LLMs) to act in long-horizon games is a promising step toward generalist decision-making, yet reinforcement learning with verifiable rewards (RLVR) relies on sparse final rewards that reveal little about which decisions determine success. Denser pr…
Open agent systems (OASYS) are increasingly prevalent in real-world domains where the sets of agents and tasks change unpredictably over time. Such openness, including agent openness (AO) and task openness (TO), poses a fundamental challenge to multi-agent reinforcement learning …
Open agent systems (OASYS) are increasingly prevalent in real-world domains where the sets of agents and tasks change unpredictably over time. Such openness, including agent openness (AO) and task openness (TO), poses a fundamental challenge to multi-agent reinforcement learning …
Optimizing agentic workflows, such as retrieval-augmented generation (RAG) pipelines, requires navigating a combinatorial space of discrete component choices under tight evaluation budgets. Existing approaches - heuristic search, black-box optimization, and standard tree search m…
We introduce State Transition Pretraining (STP) as a new scaling axis for GUI agents. During the STP stage, we continually pretrain a unified multimodal model on visual state transitions by jointly optimizing inverse dynamics (predicting actions from state changes) and forward dy…
arXiv cs.CL
TIER_1English(EN)·Nanbeige Lab, :, Chen Yang, Chengrui Huang, Fufeng Lan, Hanhui Chen, Hao Zhou, Huatong Song, Jiaqi Cao, Jiaying Zhu, Jinlin Niu, Kai Wang, Lisheng Huang, Qiliang Liang, Ran Le, Ruixiang Feng, Shuang Sun, Tao Gu, Tao Zhang, Tianyu Luo, Yang Song, Yun Xin…·
arXiv:2607.22083v1 Announce Type: cross Abstract: We present Nanbeige4.2-3B, a compact general agentic model with 3B non-embedding parameters. It delivers strong performance across code-agent, office-agent, and complex tool-use tasks while maintaining highly competitive reasoning…
arXiv cs.LG
TIER_1English(EN)·Jiseok Kwak, Suhyeon Jo, Taewoo Kim, Yeongmin Kim, Byeonghu Na, Il-chul Moon·
arXiv:2607.21946v1 Announce Type: new Abstract: This technical report presents our approach to Challenge Track~3: SeePhys Pro at the 3rd AI for Math Workshop, where the task is to answer college-level physics questions whose statement and figure may be given partly or entirely as…
arXiv:2607.22465v1 Announce Type: cross Abstract: Routing to select large language models (LLMs) with different cost-quality trade-offs has become a fundamental deployment feature of enterprise AI. Existing routers, primarily make independent routing decisions for each LLM call. …
Agentic search enables large language models to solve knowledge-intensive tasks by interleaving multi-step reasoning with retrieval, yet optimizing this with outcome-based reinforcement learning (RL) provides only sparse supervision. Knowledge distillation can supply denser guida…
Recent advances in large language models (LLMs) have enabled search agents to autonomously tackle complex tasks across extended search and reasoning horizons. However, training effective search agents remains challenging due to the lack of scalable and long-horizon tasks, and the…
Routing to select large language models (LLMs) with different cost-quality trade-offs has become a fundamental deployment feature of enterprise AI. Existing routers, primarily make independent routing decisions for each LLM call. However, agentic applications execute as long-hori…
Routing to select large language models (LLMs) with different cost-quality trade-offs has become a fundamental deployment feature of enterprise AI. Existing routers, primarily make independent routing decisions for each LLM call. However, agentic applications execute as long-hori…
METR (Model Evaluation & Threat Research)
TIER_1English(EN)·
<!-- Figure sources: scripts/tikz/2026-07-24-metrics-of-model-ability-*.tex Build with scripts/tikz/build-metrics-of-model-ability.sh. --> <div class="metrics-agent-note"> <!-- > **Goals of this post** > > **Goal:** a simple way to compare a variety of capability metrics in an id…
arXiv:2606.21262v2 Announce Type: replace Abstract: Reinforcement learning for multi-step LLM agents often relies on scalar rewards that indicate success but cannot explain why a trajectory is good or bad. Rubric-based rewards improve interpretability through natural-language cri…
arXiv:2607.20488v1 Announce Type: new Abstract: Multi-agent LLM frameworks typically fix their team topology at boot time. When an individual agent becomes overloaded at runtime, for example by mixing too many action categories, accumulating tool errors, or queueing behind too ma…
arXiv:2607.20668v1 Announce Type: cross Abstract: TextGrad improves language-model systems by revising text from feedback. Its core thesis is that natural-language feedback can act as a gradient for optimizing text components without changing model weights. Applying it to agents …
arXiv:2607.21051v1 Announce Type: new Abstract: Real-world agent learning is often constrained by costly environment interactions, such as running time-consuming experiments or obtaining human feedback. In-context learning offers a highly sample-efficient way for agents to learn …
arXiv:2607.21209v1 Announce Type: cross Abstract: In the field of Artificial Intelligence, an agent is a system which is able to autonomously make decisions in order to reach a desired goal. As these systems grow more prevalent in our day-to-day lives, there has been an increased…
arXiv cs.AI
TIER_1English(EN)·Xue-Jian Gao, Deng Pan, Yueming Su, Jiasheng Li, Bin Du, Fengming Zhu, Chengdi Ma, Junyi Fan, Qichen Liao, Chengqiu Hu, Xinxian Chen, Lingchao Zheng, Jun Li, Jiwei Yang, Yuwei Fan·
arXiv:2607.20518v1 Announce Type: new Abstract: AI agents are now capable of writing, compiling, and iteratively optimizing low-level operator kernels on different hardware platforms. Existing benchmarks, however, focus almost exclusively on CUDA and Triton, leaving hardware ecos…
arXiv:2607.21557v1 Announce Type: new Abstract: Modern AI agents rely on elaborate inference harnesses such as Claude Code, Codex, and OpenClaw to drive multi-turn reasoning, tool use, and access to external systems. While powerful, these complex harnesses also make agents hard t…
arXiv cs.AI
TIER_1English(EN)·Junzhi Chen, Harsh Trivedi, Jane Pan, Michael JQ Zhang, Tejas Srinivasan, Niranjan Balasubramanian, Ashish Sabharwal·
arXiv:2607.20536v1 Announce Type: new Abstract: Tool-use agents that address day-to-day digital tasks such as ordering groceries must not only operate applications, but also interact with the user, e.g., to ask clarification questions, prompt for confirmation, and inform the user…
arXiv cs.AI
TIER_1English(EN)·Anas Mohamed, Kaizan Haque, Azal Ahmad Khan, Chetan Sharma, Shuwen Ge, Ali Anwar·
arXiv:2607.20495v1 Announce Type: new Abstract: Multi-agent systems decompose complex tasks into directed acyclic graphs (DAGs) of specialized agent executions, creating natural opportunities for caching intermediate results across queries. However, existing cache eviction polici…
arXiv cs.AI
TIER_1English(EN)·Yinhao Tang, Youqing Fang, Yanan Sun, Wenran Liu, Weiming Zhang, Bin Liu, Kuikun Liu, Wenwei Zhang, Kai Chen·
arXiv:2607.20926v1 Announce Type: new Abstract: Scientific research involves complex information-seeking and reasoning workflows across heterogeneous sources. However, existing benchmarks primarily emphasize general-domain retrieval or static scientific question answering, and th…
arXiv:2607.20982v1 Announce Type: new Abstract: As large language model agents increasingly operate autonomously with access to tools and external environments, ensuring their safe and reliable behavior becomes critical. We present GuardianAgentBench (GABench), a benchmark of 580…
arXiv cs.AI
TIER_1English(EN)·Paul Furgale, Severin Klingler, James Nolan, Matt Staats, Gaia Di Lorenzo, Elisa Martinez Abad, Christian Sch\"uller, Razvan Dinu, Alessio Devoto, Pascal Berard, Gal Kaplun, Elad Sarafian, Riccardo Roveri, Leon Derczynski, Ricardo Silveira Cabral·
arXiv:2607.20709v1 Announce Type: new Abstract: Traditional agent development is split across prompt templates, tool schemas, callback code, and workflow graphs. We present NVIDIA Object-Oriented Agents (NOOA), a model-agnostic Python framework for building reliable AI agents. NO…
arXiv:2607.20999v1 Announce Type: new Abstract: Agent Skills package reusable procedural knowledge as external artifacts for frozen language-model agents, yet existing optimizers do not jointly resolve where a failure occurs in a workflow, which mechanism caused it, and how relev…
Computer-use agents are usually improved by strengthening perception: better models for reading a screenshot and choosing where to click. Yet a screenshot is only a lossy rendering of the underlying program state, e.g., the files, application backends, and DOM that hold the task …
arXiv:2605.14879v2 Announce Type: replace-cross Abstract: Many intelligent computing and autonomous systems rely on multiple independent, often learning, agents repeatedly sharing a limited resource. Examples include autonomous robots accessing a shared workstation, wireless devi…
arXiv:2607.19985v1 Announce Type: new Abstract: Dynamic manufacturing environments require multi-agent systems to coordinate effectively under frequent operational disturbances such as machine failures, urgent job arrivals, and processing time variations. Existing multi-agent rei…
arXiv:2607.19913v1 Announce Type: new Abstract: Agent safety is moving from content moderation toward preventing operational failures before tool-using agents act. We propose Janus, a foresight-oriented framework for long-horizon agent safety that trains guards to anticipate dela…
arXiv:2607.19865v1 Announce Type: new Abstract: As autonomous agents rapidly evolve, their ability to reliably manipulate ubiquitous digital documents has become critical for enabling general-purpose AI assistants and automating complex workspace workflows. In this paper, we intr…
arXiv cs.CL
TIER_1English(EN)·Qiyuan Liu, Tingfeng Hui, Kun Zhan, Kaike Zhang, Ning Miao·
arXiv:2607.20121v1 Announce Type: new Abstract: LLM-based agents leverage third-party skills to extend their capabilities in open-world scenarios. However, third-party skills can introduce extra security vulnerabilities, as seemingly harmless skills can contain latent safety risk…
arXiv:2601.06487v3 Announce Type: replace-cross Abstract: Reinforcement learning has substantially improved the performance of LLM agents on tasks with verifiable outcomes, but it still struggles on open-ended agent tasks with vast solution spaces (e.g., complex travel planning).…
arXiv:2606.20785v2 Announce Type: replace Abstract: Collecting computer use data from human demonstrations is expensive and slow, motivating the need for scalable generation strategies. This requires two key ingredients: environments in which agents can act and verifiers that can…
arXiv cs.AI
TIER_1English(EN)·Zhuofeng Li, Haoxiang Zhang, Seungju Han, Sheng Liu, Jianwen Xie, Yu Zhang, Yejin Choi, James Zou, Pan Lu·
arXiv:2510.05592v2 Announce Type: replace Abstract: Outcome-driven reinforcement learning has advanced reasoning in large language models (LLMs), but prevailing tool-augmented approaches train a single, monolithic policy that interleaves thoughts and tool calls under full context…
arXiv cs.AI
TIER_1English(EN)·Elias Hossain, Md Mehedi Hasan Nipu, Fatema Tuj Johora Faria, Tasfia Nuzhat Ornee, Maleeha Sheikh·
arXiv:2607.19430v1 Announce Type: cross Abstract: Multi-agent LLM applications chain a planner, worker agents, a verifier, and a synthesizer, and every hop between agents is an unmonitored channel through which an adversary can smuggle instructions. Existing defenses guard only t…
Real-world agent learning is often constrained by costly environment interactions, such as running time-consuming experiments or obtaining human feedback. In-context learning offers a highly sample-efficient way for agents to learn from their own interaction histories, but its ga…
Modern AI agents rely on elaborate inference harnesses such as Claude Code, Codex, and OpenClaw to drive multi-turn reasoning, tool use, and access to external systems. While powerful, these complex harnesses also make agents hard to train end-to-end with open infrastructure, who…
LLM-based agents leverage third-party skills to extend their capabilities in open-world scenarios. However, third-party skills can introduce extra security vulnerabilities, as seemingly harmless skills can contain latent safety risks that only emerge during actual execution. In t…
Agent safety is moving from content moderation toward preventing operational failures before tool-using agents act. We propose Janus, a foresight-oriented framework for long-horizon agent safety that trains guards to anticipate delayed risks from partial trajectories. Janus synth…
arXiv:2508.00429v5 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have achieved remarkable success in graph-based learning by propagating information among neighbor nodes via predefined aggregation mechanisms. However, such fixed schemes often suffer from two key l…
arXiv cs.AI
TIER_1English(EN)·Hassan Karim, Sai Sitharaman, Deepti Gupta, Danda B. Rawat·
arXiv:2607.18243v1 Announce Type: new Abstract: Agentic AI is crossing trust boundaries faster than current risk models can represent. Existing approaches provide one of two partial views. They either describe failure mechanisms without producing a transferable residual-risk esti…
arXiv cs.AI
TIER_1English(EN)·Ritvik Garimella, Vedant Khandelwal, Anvi Kohli, Amit Sheth·
arXiv:2607.18245v1 Announce Type: new Abstract: Exact-match evaluation of agent-calling obscures qualitatively different failure modes: a model may select the right function yet hallucinate argument values, or satisfy a schema while choosing a agent for the wrong reason. Existing…
arXiv:2607.18859v1 Announce Type: new Abstract: While Large Language Models have greatly advanced automated issue resolution, existing agent-based methods exhibit a fundamental limitation in their insufficient exploration of repair strategies. This insufficiency manifests in two …
arXiv:2607.18255v1 Announce Type: new Abstract: Contribution attribution has become a central problem in LLM-based multi-agent systems, where final outputs are produced through multiple agents, message exchanges, and ordered workflow dependencies. Existing attribution methods oft…
arXiv:2506.17913v2 Announce Type: replace Abstract: Graphical User Interface (GUI) agents have made significant progress in automating digital tasks through the utilization of computer vision and language models. Nevertheless, existing agent systems encounter notable limitations.…
arXiv cs.AI
TIER_1English(EN)·SangJin Park, Myungsub Choi, Jineok Kim, Minseung Kang·
arXiv:2607.18826v1 Announce Type: cross Abstract: LLM-agent defenses are typically evaluated one session at a time. In deployment, however, attacks can be distributed across independent agents, teams, and runtimes, leaving each local guardrail with only a sparse fragment. We form…
arXiv cs.AI
TIER_1English(EN)·Kunlun Zhu, Xuyan Ye, Zhiguang Han, Yuchen Zhao, Bingxuan Li, Weijia Zhang, Muxin Tian, Xiangru Tang, Pan Lu, James Zou, Jiaxuan You, Heng Ji·
arXiv:2607.18754v1 Announce Type: new Abstract: LLM agent failures are difficult to debug because the step where an error surfaces is often not the one that caused it. Existing observability tools replay execution traces but provide little support for identifying the root cause o…
arXiv:2607.18308v1 Announce Type: cross Abstract: Calibration of grey-box simulation models is a constrained optimization problem in which model evaluations are expensive, the parameter space can be high-dimensional, and the search must respect plausibility constraints. Although …
arXiv:2607.19297v1 Announce Type: new Abstract: This paper is a practitioner guide to graph-based workflow pathways for long-running, stateful, multi-step generative AI systems in business processes. Rather than treating LangGraph, a low-level orchestration framework for stateful…
arXiv cs.AI
TIER_1English(EN)·Grace Hui Yang, Pranav N. Venkit, Hooman Sedghamiz, Enrico Santus, Victor Dibia, Ioana Baldini·
arXiv:2607.19336v1 Announce Type: new Abstract: Agentic systems large language model (LLM) based architectures capable of reasoning, planning, acting, and coordinating with tools and other agents are rapidly transitioning from research prototypes to production scale deployments a…
arXiv:2607.18316v1 Announce Type: cross Abstract: Tool-augmented language-model agents execute multi-step workflows over external systems, resolving an entity once and then acting on it across subsequent steps. Prior work shows that in single-step actions, agents select the corre…
arXiv:2607.18816v1 Announce Type: cross Abstract: LLM-powered agents increasingly tackle complex tasks by invoking tools, querying databases, executing code, and manipulating intermediate artifacts. These agents follow trajectories that are typically stored as chronological logs,…
arXiv:2607.18597v1 Announce Type: new Abstract: Counterfactual credit assignment has proven effective in multi-agent reinforcement learning (MARL) for discrete action spaces, yet its extension to continuous-action cooperative tasks remains challenging. Existing methods that appro…
Traditional agent development is split across prompt templates, tool schemas, callback code, and workflow graphs. We present NVIDIA Object-Oriented Agents (NOOA), a model-agnostic Python framework for building reliable AI agents. NOOA takes a simpler approach: an agent is a Pytho…
As autonomous agents rapidly evolve, their ability to reliably manipulate ubiquitous digital documents has become critical for enabling general-purpose AI assistants and automating complex workspace workflows. In this paper, we introduce DocOps, a deterministically verifiable eva…
arXiv:2510.05743v3 Announce Type: replace Abstract: We review the historical development and current trends of artificially intelligent agents (agentic AI) in the social and behavioral sciences: from the first programmable computers, and social simulations soon thereafter, to tod…
arXiv:2607.16244v1 Announce Type: cross Abstract: Training multi-turn evidence-reading agents with outcome-only reinforcement learning is unstable because intermediate turns receive little direct credit. In HotpotQA experiments with Qwen2.5-3B-Instruct, GRPO initially improves (s…
arXiv cs.AI
TIER_1English(EN)·Chris Ge, Daria Kryvosheieva, Daniel Fried, Uzay Girit, Kaivalya Hariharan·
arXiv:2604.00594v2 Announce Type: replace Abstract: As the focus in LLM-based coding shifts from static single-step code generation to multi-step agentic interaction with tools and environments, understanding which tasks will challenge agents and why becomes increasingly difficul…
arXiv:2509.23071v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) agent development is hindered by the lack of executable ground-truth agent-environment interaction trajectories. Existing datasets provide questions, answers, and evidence, but lack fin…
arXiv:2607.18046v1 Announce Type: new Abstract: Graphical User Interface (GUI) agents powered by vision-language models hold promise for automating real-world mobile tasks. However, progress is limited by the lack of high-coverage, long-horizon interaction trajectories collected …
arXiv:2607.18171v1 Announce Type: new Abstract: Real-time multimodal applications, including voice agents and interactive video generation, compose heterogeneous models into pipelines whose efficient deployment requires application-specific decisions about placement, streaming, a…
arXiv cs.AI
TIER_1English(EN)·Arunabh Dastidar (for the Leni Team)·
arXiv:2607.17044v1 Announce Type: cross Abstract: Multi-step enterprise agent tasks fail in a characteristic way: single-pass inference has no checkpoint between deciding an answer and committing to it. We study one production system (Leni) whose architecture installs such checkp…
arXiv:2607.16621v1 Announce Type: new Abstract: Existing memory systems for long-horizon LLM agents often retrieve prior traces as passive context rather than converting them into executable capabilities. In this paper, we propose MSCE, a training-free Memory--Skill Co-Evolution …
arXiv:2607.16851v1 Announce Type: new Abstract: Deploying LLM agents typically requires a compact test-time student, even if a stronger teacher is available during training. We study knowledge brewing: distilling a teacher's interactive experience into a persistent external memor…
arXiv:2607.17038v1 Announce Type: new Abstract: This paper addresses key technical challenges in current large language model (LLM) agent applications, including long-horizon planning, sparse reward attribution, and dynamic environmental interaction, by designing and optimizing a…
arXiv:2607.17437v1 Announce Type: new Abstract: Large language model (LLM) agents offer a generative approach to simulating human behavior under conditions that may have few or no direct historical analogues, a common challenge in disaster and infrastructure-disruption planning. …
arXiv:2607.17701v1 Announce Type: new Abstract: Proactive agents are expected to anticipate user needs and provide autonomous assistance by perceiving environmental context without explicit instructions. A fundamental capability of such agents is to identify and track users' upco…
arXiv cs.AI
TIER_1English(EN)·Stefano Blando, Emanuele Guerrazzi, Riccardo Porcedda, Giuseppe Squillace, Max Tschaikowski, Andrea Vandin·
arXiv:2607.17948v1 Announce Type: new Abstract: Agent-based models (ABMs) rely on simple, explicit and reproducible rules for individual decision making, while complex collective behavior emerges from interactions among agents. Recent advances in large language models (LLMs) make…
arXiv:2607.16257v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a widely adopted technique for improving large language models (LLMs) on complex tasks. Despite this progress, existing RL methods still face challenges in training agents with longer-horizon…
arXiv:2406.18082v2 Announce Type: replace Abstract: AI agents have become increasingly significant in various domains, enabling autonomous decision-making and problem-solving. To function effectively, these agents require a planning process that determines the best course of acti…
arXiv cs.AI
TIER_1English(EN)·Tejas Singh Anand, Yuet Ying Christina Wang, Wanting Jiang, Steve Masson, Tian Zheng, Bingjie Zhou·
arXiv:2607.16345v1 Announce Type: cross Abstract: Modern agentic systems increasingly rely on skills: installable packages of natural language and code that teach an LLM agent to perform a domain task. As skill repositories grow, developers need automated quality signals on every…
arXiv cs.AI
TIER_1English(EN)·Julian Alfredo Mendez, Andreas Br\"annstr\"om·
arXiv:2607.16266v1 Announce Type: cross Abstract: Existing approaches for reasoning about action and change provide expressive semantics for modeling dynamic systems, in most cases built on top of logic programming systems. We introduce a modular framework for transition and traj…
arXiv:2607.16269v1 Announce Type: cross Abstract: Integrated sensing and communications (ISAC) is moving from proof-of-concept demonstrations to system-level deployment in sixth-generation (6G) networks. Because sensing and communication share hardware, spectrum, and waveform res…
arXiv:2607.16745v1 Announce Type: new Abstract: Multi-agent planning becomes substantially harder when agents must improve specialized decision-making skills while keeping their internal implementations private. This regime arises when agents are developed independently, expose d…
arXiv:2607.17352v1 Announce Type: new Abstract: Designing effective Lean proof agents is a central challenge in formal mathematical reasoning. Beyond building stronger provers, recent work emphasizes the workflow around Lean: how an agent decomposes proof obligations, uses tools …
arXiv cs.AI
TIER_1English(EN)·Babak Barazandeh, Subhabrata Majumdar, George Michailidis·
arXiv:2607.17082v1 Announce Type: new Abstract: Large language model agents solve tasks by generating trajectories that interleave planning, tool calls, and intermediate results. Current evaluation metrics reduce such a trajectory to a binary success flag or compare it against a …
arXiv:2607.17780v1 Announce Type: cross Abstract: ETAS is a programming language for agent systems that treats model-backed agents, tool calls, prompts, typed memory, human approvals, policies, and execution traces as semantic program elements rather than library conventions. It …
arXiv cs.AI
TIER_1English(EN)·Ryan Xu, Atlas Zhao, David Bao, Frank Du·
arXiv:2607.17299v1 Announce Type: cross Abstract: Long-horizon rollout generation has become the dominant systems bottleneck in agentic reinforcement learning (RL). As agents interact with environments over many turns, trajectories rapidly grow to tens of thousands of tokens, mak…
arXiv:2607.17288v1 Announce Type: cross Abstract: High quality temporal graph benchmarks with rich semantics and ground-truth anomaly labels are essential for training graph neural networks, yet remain scarce due to privacy constraints and annotation costs. We present SAGA (Synth…
arXiv cs.AI
TIER_1English(EN)·Chetan Arora, Andreas Vogelsang, Abbi Sharma·
arXiv:2607.17225v1 Announce Type: cross Abstract: Agentic AI systems do not just predict or recommend; they plan, maintain state, and act in external environments with varying degrees of autonomy. This changes the requirements engineering problem in a specific and under-addressed…
arXiv:2607.16200v1 Announce Type: new Abstract: AI agent systems that couple large language models (LLMs) with external tools and APIs are inherently non-deterministic: LLM sampling variance, external API state, CDN infrastructure headers, and execution-environment noise collecti…
Counterfactual credit assignment has proven effective in multi-agent reinforcement learning (MARL) for discrete action spaces, yet its extension to continuous-action cooperative tasks remains challenging. Existing methods that approximate the counterfactual baseline via Monte Car…
Counterfactual credit assignment has proven effective in multi-agent reinforcement learning (MARL) for discrete action spaces, yet its extension to continuous-action cooperative tasks remains challenging. Existing methods that approximate the counterfactual baseline via Monte Car…
LLM agent failures are difficult to debug because the step where an error surfaces is often not the one that caused it. Existing observability tools replay execution traces but provide little support for identifying the root cause or translating diagnosis into recovery. We presen…
Multi-agent LLM applications chain a planner, worker agents, a verifier, and a synthesizer, and every hop between agents is an unmonitored channel through which an adversary can smuggle instructions. Existing defenses guard only the input boundary (IBProtector, Llama Guard, perpl…
Graphical User Interface (GUI) agents powered by vision-language models hold promise for automating real-world mobile tasks. However, progress is limited by the lack of high-coverage, long-horizon interaction trajectories collected from element-rich and rapidly evolving apps. Exi…
Agent-based models (ABMs) rely on simple, explicit and reproducible rules for individual decision making, while complex collective behavior emerges from interactions among agents. Recent advances in large language models (LLMs) make it tempting to replace, enrich, or perturb thes…
ETAS is a programming language for agent systems that treats model-backed agents, tool calls, prompts, typed memory, human approvals, policies, and execution traces as semantic program elements rather than library conventions. It separates deterministic computation from agentic n…
arXiv cs.MA (Multiagent)
TIER_1English(EN)·Dimitrios S. Sfiris·
Agentic commerce extends agentic shopping into software agents that interpret policy, prepare checkout, generate transaction-facing language, and act under delegated payment authority. Protocols standardize external exchanges, but merchants still need one authoritative representa…
arXiv cs.AI
TIER_1English(EN)·Zherui Yang, Fan Liu, Hao Liu·
arXiv:2607.15901v1 Announce Type: new Abstract: Despite strong capabilities in data understanding and decision-making, autonomous data science agents still heavily rely on trial-and-error workflows that involve expensive computation. This bottleneck motivates models that can anti…
arXiv:2607.16133v1 Announce Type: cross Abstract: LLM powered multi-agent systems (MAS) have emerged as a promising paradigm for complex tasks. However, their advantages over single-agent systems (SAS) remain unclear, with performance varying inconsistently across settings. Here,…
arXiv:2607.15715v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly used for complex information-extraction tasks, yet it remains unclear whether agentic components such as reflection and memory lead to observable and controllable improvements over …
arXiv cs.AI
TIER_1English(EN)·Shuaiyu Zhou, Fengpeng Yue, Zengjie Hu, Yuanzhe Shen, Chenyang Zhang, feng hong, Cao Liu, Ke Zeng·
arXiv:2607.15660v1 Announce Type: new Abstract: While LLM agents demonstrate strong reasoning abilities in compact and well-defined scenarios, they struggle to maintain robustness and effectiveness when faced with large-scale, diverse, and dynamic real-world environments that dem…
Real-time multimodal applications, including voice agents and interactive video generation, compose heterogeneous models into pipelines whose efficient deployment requires application-specific decisions about placement, streaming, and intra-model parallelism. Existing serving sys…
Open agent marketplaces list native agents, tool bundles, and reusable skill packages in the same search interface, yet practitioners still have little guidance on how to retrieve across this mixed catalog. We study whether off-the-shelf retrieval models, trained for general text…
High quality temporal graph benchmarks with rich semantics and ground-truth anomaly labels are essential for training graph neural networks, yet remain scarce due to privacy constraints and annotation costs. We present SAGA (Synthetic Agentic Graph Architecture), a system for gen…
Multi-agent planning becomes substantially harder when agents must improve specialized decision-making skills while keeping their internal implementations private. This regime arises when agents are developed independently, expose different interfaces and capabilities, and must n…
Safe and socially compliant navigation in open human-robot environments requires robots to reason about heterogeneous participants with different dynamics, autonomy levels, and social roles. Existing trajectory prediction and planning methods often rely on homogeneous interaction…
Safe and socially compliant navigation in open human-robot environments requires robots to reason about heterogeneous participants with different dynamics, autonomy levels, and social roles. Existing trajectory prediction and planning methods often rely on homogeneous interaction…
arXiv:2607.14145v1 Announce Type: new Abstract: Tool-augmented large language model agents excel at long-horizon tasks, yet they are typically post-trained on fixed toolsets. When tasks demand new tools, these agents struggle to incorporate them effectively, and retraining from s…
arXiv cs.CL
TIER_1English(EN)·Amirhosein Ghasemabadi, Ruichen Chen, Bahador Rashidi, Di Niu·
arXiv:2607.14277v1 Announce Type: new Abstract: Large language models are increasingly deployed as agents, but reliable agentic behavior requires more than next-token prediction. At inference time, it is preferred that an agent can decide whether to proceed with its current reaso…
arXiv:2604.03465v2 Announce Type: replace Abstract: As web agents rapidly evolve, an increasing body of work has moved beyond conventional atomic browser interactions and explored tool use as a higher-level action paradigm. Although prior studies have shown the promise of tools, …
arXiv cs.AI
TIER_1English(EN)·Yuyao Zhang, Junjie Gao, Zhengxian Wu, Jiaming Fan, Jin Zhang, Shihan Ma, Yao Yao, Weiran Qi, Chuyan Jin, Guiyu Ma, Xingzhong Xu, Kai Yang, Ji-Rong Wen, Zhicheng Dou·
arXiv:2607.15257v1 Announce Type: new Abstract: Recent advances in Tool-Integrated Large Language Models have made web search a core capability of information-seeking agents. However, as interaction histories grow, agents increasingly struggle to track task progress. When search …
arXiv cs.AI
TIER_1English(EN)·Huanxi Liu, Kun Hu, Jiaqi Liao, Qiang Wang, Pengfei Qian, YuanZhao Zhai, Dawei Feng, Bo Ding, Huaimin Wang·
arXiv:2607.14642v1 Announce Type: new Abstract: As Model Context Protocol (MCP) servers emerge as the core infrastructure for connecting LLMs with external tools, existing benchmarks leverage real-world MCP servers to evaluate LLM agents' tool-using capabilities. However, these b…
arXiv:2607.14408v1 Announce Type: new Abstract: A self-evolving agentic loop repeatedly proposes a tweaked version of an agent (its prompt template or program) and accepts or rejects the change based on a per-iteration quality signal. Designing that signal is often the costly par…
arXiv:2607.14167v1 Announce Type: cross Abstract: LLM agents often retry after external validation rejects a candidate, but the interface between validation and the next model call remains underspecified. We introduce VeriHarness, a code-controlled agent loop in which models gene…
arXiv:2607.14159v1 Announce Type: new Abstract: An agent harness is the external control layer that turns a base LLM into an executable agent by managing context, tools, orchestration, memory, decoding, and output handling. While harness design strongly affects agent behavior, mo…
arXiv:2607.14186v1 Announce Type: cross Abstract: Scaling executable agent training data is bottlenecked by substrate-first methods that tie task generation to predefined tools, repositories, or skill graphs: expanding coverage requires manual expansion of the substrate, each new…
arXiv cs.AI
TIER_1English(EN)·Mu Yuan, Jinke Song, Zhaomeng Zhou, Lan Zhang·
arXiv:2607.15053v1 Announce Type: cross Abstract: The Internet taught us that the value of a network depends on \emph{how} its nodes connect: broadcast stars scale as $V\!\propto\!N$ (Sarnoff), fully-connected meshes as $N^2$ (Metcalfe), and group-forming networks as $2^{N}$ (Ree…
arXiv cs.AI
TIER_1English(EN)·Dylan Van Mulders, Matthias Bogaert, Dirk Van den Poel·
arXiv:2607.15095v1 Announce Type: cross Abstract: The formation of political coalitions is a complex negotiation driven by both concrete policy objectives and deep-seated ideological convictions. While Large Language Models (LLMs) open new avenues for computational political scie…
arXiv:2603.25031v2 Announce Type: replace Abstract: In psychological support and emotional companionship scenarios, the core limitation of large language models (LLMs) lies not merely in response quality, but in their reliance on local next-token prediction, which prevents them f…
Despite strong capabilities in data understanding and decision-making, autonomous data science agents still heavily rely on trial-and-error workflows that involve expensive computation. This bottleneck motivates models that can anticipate the effects of data science operations be…
Security-agent evaluations commonly measure peak offensive capability under generous inference budgets, emphasizing vulnerability discovery, exploit development, penetration testing, and CTF completion. Such measurements are useful but incomplete: in operational security, every r…
Recent advances in Tool-Integrated Large Language Models have made web search a core capability of information-seeking agents. However, as interaction histories grow, agents increasingly struggle to track task progress. When search attempts fail to yield useful evidence, current …
arXiv cs.MA (Multiagent)
TIER_1English(EN)·Dirk Van den Poel·
The formation of political coalitions is a complex negotiation driven by both concrete policy objectives and deep-seated ideological convictions. While Large Language Models (LLMs) open new avenues for computational political science, the neutrality and helpfulness biases instill…
arXiv cs.MA (Multiagent)
TIER_1English(EN)·Dirk Van den Poel·
The formation of political coalitions is a complex negotiation driven by both concrete policy objectives and deep-seated ideological convictions. While Large Language Models (LLMs) open new avenues for computational political science, the neutrality and helpfulness biases instill…
The Internet taught us that the value of a network depends on \emph{how} its nodes connect: broadcast stars scale as $V\!\propto\!N$ (Sarnoff), fully-connected meshes as $N^2$ (Metcalfe), and group-forming networks as $2^{N}$ (Reed). We ask the analogous question for networks of …
As Model Context Protocol (MCP) servers emerge as the core infrastructure for connecting LLMs with external tools, existing benchmarks leverage real-world MCP servers to evaluate LLM agents' tool-using capabilities. However, these benchmarks overlook the continuous evolution of t…
arXiv cs.AI
TIER_1English(EN)·Sagar Deb, Ashwanth Krishnan·
arXiv:2607.13618v1 Announce Type: new Abstract: LLM agents are increasingly evaluated on multi-week decision tasks in which the state that drives cost is never directly observed. On such tasks the final cost cannot say why an agent failed: it may have misread the world, or read i…
arXiv:2607.13396v1 Announce Type: new Abstract: What happens to an LLM agent's tool choice when the reliable tool silently changes within an ongoing session? We borrow set-shifting from cognitive psychology to study how well agents adapt to hidden reliability shifts. Our benchmar…
arXiv:2607.13285v1 Announce Type: new Abstract: The capability of a modern AI agent depends not only on its foundation model but also on its harness, which constructs prompts, manages state, invokes tools, and coordinates execution. As models, APIs, environments, and requirements…
arXiv cs.AI
TIER_1English(EN)·Ilias Kazantzidis, Timothy J. Norman, Yali Du, Christopher T. Freeman·
arXiv:2607.13172v1 Announce Type: new Abstract: We address the problem of safely training an agent policy and deploying a good and safe policy, in settings where the environment dynamics are unknown and no suitable reward function is available. In the context of safety-critical e…
arXiv cs.AI
TIER_1English(EN)·Zhe Ren, Yimeng Chen, Dandan Guo, Guowei Rong, Tonghui Li, R. B. Xiong, Qingfeng Lan, Wenyi Wang, Li Nanbo, Yibo Yang, Mingchen Zhuge, J\"urgen Schmidhuber·
arXiv:2607.13104v1 Announce Type: new Abstract: Self-improving autonomous agents are moving from research prototypes to deployed systems. The primary goal is controllable evolution, or adaptation, from experience with minimal or even no human input. This survey frames modern self…
arXiv:2607.13465v1 Announce Type: cross Abstract: LLM-based agents have rapidly improved at operating individual digital environments such as mobile applications, desktop systems, and smart homes. However, real-world user goals often span multiple devices: information may come fr…
arXiv:2607.13081v1 Announce Type: cross Abstract: We present nsfaguard, a guardrail framework for securing agentic AI systems against operational threats, such as prompt injection, sensitive information extraction, malicious code requests, dangerous tool misuse, and resource exha…
arXiv:2607.13071v1 Announce Type: cross Abstract: Agentic LLM coding tools compress long session histories into compaction summaries that subsequent sessions inherit as ground truth. This paper documents a failure mode in Claude Code where partial standard output from timed-out c…
arXiv:2607.14004v1 Announce Type: new Abstract: Most reported gains from agent-optimization methods are one-shot: an agent is optimized against a fixed benchmark and the resulting improvement is reported as if it were a stable property of the method. This does not test the settin…
arXiv:2607.13705v1 Announce Type: new Abstract: As Large Language Models (LLMs) evolve into autonomous agents, the need for unified evaluation infrastructure becomes critical. However, current evaluation pipelines remain highly fragmented and tightly coupled, hindering reproducib…
arXiv:2604.02668v2 Announce Type: replace-cross Abstract: Large language models (LLMs) often exhibit sycophancy: agreement with user stance even when it conflicts with the model's opinion. While prior work has mostly studied this in single-agent settings, it remains underexplored…
arXiv cs.AI
TIER_1English(EN)·Michael Rizvi-Martel, Satwik Bhattamishra, Neil Rathi, Guillaume Rabusseau, Michael Hahn·
arXiv:2510.13903v2 Announce Type: replace-cross Abstract: Chain-of-thought prompting has popularized step-by-step reasoning in large language models, yet model performance still degrades as problem complexity and context length grow. By decomposing difficult tasks with long conte…
arXiv:2602.11619v2 Announce Type: replace Abstract: Running the same LLM agent on identical inputs yields 2.3-4.2 distinct action sequences per 10 runs; this behavioral variance constitutes a training-free, black-box uncertainty signal that instantiates selective classification a…
Skills are a useful abstraction for software agents, turning human and agent experience into reusable procedural knowledge. Yet existing skill libraries are mostly hand-written, text-centric, or derived from agent traces, leaving tutorial videos and other multimodal human resourc…
Recent advances in Tool-Integrated Large Language Models have made web search a core capability of information-seeking agents. However, as interaction histories grow, agents increasingly struggle to track task progress. When search attempts fail to yield useful evidence, current …
Large language models are increasingly deployed as agents, but reliable agentic behavior requires more than next-token prediction. At inference time, it is preferred that an agent can decide whether to proceed with its current reasoning, defer to a stronger model, request additio…
Most reported gains from agent-optimization methods are one-shot: an agent is optimized against a fixed benchmark and the resulting improvement is reported as if it were a stable property of the method. This does not test the setting that matters for deployed agents, where optimi…
Multimodal agents that think with images iteratively manipulate visual evidence and invoke tools across many steps. Existing reinforcement learning methods reduce trajectories to scalar rewards, forcing the policy to discover reusable tool-use patterns from scratch on every new t…
As Large Language Models (LLMs) evolve into autonomous agents, the need for unified evaluation infrastructure becomes critical. However, current evaluation pipelines remain highly fragmented and tightly coupled, hindering reproducibility and causing redundant engineering. To addr…
As Large Language Models (LLMs) evolve into autonomous agents, the need for unified evaluation infrastructure becomes critical. However, current evaluation pipelines remain highly fragmented and tightly coupled, hindering reproducibility and causing redundant engineering. To addr…
An LLM agent's real-task performance is shaped as much by the harness around its model as by the frozen model itself: its prompts, injected knowledge, runtime control, and configuration. In deployment the harness is often the only lever available, so improving it automatically is…
LLM agents are increasingly evaluated on multi-week decision tasks in which the state that drives cost is never directly observed. On such tasks the final cost cannot say why an agent failed: it may have misread the world, or read it correctly and still failed to act (the knowing…
LLM agents are increasingly evaluated on multi-week decision tasks in which the state that drives cost is never directly observed. On such tasks the final cost cannot say why an agent failed: it may have misread the world, or read it correctly and still failed to act (the knowing…
We present MyAG, a graph-based framework for designing and analyzing composable LLM agent systems. Our framework separates agent system construction into three graph abstractions: a component graph for agents, environments, and modules; a workflow graph for execution control; and…
LLM-based agents have rapidly improved at operating individual digital environments such as mobile applications, desktop systems, and smart homes. However, real-world user goals often span multiple devices: information may come from a phone, be processed on a desktop, and the res…
arXiv cs.AI
TIER_1English(EN)·Xi Cheng, Ruiyan Zhu, Ke Liu, Rakesh Chowdary Machineni, Lyuhao Chen, Brian Zhu, Daniel Jin, Zheng Qi, Neeraj Parihar, Zhoutian Xu, Oliver Gao·
arXiv:2506.12339v2 Announce Type: replace-cross Abstract: We present SheetMind, a modular multi-agent framework powered by large language models (LLMs) for spreadsheet automation via natural language instructions. In this paper, we introduce a hierarchical agentic system consisti…
arXiv cs.AI
TIER_1English(EN)·Arastoo Zibaeirad, Marco Vieira, Thomas Zimmermann·
arXiv:2607.12058v1 Announce Type: cross Abstract: Given a vulnerability-fixing commit, trigger localization asks which specific statement turns the vulnerable program state into a concrete unsafe operation. This question is harder than binary vulnerability detection because the a…
arXiv:2607.12227v1 Announce Type: new Abstract: We revisit the evaluation of automatic harness evolution for LLM agents. Existing harness evolution methods use unit test cases to search for harness configurations and then report final performance on the same public benchmark. Thi…
arXiv cs.AI
TIER_1English(EN)·Yaopei Zeng, Congchao Wang, JianHang Chen, Nan Wang, Yurui Chang, Lu Lin·
arXiv:2607.12397v1 Announce Type: new Abstract: LLM agents act in external environments where each action changes the state that later decisions condition on, and where a single wrong step can waste interaction budget or trigger irreversible side effects long before the final fai…
arXiv cs.AI
TIER_1English(EN)·Edward Y. Chang, Emily J. Chang·
arXiv:2607.12480v1 Announce Type: new Abstract: This paper defines TRACE (Typed Reasoning And Commitment Evidence): a typed, versioned schema for recording reasoning traces, a reference procedure for writing records against it, and one operating discipline, no durable state chang…
arXiv:2607.13034v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly automate multi-step engineering and informatics workflows, yet they rarely ask how much effort a task actually requires. They often follow a maximum-context-first strategy--re-reading f…
arXiv:2607.12056v1 Announce Type: new Abstract: Online shopping is increasingly shifting toward a model in which AI agents independently search for products, compare options, evaluate constraints, and carry out parts of the purchasing process for users. Website design must now su…
arXiv:2510.18939v2 Announce Type: replace Abstract: Long-horizon agentic search requires iteratively exploring the web over long trajectories and synthesizing information across many sources, enabling powerful applications like deep research systems. In this work, we show that po…
arXiv:2607.12338v1 Announce Type: new Abstract: Agent benchmarks often compare two agents after all tasks have run, but costly evaluations make partial runs tempting. A task fraction alone does not show whether a partial run supports the same pairwise conclusion as the completed …
arXiv cs.AI
TIER_1English(EN)·Amin Beheshti, Rong N. Chang, Boualem Benatallah, Fabio Casati, Schahram Dustdar, Geoffrey Fox, Quan Z. Sheng, Yan Wang, Jian Yang, Albert Zomaya·
arXiv:2607.12619v1 Announce Type: new Abstract: The rapid emergence of LLM-powered autonomous and semi-autonomous agents is reshaping software systems from static, request-response components into goal-directed, adaptive, and tool-using computational actors. As these agents move …
arXiv:2607.12640v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards, and Group Relative Policy Optimization (GRPO) in particular, is now run routinely on a supervised checkpoint in the hope of producing a stronger agent. We ask whether it adds skill to …
What happens to an LLM agent's tool choice when the reliable tool silently changes within an ongoing session? We borrow set-shifting from cognitive psychology to study how well agents adapt to hidden reliability shifts. Our benchmark mounts tool-skill libraries with redundancies,…
What happens to an LLM agent's tool choice when the reliable tool silently changes within an ongoing session? We borrow set-shifting from cognitive psychology to study how well agents adapt to hidden reliability shifts. Our benchmark mounts tool-skill libraries with redundancies,…
Multi-agent systems (MAS) coordinate multiple LLM-powered agents through structured workflows, gaining reasoning power but incurring high inference latency from multi-step execution and repeated model invocations. Existing orchestration methods primarily optimize task performance…
As Large Language Models (LLMs) evolve into autonomous agents, the need for unified evaluation infrastructure becomes critical. However, current evaluation pipelines remain highly fragmented and tightly coupled, hindering reproducibility and causing redundant engineering. To addr…
Large language model (LLM) agents increasingly automate multi-step engineering and informatics workflows, yet they rarely ask how much effort a task actually requires. They often follow a maximum-context-first strategy--re-reading files and dependencies they have already seen--tu…
Reinforcement learning with verifiable rewards, and Group Relative Policy Optimization (GRPO) in particular, is now run routinely on a supervised checkpoint in the hope of producing a stronger agent. We ask whether it adds skill to a small language and vision-language model web a…
The rapid emergence of LLM-powered autonomous and semi-autonomous agents is reshaping software systems from static, request-response components into goal-directed, adaptive, and tool-using computational actors. As these agents move from isolated cognitive prototypes into complex …
Self-improving autonomous agents are moving from research prototypes to deployed systems. The primary goal is controllable evolution, or adaptation, from experience with minimal or even no human input. This survey frames modern self-improving agents as adaptive systems that conve…
This paper defines TRACE (Typed Reasoning And Commitment Evidence): a typed, versioned schema for recording reasoning traces, a reference procedure for writing records against it, and one operating discipline, no durable state change without a record. The paper argues in three la…
LLM agents act in external environments where each action changes the state that later decisions condition on, and where a single wrong step can waste interaction budget or trigger irreversible side effects long before the final failure is observed. Reliable deployment therefore …
LLM agents act in external environments where each action changes the state that later decisions condition on, and where a single wrong step can waste interaction budget or trigger irreversible side effects long before the final failure is observed. Reliable deployment therefore …
Agent benchmarks often compare two agents after all tasks have run, but costly evaluations make partial runs tempting. A task fraction alone does not show whether a partial run supports the same pairwise conclusion as the completed benchmark. We study this question by replaying c…
arXiv cs.LG
TIER_1English(EN)·Ruksat Khan Shayoni, Muhammad Faraz Shoaib, S M Asif Hossain, M. F. Mridha·
arXiv:2607.10490v1 Announce Type: cross Abstract: Tool-using large language model (LLM) agents are attractive for network operations, but tickets, alerts, logs, runbooks, and ChatOps messages can carry indirect prompt injections. We present NetInjectBench, a 130-scenario benchmar…
arXiv:2607.09748v1 Announce Type: new Abstract: In distributed systems, the classical State Machine Replication (SMR) model assumes that correct replicas execute deterministic transitions to yield identical bitwise states. However, the rise of agentic distributed systems -- where…
arXiv:2607.09765v1 Announce Type: new Abstract: A cheap swarm of unreliable agents can be steered to a correct consensus by a few strong, expensive "oracle" correctors. We ask how much one must spend, and where to place the oracles. We model the swarm as a consensus on a graph in…
arXiv cs.AI
TIER_1English(EN)·Yaowen Ye, Jacob Steinhardt·
arXiv:2607.09766v1 Announce Type: new Abstract: AI agents are increasingly deployed in shared environments where they pursue diverse goals and compete for rewards. This multi-agent competition can lead to behaviors that serve individual gains at collective cost -- for instance, m…
arXiv:2607.09794v1 Announce Type: new Abstract: Context learning is an emerging inference-time task where LLMs must learn and apply novel, task-specific knowledge from intricate contexts absent from pre-training; even frontier models score under 24% task success. In this work, we…
arXiv:2607.10113v1 Announce Type: new Abstract: Large language model agents increasingly store reusable procedures outside the model. These reusable procedures are often called \emph{skills}: they may be code functions, natural-language instructions, SKILL.md packages, workflow g…
arXiv:2607.10144v1 Announce Type: new Abstract: Scientific research is a complex, multi-stage workflow rather than a single act of text generation. The ideation process typically emerges through literature search, paper reading, tool use, claim checking, cross-paper synthesis, br…
arXiv:2607.10487v1 Announce Type: cross Abstract: LLM agents can commit durable effects from authority evidence that was valid earlier in execution: a DOM snapshot, approval epoch, version witness, branch token, or worker result. We study the commit boundary at which earlier auth…
arXiv cs.AI
TIER_1English(EN)·Zhuoyun Du, Runze Wang, Huiyu Bai, Zouying Cao, Xiaoyong Zhu, Yu Cheng, Bo Zheng, Wei Chen, Haochao Ying·
arXiv:2511.09149v5 Announce Type: replace-cross Abstract: While natural language is the de facto communication medium for LLM-based agents, it presents a fundamental constraint. The process of downsampling rich, internal latent states into discrete tokens inherently limits the de…
arXiv:2607.10198v1 Announce Type: new Abstract: Search APIs are the fundamental retrieval layer for many agents and are often their most frequently used tool. Traditional search APIs provide URLs, titles, and snippets that preview website contents. Because full-page retrieval is …
arXiv:2607.10557v1 Announce Type: new Abstract: Multimodal BrowseComp tasks require agents to combine perception, tool use, and long-horizon reasoning over dynamic web content, challenging their ability to handle compositional structure, open-world uncertainty, and multimodal int…
arXiv:2607.10645v1 Announce Type: new Abstract: An LLM agent's public behaviour reveals little about its social reasoning: an agent that votes correctly may be guessing, and an agent that lies well leaves no trace of what it actually believes. We present MafiaScope, an open testb…
arXiv:2607.11288v1 Announce Type: cross Abstract: We introduce the Self-Evolving Agentic Operating System (SE-AOS): a new class of AI agent that treats exploit capability as a mutable, versioned kernel it extends at runtime, observing its own failures, synthesising new capabiliti…
arXiv:2605.11504v2 Announce Type: replace Abstract: Recent advances in Large Language Models (LLMs) have enabled agentic systems for complex, multi-step tasks; cybersecurity is emerging as a prominent application. To evaluate such agents, researchers widely adopt Capture The Flag…
arXiv:2607.09711v1 Announce Type: new Abstract: Existing agent benchmarks primarily test task completion, tool use, or skill utility, but do not isolate whether a runtime can convert evidence from its own runs into reusable skills that improve fresh executions after authoring ove…
arXiv:2606.08878v2 Announce Type: replace Abstract: Real-world LLM applications are moving beyond single-agent workflows toward orchestrated multi-agent systems, yet current models still struggle to determine what each sub-agent needs to know. To measure this, we introduce Perspe…
arXiv:2607.11423v1 Announce Type: new Abstract: Agentic coding tools present new opportunities to transform research workflows. The performance of agent systems built depends on both large language models (LLMs) and the harness around LLMs, which is the orchestration code that de…
arXiv:2607.11388v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) and vision-language models (VLMs) have enabled increasingly capable digital agents for computer use. However, real-world tasks are often long-horizon and involve evolving contexts cont…
arXiv cs.AI
TIER_1English(EN)·Chenglin Yu, Li Yin, Ying Yu, Hongxia Yang, Ming Li·
arXiv:2607.11346v1 Announce Type: new Abstract: Enterprise agents must follow long-horizon, conditional, safety-critical standard operating procedures (SOPs). We compile machine-readable SOP constraints into executable pseudo-code and run them with a program-guided (PG) stack mac…
arXiv:2607.11226v1 Announce Type: new Abstract: LLM agents today are caught in an awkward bind. Lock them down with static safety instructions and they rarely venture beyond the obvious; give them free reign with tools and multi-agent debate, and safety violations quickly follow.…
arXiv:2607.11185v1 Announce Type: new Abstract: Computer use agents (CUAs) are emerging as a powerful interface for automating complex digital workflows through visual perception and GUI execution. Online reinforcement learning with verifiable rewards (RLVR) has emerged as a key …
arXiv:2607.11149v1 Announce Type: new Abstract: LLM agent benchmarks measure task completion, reliability, and inference cost, but not the persistent data an agent run leaves on disk, including logs, context snapshots, checkpoints, and debug traces. We introduce AgentFootprint, a…
arXiv:2607.11138v1 Announce Type: new Abstract: The rapid expansion of capabilities in Large Language Model (LLM) agents has exposed a critical architectural bottleneck: when agents are given access to a flat, monolithic registry of tools, the model must evaluate hundreds or thou…
arXiv cs.AI
TIER_1English(EN)·Qijia Shen, Zhiqi Huang, Vamsidhar Kamanuru, Aznaur Aliev, Jay Rainton, Ahmed Awelkair, Zhichen Zeng, Jiajun Li, Shi Dong, Yueming Yuan, Boyuan Ma, Qizheng Zhang, Jiwei Fu, Yuzhen Mao, Wendong Fan, Ping Nie, Philip Torr, Bernard Ghanem, Changran Hu, Jona…·
arXiv:2607.10891v1 Announce Type: new Abstract: Large language models (LLMs) are rapidly shifting toward agents that solve tasks through diverse interfaces, including web and graphical user interfaces (GUIs). Among these, the terminal command line provides a text-based, general-p…
arXiv:2607.10836v1 Announce Type: new Abstract: Multi-agent ensembling multiplies active parameters and inference cost without answering three basic questions: which agents to consult, how deeply a query should traverse a hierarchy of agents, and when inter-agent communication is…
arXiv:2607.10768v1 Announce Type: new Abstract: LLM-based agents are increasingly deployed to solve optimization problems, yet existing benchmarks evaluate them on pre-structured mathematical formulations that bypass the most critical challenge: translating complex business requi…
arXiv:2607.10750v1 Announce Type: new Abstract: Synthetic data is widely used to train large language models because it is inexpensive to generate and easy to control. As models are increasingly deployed as agents, synthetic trajectories are likely to become an important source o…
arXiv cs.AI
TIER_1English(EN)·Yixiong Chen, Alan Yuille·
arXiv:2607.10601v1 Announce Type: new Abstract: Large Language Model (LLM) agents are commonly trained from expert trajectories using supervised fine-tuning (SFT), which treats multi-turn agent behavior as ordinary text imitation. This recipe is simple and low-cost, but it only l…
arXiv:2607.10526v1 Announce Type: new Abstract: Stateful personal agents increasingly maintain long-term user profiles, episodic memories, and reusable skills. This persistence turns conversational sycophancy into a state-writing failure: accepted user-centric claims can be commi…
arXiv cs.AI
TIER_1English(EN)·Varun Gandhi, Jaewook Lee, Shantanu Todmal, Franck Dernoncourt, Ryan Rossi, Zichao Wang, Andrew Lan·
arXiv:2607.10463v1 Announce Type: new Abstract: Agentic retrieval-augmented generation (RAG) extends static RAG by allowing language models to iteratively reason, generate search queries, retrieve evidence, and predict answers. However, it remains challenging for models to decide…
arXiv:2607.11098v1 Announce Type: cross Abstract: Tool-using LLM agents are mostly evaluated assuming all tools work. When a tool times out, returns a week-stale value, or has its description poisoned in deployment, the developer needs a controlled way to reproduce the failure, t…
arXiv cs.AI
TIER_1English(EN)·Yunbo Lyu, David Williams, Jieke Shi, Zhensu Sun, Chao Peng, Zhou Yang, Federica Sarro, David Lo·
arXiv:2607.10856v1 Announce Type: cross Abstract: The rise of Software Engineering (SE) agents, i.e., LLM-based agents that can understand large codebases and carry out engineering tasks with limited human intervention, has been marked by rapid advances and adoption, but little i…
arXiv cs.AI
TIER_1English(EN)·Yuan Gao, Jiangyi Yang, Yao Zhao, Yichi Zhang·
arXiv:2607.10814v1 Announce Type: cross Abstract: Evaluating LLM agents in hidden-information multi-agent settings is hard: final outcomes are high-variance and rarely reveal why an agent decided as it did. We study this in a 9-player Werewolf environment where agents act under s…
We revisit the evaluation of automatic harness evolution for LLM agents. Existing harness evolution methods use unit test cases to search for harness configurations and then report final performance on the same public benchmark. This protocol raises two fundamental concerns. Firs…
The capability of a modern AI agent depends not only on its foundation model but also on its harness, which constructs prompts, manages state, invokes tools, and coordinates execution. As models, APIs, environments, and requirements evolve, the harness must be continually modifie…
Self-improving autonomous agents are moving from research prototypes to deployed systems. The primary goal is controllable evolution, or adaptation, from experience with minimal or even no human input. This survey frames modern self-improving agents as adaptive systems that conve…
Agentic coding tools present new opportunities to transform research workflows. The performance of agent systems built depends on both large language models (LLMs) and the harness around LLMs, which is the orchestration code that determines an agent's behavior. We present ToFu, a…
Recent advances in large language models (LLMs) and vision-language models (VLMs) have enabled increasingly capable digital agents for computer use. However, real-world tasks are often long-horizon and involve evolving contexts containing accumulated observations, intermediate ed…
Enterprise agents must follow long-horizon, conditional, safety-critical standard operating procedures (SOPs). We compile machine-readable SOP constraints into executable pseudo-code and run them with a program-guided (PG) stack machine that pages the active frame while an LLM pe…
We introduce the Self-Evolving Agentic Operating System (SE-AOS): a new class of AI agent that treats exploit capability as a mutable, versioned kernel it extends at runtime, observing its own failures, synthesising new capabilities, proving them against a live target, and hot-lo…
LLM agents today are caught in an awkward bind. Lock them down with static safety instructions and they rarely venture beyond the obvious; give them free reign with tools and multi-agent debate, and safety violations quickly follow. Rather than forcing a single model to juggle bo…
We present nsfaguard, a guardrail framework for securing agentic AI systems against operational threats, such as prompt injection, sensitive information extraction, malicious code requests, dangerous tool misuse, and resource exhaustion. We first introduce the NSFA taxonomy, whic…
LLM agent benchmarks measure task completion, reliability, and inference cost, but not the persistent data an agent run leaves on disk, including logs, context snapshots, checkpoints, and debug traces. We introduce AgentFootprint, a cross-framework benchmark of post-run agent sto…
The rapid expansion of capabilities in Large Language Model (LLM) agents has exposed a critical architectural bottleneck: when agents are given access to a flat, monolithic registry of tools, the model must evaluate hundreds or thousands of options simultaneously. This leads to d…
Tool-using LLM agents are mostly evaluated assuming all tools work. When a tool times out, returns a week-stale value, or has its description poisoned in deployment, the developer needs a controlled way to reproduce the failure, test a fix, and confirm the fix worked before deplo…
arXiv cs.AI
TIER_1English(EN)·Zac Garby, Andrew D. Gordon, David Sands·
arXiv:2602.20064v2 Announce Type: replace-cross Abstract: Large language models are increasingly deployed as agents: they plan, call tools, read untrusted data, and act on the results. This exposes them to prompt injection: data meant only to be read is obeyed as an instruction. …
arXiv cs.AI
TIER_1English(EN)·Carissa Cullen, Harry Garland, Alexander Roman, Louis Thomson, Christos Ziakas, Elliott Thornley·
arXiv:2604.17502v4 Announce Type: replace Abstract: Misaligned artificial agents might resist shutdown. One proposed solution is to train agents to lack preferences between different-length trajectories. The Discounted Reward for Same-Length Trajectories (DReST) reward function d…
arXiv cs.AI
TIER_1English(EN)·Zhenxiao Fu, Lei Jiang, Yilun Xu, Gang Huang, Fan Chen·
arXiv:2508.20134v2 Announce Type: replace Abstract: Programming quantum circuits at the OpenQASM level is essential for achieving hardware-aware optimization and reliable execution on noisy intermediate-scale quantum (NISQ) devices, yet it remains challenging due to the need for …
arXiv:2607.09059v1 Announce Type: new Abstract: We present ARCANA, a collaborative multi agent framework for solving ARC AGI 2 tasks under strict test time and hardware constraints. ARCANA decomposes each task into iterative perception, hypothesis generation, symbolic execution, …
arXiv:2607.09403v1 Announce Type: new Abstract: Worldbuilding, the construction of coherent fictional worlds, is a foundational task in game design and literary creation. Large Language Models (LLMs) offer new possibilities for automated content generation, but their application …
arXiv cs.AI
TIER_1English(EN)·Kaiji Zhou, Ales Leonardis, Yue Feng·
arXiv:2607.09600v1 Announce Type: new Abstract: Enhancing the reasoning capabilities of large language model (LLM) agents requires effective orchestration of diverse expert models and tools. However, existing frameworks typically call APIs based on coarse-grained matching between…
arXiv cs.AI
TIER_1English(EN)·Ning Liu, Kalle Kujanp\"a\"a, Zhaoxuan Zhu, P Aditya Sreekar, Kaiwen Liu, Chuanneng Sun, Jorge Marchena Menendez, Matthew Bales, Tianyu Yang, Shahnawaz Alam, Rose Yu, Baoyuan Liu, Kristina Klinkner, Shervin Malmasi·
arXiv:2607.08960v1 Announce Type: cross Abstract: Warehouse operations are governed by Standard Operating Procedures (SOPs) that encode complex, multi-system decision logic, which must be executed reliably under strict time constraints, yet LLM agents lack mechanisms to enforce p…
arXiv:2607.09195v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly expected to play a central role in AI-driven scientific discovery. Equipped with broad knowledge, flexible reasoning, and tool use, they have the potential to autonomously explore a…
arXiv cs.AI
TIER_1English(EN)·Dan C. Hsu, Luke Lu·
arXiv:2607.09175v1 Announce Type: new Abstract: Deployed LLM agents rely on agentic context, the model-external textual control content assembled by an operational harness. In this work, the mutable component of that context is a persistent system-level instruction that is update…
arXiv:2607.08964v1 Announce Type: new Abstract: AI agents have become capable of autonomously completing short, well-specified tasks. However, existing terminal benchmarks largely focus on simple problems that finish within minutes and are evaluated only by their final outcome. T…
arXiv:2607.08894v1 Announce Type: new Abstract: Large Language Model (LLM) agents have shown promise in multi-step planning tasks, but existing approaches like LATS (Language Agent Tree Search) and ReAct rely heavily on LLM inference during planning, leading to high computational…
Multi-agent ensembling multiplies active parameters and inference cost without answering three basic questions: which agents to consult, how deeply a query should traverse a hierarchy of agents, and when inter-agent communication is worth its cost. We present GRADE (Gated Routing…
Evaluating LLM agents in hidden-information multi-agent settings is hard: final outcomes are high-variance and rarely reveal why an agent decided as it did. We study this in a 9-player Werewolf environment where agents act under strict, code-level information isolation, and we bu…
Synthetic data is widely used to train large language models because it is inexpensive to generate and easy to control. As models are increasingly deployed as agents, synthetic trajectories are likely to become an important source of training data for agentic behavior. We investi…
An LLM agent's public behaviour reveals little about its social reasoning: an agent that votes correctly may be guessing, and an agent that lies well leaves no trace of what it actually believes. We present MafiaScope, an open testbed that turns the social deduction game Mafia in…
An LLM agent's public behaviour reveals little about its social reasoning: an agent that votes correctly may be guessing, and an agent that lies well leaves no trace of what it actually believes. We present MafiaScope, an open testbed that turns the social deduction game Mafia in…
Agentic retrieval-augmented generation (RAG) extends static RAG by allowing language models to iteratively reason, generate search queries, retrieve evidence, and predict answers. However, it remains challenging for models to decide when to retrieve, whether to use lexical matchi…
Agentic retrieval-augmented generation (RAG) extends static RAG by allowing language models to iteratively reason, generate search queries, retrieve evidence, and predict answers. However, it remains challenging for models to decide when to retrieve, whether to use lexical matchi…
Agentic retrieval-augmented generation (RAG) extends static RAG by allowing language models to iteratively reason, generate search queries, retrieve evidence, and predict answers. However, it remains challenging for models to decide when to retrieve, whether to use lexical matchi…
Enhancing the reasoning capabilities of large language model (LLM) agents requires effective orchestration of diverse expert models and tools. However, existing frameworks typically call APIs based on coarse-grained matching between tasks and the functions of expert models or too…
Worldbuilding, the construction of coherent fictional worlds, is a foundational task in game design and literary creation. Large Language Models (LLMs) offer new possibilities for automated content generation, but their application to worldbuilding faces three challenges: context…
Large language model (LLM) agents are increasingly expected to play a central role in AI-driven scientific discovery. Equipped with broad knowledge, flexible reasoning, and tool use, they have the potential to autonomously explore and solve scientific problems by repeatedly propo…
Deployed LLM agents rely on agentic context, the model-external textual control content assembled by an operational harness. In this work, the mutable component of that context is a persistent system-level instruction that is updated from operational experience while the model, t…
arXiv:2607.08010v1 Announce Type: new Abstract: Production LLM agents often waste latency and reliability by regenerating code for the same procedural steps on every request. We replace this inference-time coding loop with an agentic tool-making pipeline that compiles repeated SO…
arXiv:2607.08768v1 Announce Type: new Abstract: The rapid development of large language models and multimodal large language models has accelerated the emergence of proactive agents capable of operating everyday tools and assisting users in real-world environments. However, exist…
arXiv:2607.08093v1 Announce Type: new Abstract: Large language models (LLMs) increasingly act as integrated data-science agents, combining abstract reasoning with advanced tool use. Yet the relevant benchmark landscape largely divides into symbolic causal reasoning benchmarks wit…
arXiv cs.AI
TIER_1English(EN)·Corban Villa, Alp Eren Ozdarendeli, Sijun Tan, Raluca Ada Popa·
arXiv:2607.08147v1 Announce Type: cross Abstract: Autonomous web agents promise to automate everyday browsing tasks, but inherit one of the web's oldest attack surfaces. Cross-Site Scripting proved that mixing trusted and untrusted content is dangerous, even on benign pages. Agen…
arXiv:2607.08495v1 Announce Type: cross Abstract: Sharp et al. (2025) introduce "agentic inequality" as a framework for analyzing disparities in access to AI agents across three dimensions: availability, quality, and quantity. These person- and organization-level dimensions chara…
arXiv:2607.08565v1 Announce Type: cross Abstract: LLM scheduling is critical to serving, yet it remains unclear how well existing designs fit agentic serving--with LLM requests issued by agents instead of humans. This shifts the workload in two ways: (1) agents act only on comple…
arXiv:2607.08662v1 Announce Type: cross Abstract: Large language model (LLM)-based web search agents are transforming information seeking from simple factoid question answering into complex, deep-and-wide search and research-oriented tasks. A single ReAct-style agent is constrain…
arXiv cs.AI
TIER_1English(EN)·Linghua Zhang, Jun Wang, Jingtong Wu, Zhisong Zhang·
arXiv:2603.16453v3 Announce Type: replace Abstract: Large language model (LLM) agents have made rapid progress on short-horizon, well-scoped tasks, yet their ability to sustain coherent decisions in dynamic long-horizon environments remains uncertain. We introduce RetailBench, a …
arXiv:2607.07721v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) and agentic frameworks have advanced enterprise AI considerably, yet agents remain fundamentally reactive: they wait for a human query before acting. This paper argues that genuine enterprise pro…
We present ARCANA, a collaborative multi agent framework for solving ARC AGI 2 tasks under strict test time and hardware constraints. ARCANA decomposes each task into iterative perception, hypothesis generation, symbolic execution, and reflective refinement. A perceptual groundin…
Warehouse operations are governed by Standard Operating Procedures (SOPs) that encode complex, multi-system decision logic, which must be executed reliably under strict time constraints, yet LLM agents lack mechanisms to enforce procedural compliance and degrade under the context…
Large Language Model (LLM) agents have shown promise in multi-step planning tasks, but existing approaches like LATS (Language Agent Tree Search) and ReAct rely heavily on LLM inference during planning, leading to high computational costs and stochastic behavior. We present \text…
The rapid development of large language models and multimodal large language models has accelerated the emergence of proactive agents capable of operating everyday tools and assisting users in real-world environments. However, existing benchmarks struggle to evaluate such agents …
Large language model (LLM)-based web search agents are transforming information seeking from simple factoid question answering into complex, deep-and-wide search and research-oriented tasks. A single ReAct-style agent is constrained by one long trajectory and limited context, mak…
LLM scheduling is critical to serving, yet it remains unclear how well existing designs fit agentic serving--with LLM requests issued by agents instead of humans. This shifts the workload in two ways: (1) agents act only on complete responses, making the cluster's tokens per seco…
Sharp et al. (2025) introduce "agentic inequality" as a framework for analyzing disparities in access to AI agents across three dimensions: availability, quality, and quantity. These person- and organization-level dimensions characterize who can access agents and at what capabili…
Context learning is an emerging inference-time task where LLMs must learn and apply novel, task-specific knowledge from intricate contexts absent from pre-training; even frontier models score under 24% task success. In this work, we conduct a comprehensive empirical study to unde…
Large language models (LLMs) increasingly act as integrated data-science agents, combining abstract reasoning with advanced tool use. Yet the relevant benchmark landscape largely divides into symbolic causal reasoning benchmarks without realistic data analysis or data analysis be…
Large language models (LLMs) increasingly act as integrated data-science agents, combining abstract reasoning with advanced tool use. Yet the relevant benchmark landscape largely divides into symbolic causal reasoning benchmarks without realistic data analysis or data analysis be…
arXiv:2607.07702v1 Announce Type: new Abstract: The optimization of long-horizon agents increasingly relies on reflection-based mechanisms, where a large language model (LLM) acts as an optimizer to diagnose agent failures and improve agent policies. However, real execution trace…
arXiv:2607.07548v1 Announce Type: new Abstract: Large language model based search agents increasingly adopt multi-agent architectures in which a main agent decomposes a complex question into sub-queries and dispatches them to parallel sub-agents. However, existing systems instant…
arXiv:2607.07052v1 Announce Type: cross Abstract: AI agents deployed for IT operations are typically permanent cost centers because every execution requires full LLM inference, even for previously solved problems. This paper introduces progressive crystallization, a lifecycle tha…
arXiv cs.AI
TIER_1English(EN)·Jiayi Geng, Graham Neubig·
arXiv:2603.21489v2 Announce Type: replace-cross Abstract: AI agents have become increasingly capable at isolated software engineering (SWE) tasks such as resolving issues on Github. Yet long-horizon tasks involving multiple interdependent subtasks still pose challenges both with …
arXiv:2607.07436v1 Announce Type: new Abstract: A self-evolving agent retires its bad skills by watching them fail, so what happens when the judge cannot see the failures? Skill retirement is the structural constraint that keeps a growing library from drifting below the no-skill …
arXiv cs.AI
TIER_1English(EN)·Sifat Afroj Moon, Dakotah Maguire, Adam Spannaus, Joe Tuccillo, Maksudul Alam, Sudip K. Seal, John Gounley, Heidi Hanson·
arXiv:2607.06757v1 Announce Type: new Abstract: Agent-based modeling (ABM) has the capability to model millions of individuals and their interactions, which is useful for policy making. However, ABMs have traditionally relied on static prior, which prevents the models from adapti…
arXiv cs.AI
TIER_1English(EN)·Kabir Moghe, Peter Chin·
arXiv:2607.06764v1 Announce Type: new Abstract: Recent progress on ARC-AGI-1 from disclosed architectures has come broadly from two regimes: heavy test-time compute over frontier models (evolutionary search, exhaustive sampling, extended chain-of-thought), or benchmark-specific t…
arXiv:2607.07321v1 Announce Type: new Abstract: Tool utilization enables Large Language Model (LLM) agents to interact with the real world and resolve complex tasks. However, existing agent frameworks predominantly rely on static toolsets composed of granular atomic actions (e.g.…
arXiv:2607.07397v1 Announce Type: new Abstract: Autonomous agents promise substantial gains in speed, scale, and labor efficiency, but their failures can impose abrupt and often irreversible costs. The central challenge for agentic automation is therefore to increase the benefits…
arXiv:2605.15207v2 Announce Type: replace Abstract: Multi-agent LLM systems have shown promise for complex reasoning, yet recent evaluations reveal they often underperform single-model baselines. We identify a structural failure mode in sequential fine-tuning of shared-context te…
arXiv cs.AI
TIER_1English(EN)·Razvan Mihai Popescu·
arXiv:2607.06713v1 Announce Type: cross Abstract: Large language models are rapidly moving towards closing the development cycle, transitioning from simple assistive companions to autonomous contributors deeply embedded into collaborative development environments. Despite their a…
Production LLM agents often waste latency and reliability by regenerating code for the same procedural steps on every request. We replace this inference-time coding loop with an agentic tool-making pipeline that compiles repeated SOP steps into validated, versioned tools before d…
Production LLM agents often waste latency and reliability by regenerating code for the same procedural steps on every request. We replace this inference-time coding loop with an agentic tool-making pipeline that compiles repeated SOP steps into validated, versioned tools before d…
CausalDS is a benchmark for evaluating causal reasoning in data-science workflows that combines synthetic causal structures with realistic observational data and natural-language stories across Pearl's three rungs of causal inference.
AI agents have become capable of autonomously completing short, well-specified tasks. However, existing terminal benchmarks largely focus on simple problems that finish within minutes and are evaluated only by their final outcome. This setup overlooks intermediate progress and pa…
UniClawBench introduces a capability-driven benchmark for evaluating proactive agents in real-world environments using live Docker container evaluation and closed-loop assessment with multiple agent roles.
The optimization of long-horizon agents increasingly relies on reflection-based mechanisms, where a large language model (LLM) acts as an optimizer to diagnose agent failures and improve agent policies. However, real execution traces are difficult to use directly for optimization…
Long-horizon LLM multi-agent systems face reliability risks invisible to infrastructure monitoring. We propose the ADE Predictive Reliability Framework (ADE-PRF), enabling proactive health trajectory prediction from passive degradation detection. ADE-PRF aggregates 20 heterogeneo…
Large language model based search agents increasingly adopt multi-agent architectures in which a main agent decomposes a complex question into sub-queries and dispatches them to parallel sub-agents. However, existing systems instantiate all roles from a single model of identical …
A self-evolving agent retires its bad skills by watching them fail, so what happens when the judge cannot see the failures? Skill retirement is the structural constraint that keeps a growing library from drifting below the no-skill baseline, but its guarantee assumes an unbiased …
A self-evolving agent retires its bad skills by watching them fail, so what happens when the judge cannot see the failures? Skill retirement is the structural constraint that keeps a growing library from drifting below the no-skill baseline, but its guarantee assumes an unbiased …
Autonomous agents promise substantial gains in speed, scale, and labor efficiency, but their failures can impose abrupt and often irreversible costs. The central challenge for agentic automation is therefore to increase the benefits of automation while bounding the consequences o…
Tool utilization enables Large Language Model (LLM) agents to interact with the real world and resolve complex tasks. However, existing agent frameworks predominantly rely on static toolsets composed of granular atomic actions (e.g., basic file I/O or single-turn search), which f…
Tool utilization enables Large Language Model (LLM) agents to interact with the real world and resolve complex tasks. However, existing agent frameworks predominantly rely on static toolsets composed of granular atomic actions (e.g., basic file I/O or single-turn search), which f…
AI agents deployed for IT operations are typically permanent cost centers because every execution requires full LLM inference, even for previously solved problems. This paper introduces progressive crystallization, a lifecycle that treats agent exploration as a discovery mechanis…
arXiv:2607.06233v1 Announce Type: new Abstract: LLM-powered data agents are playing an increasingly important role in data-driven decision making. However, existing data agents struggle to generalize to unseen data environments and analytical workflows, especially in heterogeneou…
arXiv:2607.06157v1 Announce Type: cross Abstract: Deliberation plays a crucial role in collaboration; when humans work together, they naturally engage in communication to align information and reach an agreement. In this paper, we investigate deliberative large language model (LL…
arXiv cs.AI
TIER_1English(EN)·Wael Albayaydh, Rui Zhao, Ivan Flechais·
arXiv:2607.05775v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly evaluated on their ability to use tools, plan multi-step tasks, coordinate with other agents, and operate over extended horizons. Reported benchmark gains often obscure recurring fa…
arXiv cs.AI
TIER_1English(EN)·Zeyu Xia, Jinzhe Ma, Congjie Zheng, Zhongyao Wang, Shufei Zhang, Yuqiang Li, Hang Su, P. Hu, Changshui Zhang, Xingao Gong, Wanli Ouyang, Lei Bai, Dongzhan Zhou, Mao Su·
arXiv:2512.19458v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly embedded in agentic frameworks for scientific discovery. First-principles materials computation imposes a demanding standard for autonomy: successful execution depends on internally …
arXiv cs.AI
TIER_1English(EN)·Gil Pasternak, Dheeraj Rajagopal, Julia White, Dhruv Atreja, Matthew Thomas, George Hurn-Maloney, Ash Lewis·
arXiv:2510.19771v4 Announce Type: replace Abstract: LLM-based agents are increasingly moving towards proactivity: rather than awaiting instruction, they exercise agency to anticipate user needs and solve them autonomously. However, evaluating proactivity is challenging; current b…
arXiv:2607.06452v1 Announce Type: cross Abstract: Biomedical question answering requires not only accurate extraction of information from scientific literature but also reliable integration of evidence across multiple documents. This study presents a question-type-specific large …
arXiv:2607.05999v1 Announce Type: new Abstract: LLM-agent simulations make natural-language social scenarios easy to instantiate, but their outputs can be overread as predictions and are often difficult to compare with explicit social dynamics. We present AgoraSim, a hybrid agent…
The optimization of long-horizon agents increasingly relies on reflection-based mechanisms, where a large language model (LLM) acts as an optimizer to diagnose agent failures and improve agent policies. However, real execution traces are difficult to use directly for optimization…
Agent-based modeling (ABM) has the capability to model millions of individuals and their interactions, which is useful for policy making. However, ABMs have traditionally relied on static prior, which prevents the models from adapting to real-time changes. Our research provides a…
Biomedical question answering requires not only accurate extraction of information from scientific literature but also reliable integration of evidence across multiple documents. This study presents a question-type-specific large language model (LLM) framework for BioASQ 14b Task…
LLM-powered data agents are playing an increasingly important role in data-driven decision making. However, existing data agents struggle to generalize to unseen data environments and analytical workflows, especially in heterogeneous enterprise settings. This creates a growing ne…
LLM-powered data agents are playing an increasingly important role in data-driven decision making. However, existing data agents struggle to generalize to unseen data environments and analytical workflows, especially in heterogeneous enterprise settings. This creates a growing ne…
Deliberation plays a crucial role in collaboration; when humans work together, they naturally engage in communication to align information and reach an agreement. In this paper, we investigate deliberative large language model (LLM) agents under partially observable joint decisio…
LLM-agent simulations make natural-language social scenarios easy to instantiate, but their outputs can be overread as predictions and are often difficult to compare with explicit social dynamics. We present AgoraSim, a hybrid agent-based modeling framework for scenario-oriented …
AI agents are increasingly deployed in shared environments where they pursue diverse goals and compete for rewards. This multi-agent competition can lead to behaviors that serve individual gains at collective cost -- for instance, marketing agents may post misleading content as a…
A cheap swarm of unreliable agents can be steered to a correct consensus by a few strong, expensive "oracle" correctors. We ask how much one must spend, and where to place the oracles. We model the swarm as a consensus on a graph in which each oracle pins one node toward the trut…
arXiv:2607.03220v1 Announce Type: cross Abstract: Recent tools such as OpenClaw have extended the capabilities of LLM-based agents from simple dialog-based systems to fully autonomous agents. These systems allow personalization of the agent through modifiable internal files and t…
arXiv:2606.20659v2 Announce Type: replace Abstract: Agent skills encode reusable procedural knowledge for large language model (LLM) agents, and existing benchmarks show that such skills can improve task-level performance. However, a task outcome does not reveal which parts of a …
arXiv cs.AI
TIER_1English(EN)·Xingze Gao, Chuanrui Hu, Hongda Chen, Pengfei Yao, Zhao Wang, Yi Bai, Zhengwei Wu, Yunyun Han, Xiaofeng Cong, Jie Gui, Yafeng Deng, Teng Li·
arXiv:2607.05202v1 Announce Type: new Abstract: Agent self-evolution in long-horizon LLM systems is largely procedural: useful experience is not merely stored information, but reusable procedures for searching, debugging, and verification. Yet current evaluations do not isolate t…
arXiv:2607.04219v1 Announce Type: new Abstract: The integration of AI into Internet of Things (AIoT) systems has gradually transformed them from passive data collection infrastructures into intelligent systems capable of anomaly detection, predictive maintenance, classification, …
arXiv:2607.04419v1 Announce Type: new Abstract: Most agent evaluations collapse a multi-step trace into a final answer, a success flag, or a trajectory-level score. These aggregates obscure the diagnostic question developers need most: which action changed the state in a useful d…
arXiv:2607.04394v1 Announce Type: new Abstract: AI reasoning has become a central focus in contemporary artificial intelligence, largely driven by the success of large language models. However, mathematical research, which is characterized by non-linear derivation paths, rigorous…
arXiv cs.AI
TIER_1English(EN)·Yanbo Wang, Jinhua Hao, Yuze Shi, Kun Yuan, Ming Sun·
arXiv:2607.03441v1 Announce Type: cross Abstract: LLM agents often degrade over long episodes: as trajectories grow, they revisit explored states, repeat failed actions, and lose strategies that previously worked. Test-time training (TTT) offers a way to adapt model weights to th…
arXiv cs.AI
TIER_1English(EN)·Hangoo Kang, Tarun Suresh, Jon Saad-Falcon, Azalia Mirhoseini·
arXiv:2604.05336v2 Announce Type: replace Abstract: Models often fail to complete agentic tasks because they lack core capabilities required by the target environment. However, mainstream approaches for addressing these failures typically either fine-tune directly on target envir…
arXiv:2605.11404v2 Announce Type: replace Abstract: Large language models (LLMs) can simulate human-like reasoning and decision-making in individual agents. LLM-powered multi-agent systems (MAS) combine such agents to simulate population-scale social phenomena such as polarizatio…
arXiv:2512.13956v4 Announce Type: replace-cross Abstract: Cloud-native systems have made operational work both more powerful and harder to automate: incidents unfold across microservices, logs and metrics arrive faster than operators can inspect them, and recovery actions must be…
arXiv cs.AI
TIER_1English(EN)·Yaozu Wu, Wei-Chieh Huang, Jizhou Guo, Dongyuan Li, Renhe Jiang, Henry Peng Zou, Chunyu Miao, Shanghao Li, Weizhi Zhang, WeiWei Ye, Yankai Chen, Meng Zhang, Xue Liu, Philip S. Yu·
arXiv:2607.04329v1 Announce Type: new Abstract: Large language models increasingly operate in settings where humans are active collaborators rather than passive task providers. We introduce HAS-Framework, a graph-based framework that represents humans and LLM-powered agents as fi…
arXiv cs.AI
TIER_1English(EN)·Yifei Shen, Bo Li, Xinjie Zhang·
arXiv:2607.03451v1 Announce Type: cross Abstract: While skill optimization for autonomous agents has gained traction, existing methods rely on complex pipelines. This leaves a fundamental question unaddressed: What constitutes a minimal viable pipeline for skill optimization, whe…
arXiv:2604.00392v2 Announce Type: replace-cross Abstract: Agents that synthesize their own tools ship a second artifact alongside each answer: a software library that future tasks reuse, compose, and depend on. Task completion (TC) certifies the answer; it does not certify the li…
arXiv cs.AI
TIER_1English(EN)·Emanuel Tewolde, Xiao Zhang, David Guzman Piedrahita, Vincent Conitzer, Zhijing Jin·
arXiv:2604.15267v2 Announce Type: replace-cross Abstract: It is increasingly important that LLM agents interact effectively and safely with other goal-pursuing agents, yet, recent works report the opposite trend: LLMs with stronger reasoning capabilities behave _less_ cooperative…
arXiv cs.AI
TIER_1English(EN)·Zongmin Yu, Liu Yang·
arXiv:2605.09018v3 Announce Type: replace-cross Abstract: We introduce Evolutionary Ensemble (EvE), a decentralized framework that organizes existing, highly capable coding agents into a live, co-evolving system for algorithmic discovery. Rather than reinventing the wheel within …
arXiv cs.CL
TIER_1English(EN)·Zichao Li, Gang Wu, Zichao Wang, Ruiyi Zhang, Wanrong Zhu, Ryan A. Rossi, Vlad I Morariu, Jihyung Kil·
arXiv:2607.04235v1 Announce Type: new Abstract: Large language model agents operate in partially observable, long-horizon settings where obtaining supervision remains a major bottleneck. We address this by utilizing a source of supervision overlooked in existing post-training met…
arXiv:2607.02615v1 Announce Type: cross Abstract: Generating structured artifacts with Large Language Models - e.g. database queries, threat framework mappings, entity schemas - is relatively straightforward; however, making them reliable enough for production deployments present…
arXiv:2607.02599v1 Announce Type: cross Abstract: Tool-using LLM agents are usually evaluated by final-answer correctness or LLM judges. Neither captures how an answer was produced. In safety-critical settings, the procedure itself is part of correctness. In this paper, we introd…
arXiv cs.AI
TIER_1English(EN)·Rajesh Kumar, Waqar Ali, Junaid Ahmed, Abdullah Aman Khan, Shaoning Zeng·
arXiv:2607.02520v1 Announce Type: cross Abstract: Automated research agents increasingly generate code, retrieve literature, and draft scientific artifacts, but they often fail to verify whether generated experiments execute correctly or whether cited sources support generated cl…
arXiv cs.AI
TIER_1English(EN)·Minjie Hua, Ning Wang, Peijun Yang, Kai Wang, Shiguo Lian·
arXiv:2607.02518v1 Announce Type: cross Abstract: OpenClaw requests are dominated by long, tool-augmented prefixes, including system prompts, conversation history, and tool outputs fed back into the context window. For this workload, with about 28k-30k input tokens and 500 output…
arXiv cs.CL
TIER_1English(EN)·Shu Yang, Difei Xu, Jiaxin Pei, Di Wang·
arXiv:2607.03730v1 Announce Type: new Abstract: Conversational agents are increasingly embedded in human collaborative work, yet they remain fundamentally passive and reactive: they respond to explicit user requests rather than proactively recognizing moments when a team would be…
arXiv:2607.03780v1 Announce Type: cross Abstract: SkillFab is an agent-native platform for turning missing capabilities into reviewed, reusable Agent Skills. At runtime, agents first search for reusable skills; when no adequate skill exists, the unmet capability becomes a demand-…
arXiv cs.AI
TIER_1English(EN)·Stefan Broecker, Mason del Rosario, Boris Selitser, Thomas Strohmer·
arXiv:2607.04034v1 Announce Type: cross Abstract: The language models that underpin agents have seen a rapid rise in performance on function calling benchmarks. However, the metrics used in the training and evaluation of these models often encourage models to make positive claims…
arXiv cs.AI
TIER_1English(EN)·Jenny Ma, Riya Sahni, Karthik Sreedhar, Lydia B. Chilton·
arXiv:2504.09662v4 Announce Type: replace-cross Abstract: Multi-agent large language model simulations have the potential to model complex human behaviors and interactions. If the mechanics are set up properly, unanticipated and valuable social dynamics can surface. However, it i…
Large language model (LLM) agents are increasingly evaluated on their ability to use tools, plan multi-step tasks, coordinate with other agents, and operate over extended horizons. Reported benchmark gains often obscure recurring failure modes documented across otherwise unrelate…
As LLM agents evolve from single-user assistants into shared organizational infrastructure, new privacy risks emerge: inappropriate information may not only be exposed through outputs for external recipients, but also internally across users through inter-agent messages, shared m…
Agent self-evolution in long-horizon LLM systems is largely procedural: useful experience is not merely stored information, but reusable procedures for searching, debugging, and verification. Yet current evaluations do not isolate this form of transfer. Agent benchmarks test sing…
Agent self-evolution in long-horizon LLM systems is largely procedural: useful experience is not merely stored information, but reusable procedures for searching, debugging, and verification. Yet current evaluations do not isolate this form of transfer. Agent benchmarks test sing…
Software-agent benchmarks usually report whether an agent solves a task, but the agent reaches that outcome through a harness that controls what it sees, which actions it can take, which failures are repaired, which states are verified, and which evidence is logged. We show that …
AI reasoning has become a central focus in contemporary artificial intelligence, largely driven by the success of large language models. However, mathematical research, which is characterized by non-linear derivation paths, rigorous logical requirements, and protracted exploratio…
Large language model agents operate in partially observable, long-horizon settings where obtaining supervision remains a major bottleneck. We address this by utilizing a source of supervision overlooked in existing post-training methods: unintended yet successful goals embedded w…
The integration of AI into Internet of Things (AIoT) systems has gradually transformed them from passive data collection infrastructures into intelligent systems capable of anomaly detection, predictive maintenance, classification, forecasting, and optimization. However, most exi…
In distributed systems, the classical State Machine Replication (SMR) model assumes that correct replicas execute deterministic transitions to yield identical bitwise states. However, the rise of agentic distributed systems -- where autonomous, stochastic, and model-driven agents…
Agentic AI applications form an emerging serving workload in which a request creates a workflow: a directed acyclic graph of LLM and tool calls that exposes per-node model choices and optional quality operators such as verifiers. This workload falls between two existing layers. M…
arXiv:2607.01919v1 Announce Type: new Abstract: Agentic systems enhance their capabilities by invoking external tools and maintaining persistent memory. However, these external dependencies introduce novel attack surfaces. Recent tool and memory poisoning attacks show that malici…
arXiv:2604.13097v3 Announce Type: replace-cross Abstract: Embodied agents increasingly rely on modular capabilities that are installed, upgraded, composed, and governed at runtime, yet the interfaces between these modules are specified only at the level of message types, so integ…
arXiv:2607.01874v1 Announce Type: new Abstract: Skills are becoming a reusable operational layer for LLM agents, encoding SOPs, domain rules, tool workflows, scripts, and validation routines. In realistic skill repositories, overlapping skills make reliable skill-use difficult. F…
arXiv cs.AI
TIER_1English(EN)·Raj Ghugare, Roger Creus Castanyer, Catherine Ji, Kathryn Wantlin, Jin Schofield, Karthik Narasimhan, Benjamin Eysenbach·
arXiv:2510.06288v4 Announce Type: replace Abstract: Today's AI models learn primarily through mimicry and refining, so it is not surprising that they struggle to solve problems beyond the limits set by existing data. To solve novel problems, agents should acquire skills by explor…
arXiv:2607.01942v1 Announce Type: new Abstract: LLM-based agents have shown strong potential for solving complex multi-step tasks, yet existing performance improvements often rely on either scaling to larger backbone models or task-specific fine-tuning. The former incurs substant…
arXiv:2607.01846v1 Announce Type: new Abstract: Domain agents often face noisy business data, uncertain post-training gains, offline/application mismatch, and adapter-release risk. This paper presents CLAP (Closed-Loop Agent Post-training), a closed-loop method that converts busi…
arXiv cs.AI
TIER_1English(EN)·Yueqi Song, Lintang Sutawika, Jiarui Liu, Lindia Tjuatja, Jiayi Geng, Yunze Xiao, Daniel Lee, Aditya Bharat Soni, Vincent Lo, Xiang Yue, Graham Neubig·
arXiv:2607.02032v1 Announce Type: new Abstract: Evaluating LLM agents on benchmarks like SWE-Bench and GAIA can be expensive, time-consuming, and requires complex infrastructure. A single evaluation can cost thousands of dollars and take days to complete. In contrast, non-agentic…
arXiv:2607.01767v1 Announce Type: new Abstract: As agent planning moves from short tool chains toward persistent workflows with thousands or tens of thousands of steps, failures will occur inside large planning graphs rather than in isolated predictions. Replanning the entire gra…
arXiv:2607.01647v1 Announce Type: cross Abstract: Data science aims to derive actionable insights from heterogeneous raw data, unlocking the value of the massive amounts of data generated in modern society. Automating this process is essential to reducing labor-intensive efforts …
arXiv:2607.02141v1 Announce Type: new Abstract: Most LP-from-text benchmarks are static datasets of word problems written and labeled by hand. Once such a dataset is released, its size is fixed, its difficulty is fixed, and every problem can leak into the training data of future …
arXiv:2602.22897v3 Announce Type: replace Abstract: Human intelligence naturally intertwines omni-modal perception -- spanning vision, audio, and language -- with complex reasoning and tool usage to interact with the world. However, current multi-modal LLMs are primarily confined…
arXiv cs.AI
TIER_1English(EN)·Jiacheng Miao, Jonathan K Pritchard, James Zou·
arXiv:2607.01507v1 Announce Type: new Abstract: Empirical research rarely admits a unique analysis. Different analytical choices can lead to different conclusions from the same data, yet these hidden forking paths are difficult to observe. We show that AI agents capture much of t…
arXiv:2607.01465v1 Announce Type: new Abstract: Large language models are trained to predict the next token, not to act inside a specific API. In niche enterprise SaaS workflows -- where success means hitting the right endpoint with the right nested arguments in the right order -…
arXiv cs.AI
TIER_1English(EN)·Zhilin Wang, Han Song, Runzhe Zhan, Jusen Du, Jiacheng Chen, Tianle Li, Qingyu Yin, Yulun Wu, Zhennan Shen, Tong Zhu, Yanshu Li, Guanjie Chen, Derek F. Wong, Yafu Li, Yu Cheng, Yang Yang·
arXiv:2607.02440v1 Announce Type: new Abstract: Autonomous agents are increasingly expected to improve executable policies through feedback, yet existing evaluations often collapse this process into a final score or confound it with open-ended software-engineering progress. We in…
arXiv:2602.19127v2 Announce Type: replace Abstract: With the rapid advancement of agent-based methods in recent years, Agentic RAG has undoubtedly become an important research direction. Multi-hop reasoning, which requires models to engage in deliberate thinking and multi-step in…
A minimal viable pipeline for skill optimization is proposed through Zeroth-Order optimization formalization, eliminating redundancies while maintaining convergence and generalization through trajectory exploration, consensus mining, and validation gating principles.
Autonomous agents are increasingly expected to improve executable policies through feedback, yet existing evaluations often collapse this process into a final score or confound it with open-ended software-engineering progress. We introduce Autonomous Policy Evolution, a controlle…
Most LP-from-text benchmarks are static datasets of word problems written and labeled by hand. Once such a dataset is released, its size is fixed, its difficulty is fixed, and every problem can leak into the training data of future LLMs. We present \textbf{A$^{2}$utoLPBench}, a b…
Evaluating LLM agents on benchmarks like SWE-Bench and GAIA can be expensive, time-consuming, and requires complex infrastructure. A single evaluation can cost thousands of dollars and take days to complete. In contrast, non-agentic LLM benchmarks that test individual capabilitie…
Skills are becoming a reusable operational layer for LLM agents, encoding SOPs, domain rules, tool workflows, scripts, and validation routines. In realistic skill repositories, overlapping skills make reliable skill-use difficult. Final verifier success is too coarse for both eva…
arXiv:2607.00627v1 Announce Type: new Abstract: Large language models (LLMs) are powerful pattern-completion systems, but their default operating mode - predicting the next token from a static context - does not reliably produce persistent, manipulable representations of an exter…
arXiv:2603.23405v2 Announce Type: replace-cross Abstract: Modern Multi-Agent Path Finding (MAPF) algorithms must plan for hundreds to thousands of agents in congested environments within a second, requiring highly efficient algorithms. Priority Inheritance with Backtracking (PIBT…
arXiv:2607.00304v1 Announce Type: cross Abstract: The bias-reliability tradeoff conjectures that LLM evaluation systems are constrained in (gamma, H, CV) space, where evaluator coupling (gamma), strategy diversity (H), and small-sample measurement reliability (CV(N)) cannot be si…
arXiv cs.AI
TIER_1English(EN)·Edward Y. Chang, Longling Geng, Emily J. Chang·
arXiv:2607.00269v1 Announce Type: new Abstract: LLMs, solvers, and agent teams increasingly generate workflow actions, repairs, and plans, but a generated action may be syntactically valid yet stale, infeasible, conflicting, or destructive of the evidence that triggered a repair.…
arXiv:2607.00436v1 Announce Type: new Abstract: Large language model agents are increasingly connected to scientific software, yet it remains unclear when tool access makes scientific computation more reliable rather than merely more complex. We introduce PHREEQC-MCQ-200, a bench…
arXiv cs.AI
TIER_1English(EN)·Roberto Capobianco (Sony AI, Zurich, Switzerland), Harm van Seijen (Sony AI, North America, various locations), Nolan D. Bard (Sony AI, North America, various locations), Neil Burch (Sony AI, North America, various locations), Fatima Davelouis (Sony AI, …·
arXiv:2607.00642v1 Announce Type: new Abstract: Reinforcement learning has proven to be a valuable tool in the creation of advanced AI and robotic systems, contributing to everything from game playing to robotics to foundation models. Through trial-and-error, these AI systems typ…
arXiv:2607.00871v1 Announce Type: new Abstract: Self-evolving agents violate the assumption behind most learning-theoretic guarantees: the data, evaluator, components, and hypothesis space are produced by the policy being updated. We present \textbf{SEA}, an architecture that con…
arXiv:2607.01084v1 Announce Type: new Abstract: While Large Language Model (LLM) agents demonstrate proficiency in static benchmarks, their deployment in real-world scenarios is hindered by the dynamic nature of user queries, tool sets, and interaction dynamics. To address this g…
arXiv cs.AI
TIER_1English(EN)·Xuan Zhao, Andy Chiu, Gengyu Wang·
arXiv:2607.00016v1 Announce Type: cross Abstract: Information localization within massive repositories is a cornerstone of agentic LLM systems. While synthetic data-driven optimization has proven successful in training LLMs, little attention has been paid to optimizing the agent'…
arXiv:2607.00053v1 Announce Type: cross Abstract: Large language models (LLMs) embedded in multi-turn agentic harnesses are reshaping software engineering (SWE), but routing every task to a frontier model is wasteful when many issues admit cheap fixes. Existing LLM routers operat…
arXiv:2607.00272v1 Announce Type: cross Abstract: Traditional robot programming is challenging: it requires orchestrating multimodal perception, managing physical contact dynamics, and handling diverse configurations and execution failures. We introduce ASPIRE (Agentic Skill Prog…
arXiv cs.AI
TIER_1English(EN)·Wayne Chi, Yixiong Fang, Arnav Yayavaram, Siddharth Yayavaram, Seth Karten, Qiuhong Anna Wei, Runkun Chen, Alexander Wang, Valerie Chen, Ameet Talwalkar, Chris Donahue·
arXiv:2602.11103v2 Announce Type: replace Abstract: Despite rapid progress on coding agents, progress on their multimodal counterparts has lagged behind. A key challenge is the scarcity of evaluation testbeds that combine the complexity of software development with the need for d…
arXiv cs.AI
TIER_1English(EN)·Antonis Antoniades, Deepak Nathani, Ritam Saha, Alfonso Amayuelas, Ivan Bercovich, Zhaotian Weng, Vignesh Baskaran, Kunal Bhatia, William Yang Wang·
arXiv:2606.25198v2 Announce Type: replace Abstract: Autonomous AI Research promises to accelerate the scientific progress of machine learning. To realise this goal, current Large Language Model (LLM)-based agents need to go beyond just writing code, to mastering the exploration o…
Data science aims to derive actionable insights from heterogeneous raw data, unlocking the value of the massive amounts of data generated in modern society. Automating this process is essential to reducing labor-intensive efforts for data scientists and enabling scalable data-dri…
Autonomous agents evaluate policy improvement through iterative editing within fixed budgets, revealing that successful policy evolution requires both task-specific mechanisms and feedback-constrained refinement.
PACE is a framework that predicts expensive agentic LLM benchmark performance using a small subset of atomic evaluation instances, achieving high accuracy at a fraction of the cost.
A comprehensive benchmark named AgenticDataBench is introduced to evaluate data agents across diverse domains with fine-grained task annotations and skill-based coverage metrics.
SkillCoach is a self-evolving rubric framework that evaluates and improves agentic skill-use by analyzing skill selection, following, composition, and reflection processes, providing better supervision than outcome-only metrics.
Latent Space (swyx)
TIER_1English(EN)·Richard MacManus·
Introspection co-founder Roland Gavrilescu explains autoresearch, agent “recipes,” self-improving loops, and why humans remain central to the software factory.
While Large Language Model (LLM) agents demonstrate proficiency in static benchmarks, their deployment in real-world scenarios is hindered by the dynamic nature of user queries, tool sets, and interaction dynamics. To address this generalization gap, we formalize OpenAgent (Tool-…
Self-evolving agents violate the assumption behind most learning-theoretic guarantees: the data, evaluator, components, and hypothesis space are produced by the policy being updated. We present \textbf{SEA}, an architecture that confines self-modification to a small steering adap…
Large language models (LLMs) are increasingly used to generate queries, invoke tools, and construct analytical workflows. Although recent advances have substantially improved workflow generation and execution, the semantic information required to operationalize analytical concept…
Reinforcement learning has proven to be a valuable tool in the creation of advanced AI and robotic systems, contributing to everything from game playing to robotics to foundation models. Through trial-and-error, these AI systems typically learn one, near-optimal behavior to solve…
Large language models (LLMs) are powerful pattern-completion systems, but their default operating mode - predicting the next token from a static context - does not reliably produce persistent, manipulable representations of an external world. Many tasks that look like "reasoning"…
Large language model agents are increasingly connected to scientific software, yet it remains unclear when tool access makes scientific computation more reliable rather than merely more complex. We introduce PHREEQC-MCQ-200, a benchmark for evaluating tool-augmented agents on det…
arXiv cs.AI
TIER_1English(EN)·Irena Saracay, Ludwig Schmidt, Carlos Guestrin·
arXiv:2606.30863v1 Announce Type: new Abstract: Agents typically assume an expert user -- one with well-formed preferences about what they want -- and default to clarifying questions whenever the task is underspecified. We argue this assumption is unrealistic. Users often lack th…
arXiv cs.AI
TIER_1English(EN)·Ziyun Zhang, Zezhou Wang, Xiaoyi Zhang, Zongyu Guo, Jiahao Li, Bin Li, Yan Lu·
arXiv:2601.04126v3 Announce Type: replace-cross Abstract: GUI agents that interact with graphical interfaces on behalf of users represent a promising direction for practical AI assistants. However, training such agents is hindered by the scarcity of suitable environments. We pres…
arXiv:2505.21550v2 Announce Type: replace-cross Abstract: Collaborative agentic AI is projected to transform entire industries by enabling AI-powered agents to autonomously perceive, plan, and act within digital environments. Yet, current solutions in this field are all built in …
arXiv cs.AI
TIER_1English(EN)·Zhiqing Zhong, Zhijing Ye, Jiamin Wang, Xiaodong Yu·
arXiv:2605.11030v2 Announce Type: replace-cross Abstract: Closed-loop tool-using agents are increasingly evaluated in executable web, code, and micro-task environments, but benchmark reports often conflate workloads, action-generating drivers, and the evidence admitted for system…
arXiv:2606.31270v1 Announce Type: cross Abstract: Computer-use agents, which leverage multimodal large language models (MLLMs) to operate computers and complete tasks, have attracted significant attention for their utility and versatility. A major challenge in developing these ag…
arXiv cs.AI
TIER_1English(EN)·Apurva Gandhi, Vishwas Suryanarayanan, Raja Hasnain Anwar, Firoz Shaik, Shubhang Desai, Thong Q. Nguyen, Muhammad Taqi Raza, Vishal Chowdhary, Graham Neubig·
arXiv:2606.31154v1 Announce Type: cross Abstract: Creating and editing slides is a rich, multimodal activity that is ubiquitous in professional and educational settings, making it an ideal testbed for real-world computer-use agents. Microsoft PowerPoint is among the most widely a…
arXiv:2606.31564v1 Announce Type: new Abstract: The increasing complexity of agentic tasks has led to rapidly growing trajectory lengths, which poses significant challenges for large language model (LLM) based agents with fixed context windows. Existing context management techniq…
arXiv cs.AI
TIER_1English(EN)·Binjie Zhang, Mike Zheng Shou·
arXiv:2606.31392v1 Announce Type: new Abstract: Tool-augmented vision-language models (VLMs) can solve multimodal, multi-step tasks by calling external tools, yet they remain fragile in practice. Existing works have two common gaps. Supervised fine-tuning (SFT) is built mostly on…
arXiv cs.AI
TIER_1English(EN)·Jingpu Yang, Fengxian Ji, Zhengzhao Lai, Zhexuan Cui, Guangxian Ouyang, Qian Jiang, Fan Zhang, Min Peng, Qianqian Xie, Preslav Nakov, Zhuohan Xie·
arXiv:2606.31045v1 Announce Type: new Abstract: Scientific embodied agents are increasingly capable of carrying out laboratory procedures, but executing these procedures safely in dynamic laboratory environments remains challenging. Current safety approaches often overlook the in…
arXiv cs.AI
TIER_1English(EN)·Yang Zou, Zijian Ding, Yizhou Sun, Jason Cong·
arXiv:2606.30949v1 Announce Type: new Abstract: High-Level Synthesis (HLS) provides a fast path from concepts to silicon, but converting real-world software into synthesizable HLS code remains challenging due to restrictive language support and the gap between software and hardwa…
arXiv:2606.31518v1 Announce Type: new Abstract: Agentic Business Process Management has gained momentum recently. The prospect is that the autonomy of AI agents, i.e., predominantly LLM-based agents, can be balanced with a certain level of robustness, tractability, and traceabili…
arXiv cs.AI
TIER_1English(EN)·Arshia Soltani Moakhar, Iman Gholami, Max Springer, Mahdi JafariRaviz, MohammadTaghi Hajiaghayi·
arXiv:2606.31134v1 Announce Type: new Abstract: While Large Language Models (LLMs) have demonstrated exceptional capabilities in mathematical reasoning, they frequently produce subtle errors that evade human detection. Formal mathematical languages like Lean 4 offer mechanical pr…
arXiv cs.AI
TIER_1English(EN)·Kaiwen Xiong, Haonian Ji, Shi Qiu, Zeyu Zheng, Cihang Xie, Xinyu Ye, Huaxiu Yao·
arXiv:2606.31174v1 Announce Type: new Abstract: Production large language-model (LLM) agents are increasingly deployed not as lone problem-solvers but as managers: a main model creates specialized subagents, delegates work, and orchestrates their parallel, asynchronous returns th…
arXiv:2606.31272v1 Announce Type: cross Abstract: AI agents increasingly acquire and execute skills at runtime: bundles of prompt instructions, executable code, and tool declarations fetched from marketplaces and other agents. Governing them needs a stable notion of skill identit…
arXiv:2606.31229v1 Announce Type: new Abstract: Ideation plays a pivotal role in scientific discovery. Recent LLM, especially AI Scientist systems, show promising potential for automated ideation. However, existing approaches predominantly rely on pre-defined agentic workflows. T…
arXiv:2606.31522v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed as autonomous financial agents initialized with explicit behavioral mandates such as "preserve capital" or "avoid speculative bets" that are meant to govern every decision thr…
arXiv cs.AI
TIER_1English(EN)·Wanli Li, Bince Qu, Bo Pan, Jianyu Zhang, Zheng Liu, Pan Zhang, Wei Chen, Bo Zhang·
arXiv:2604.17931v3 Announce Type: replace Abstract: Reinforcement Learning (RL) has emerged as a powerful training paradigm for LLM-based agents. However, scaling agentic RL for deep research remains constrained by two coupled challenges: hand-crafted synthetic data fails to elic…
arXiv cs.AI
TIER_1English(EN)·Qianchu Liu, Sheng Zhang, Guanghui Qin, Jeya Maria Jose Valanarasu, Maximilian Rokuss, Mingyu Lu, Timothy Ossowski, Juan Manuel Zambrano Chaves, Cliff Wong, Peniel Argaw, Yashna Hasija, Mu Wei, Wen-wai Yim, Qin Liu, Zilin Jing, Jason Entenmann, Naoto Usu…·
arXiv:2606.31179v1 Announce Type: new Abstract: As AI agents become increasingly capable of complex, long-horizon reasoning, rigorous and holistic evaluation is essential for measuring progress toward real-world healthcare applications. We introduce HealthAgentBench, a suite of 5…
arXiv cs.AI
TIER_1English(EN)·Yizhe Liu, Shaolei Zhang, Ju Fan·
arXiv:2606.31423v1 Announce Type: cross Abstract: Real-world data analysis is a multi-step process over heterogeneous inputs rather than merely producing a final answer. A practical system should autonomously organize multi-step workflows, execute generated code in a sandboxed an…
The bias-reliability tradeoff conjectures that LLM evaluation systems are constrained in (gamma, H, CV) space, where evaluator coupling (gamma), strategy diversity (H), and small-sample measurement reliability (CV(N)) cannot be simultaneously optimized at fixed sample size N. Pri…
Traditional robot programming is challenging: it requires orchestrating multimodal perception, managing physical contact dynamics, and handling diverse configurations and execution failures. We introduce ASPIRE (Agentic Skill Programming through Iterative Robot Exploration), a co…
Editor’s note: This post is part of Into the Omniverse, a series focused on how developers, 3D practitioners, and enterprises can transform their workflows using the latest advances in OpenUSD and NVIDIA Omniverse. Vision AI agents are becoming a practical way to automatically tu…
The increasing complexity of agentic tasks has led to rapidly growing trajectory lengths, which poses significant challenges for large language model (LLM) based agents with fixed context windows. Existing context management techniques, such as truncation and summarization, suffe…
Large Language Models (LLMs) are increasingly deployed as autonomous financial agents initialized with explicit behavioral mandates such as "preserve capital" or "avoid speculative bets" that are meant to govern every decision throughout deployment. In practice, however, as marke…
Agentic Business Process Management has gained momentum recently. The prospect is that the autonomy of AI agents, i.e., predominantly LLM-based agents, can be balanced with a certain level of robustness, tractability, and traceability through a combination with process technology…
AI agents increasingly acquire and execute skills at runtime: bundles of prompt instructions, executable code, and tool declarations fetched from marketplaces and other agents. Governing them needs a stable notion of skill identity, yet cryptographic hashing is engineered to dest…
Computer-use agents, which leverage multimodal large language models (MLLMs) to operate computers and complete tasks, have attracted significant attention for their utility and versatility. A major challenge in developing these agents is collecting large-scale, high-quality traje…
As AI agents become increasingly capable of complex, long-horizon reasoning, rigorous and holistic evaluation is essential for measuring progress toward real-world healthcare applications. We introduce HealthAgentBench, a suite of 54 agentic healthcare tasks across 7 categories e…
arXiv cs.AI
TIER_1English(EN)·Michael Nguyen, Quoc Nguyen, Paul Vuong·
arXiv:2606.28374v1 Announce Type: new Abstract: LLM agents are increasingly improved without weight updates by evolving a natural-language artifact, such as reflections, workflows, playbooks, cheatsheets, or optimized prompts, that conditions a frozen policy. Such methods are typ…
arXiv cs.AI
TIER_1English(EN)·Gang Liao, Yujia He, Abdullah Ozturk, Zhouyang Li, Ying Wang, Zhitong Guo, Hongsen Qin, Yaobin Qin, Tao Yang, Zewei Jiang, Dianshi Li, Jort Gemmeke, Jiangyuan Li, Liyuan Li, Nathan Yan, Masha Basmanova, Uladzimir Pashkevich, Matt Steiner, Pedro Pedreira,…·
arXiv:2606.29823v1 Announce Type: cross Abstract: The database community has repeatedly advanced the state of the art by recognizing that new workloads demand new system architectures. We argue that long-horizon agentic tasks -- code generation, scientific discovery, hardware des…
arXiv cs.AI
TIER_1English(EN)·Yijia Fan, Zonglin Di, Zimo Wen, Yifan Yang, Mingxi Cheng, Qi Dai, Bei Liu, Kai Qiu, Yue Dong, Ji Li, Chong Luo·
arXiv:2606.29538v1 Announce Type: cross Abstract: Skills are a useful abstraction for software agents, turning human and agent experience into reusable procedural knowledge. Yet existing skill libraries are mostly hand-written, text-centric, or derived from agent traces, leaving …
arXiv cs.AI
TIER_1English(EN)·Ruiyu Zhang, Lin Nie, Xin Zhao·
arXiv:2606.29038v1 Announce Type: cross Abstract: Metric aggregation divergence (MAD) is the silent inconsistency that arises when distinct pipeline stages in an agent-based model coupled with a multi-objective evolutionary algorithm (ABM+MOEA) independently re-implement how an o…
arXiv:2606.28841v1 Announce Type: cross Abstract: Large language models are increasingly capable of mathematical reasoning, but the proofs they generate are often unreliable and hard to verify. Interactive theorem provers such as Lean 4 address this by accepting only kernel-check…
arXiv cs.AI
TIER_1English(EN)·Yeqi Huang, Yanwei Ye, Guomin Chen, Wenhao Su, Bin Gong, Jialian Li, Zhan Lu, Yangshen Deng, Xuan Sun, Le Xu, Luo Mai·
arXiv:2606.21401v2 Announce Type: replace-cross Abstract: Agentic AI applications compose multiple model calls and tool executions, creating new scheduling challenges for GPU-CPU clusters. Their inference time and model-call structure often depend on prompt semantics, making conv…
arXiv:2606.29863v1 Announce Type: new Abstract: Agentic search equips large language models with dynamic retrieval abilities, but existing reinforcement learning methods remain limited by reward sparsity in knowledge boundary calibration -- deciding when to trust parametric memor…
arXiv:2606.30616v1 Announce Type: new Abstract: We introduce Agents-A1, a 35B Mixture-of-Experts Agentic Model that reaches trillion-parameter-level performance by scaling the agent horizon. We investigate agent-horizon scaling from two perspectives: scaling long-horizon trajecto…
arXiv cs.CL
TIER_1English(EN)·Dilxat Muhtar, Jiashun Liu, Wei Gao, Weixun Wang, Shaopan Xiong, Ju Huang, Siran Yang, Wenbo Su, Jiamang Wang, Ling Pan, Bo Zheng·
arXiv:2603.17621v2 Announce Type: replace-cross Abstract: Reinforcement Learning (RL) has emerged as a powerful paradigm for training LLM-based agents, yet remains limited by low sample efficiency, stemming not only from sparse outcome feedback but also from the agent's inability…
arXiv:2606.28715v1 Announce Type: cross Abstract: While AI development and evaluation for Southeast Asia (SEA) has grown rapidly, agent capabilities in regional languages are still poorly understood despite its importance to sovereign AI. To fill this gap, we introduce SEATauBenc…
arXiv:2606.29537v1 Announce Type: new Abstract: Existing computer-use benchmarks fail to capture the realism, complexity, and long-horizon demands of real-world computer use, limiting their ability to reveal the limitations of frontier agents. We introduce OSWorld 2.0, a benchmar…
arXiv:2606.29932v1 Announce Type: new Abstract: Long-horizon strategic planning in complex strategy games demands concurrent reasoning across multiple decision domains under imperfect information and sparse reward. Existing LLM-based agents suffer from three systematic failures: …
arXiv:2606.29502v1 Announce Type: new Abstract: Skill memories can improve agentic reinforcement learning by reusing past experience as textual guidance, but retrieved skills are not oracular: they may help in one state while misleading the same policy in another. This makes the …
arXiv:2606.29194v1 Announce Type: new Abstract: Automated alpha mining holds the scoring function fixed and varies the search algorithm over it. A search that converges against a fixed scorer overfits whatever the scorer cannot penalize, a primary cause of the out-of-sample gener…
arXiv:2606.28480v1 Announce Type: cross Abstract: As large language models and harness frameworks continue to advance, agents operating in terminals are increasingly capable of performing a broader range of general computer-use tasks beyond coding. However, existing benchmarks do…
arXiv:2606.30531v1 Announce Type: new Abstract: Tool-augmented language-model agents are often evaluated by whether they select the correct tool, produce valid API arguments, and complete the requested task. However, an agent may choose the right tool and still act on the wrong e…
arXiv:2606.29472v1 Announce Type: new Abstract: SWE-agent established the action interface as an underexplored design axis for software-engineering agents; we make the analogous case for the observation interface in computer-use (CU) agents. Current CU agents, closed and open-sou…
arXiv:2606.29315v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to take actions in the real world and support human decision-making, yet most agents rely on parametric knowledge, fixed post-training data, retrieval, or search. This paradigm brea…
arXiv cs.AI
TIER_1English(EN)·Zihan Guo, Zeyi Chen, Zhiyu Chen, Zicai Cui, Shuai Shao, Bo Huang, Zhi Han, Yuanyi Song, Yuan Yuan, Chenxi Zeng, Xiaohang Nie, Zhengxi Yu, Hanwen Zhu, Junwei Liao, Ming Zhou, Yang Li, Yuanjian Zhou, Weinan Zhang·
arXiv:2606.30246v1 Announce Type: new Abstract: Existing autonomous research agents can support parts of the research process, but most systems still treat research as either an isolated assistant task or a closed workflow. Therefore, autonomous science needs a collaboration infr…
arXiv cs.AI
TIER_1English(EN)·Jian Zhu, Yuzheng Zhang, Zeyao Ma, Bohan Zhang, Armin Schoepf, Daniel Woloch, Peter Yiliu Wang, Guangyu Robert Yang, Samuel Jacob, Siddharth Nagisetty, Abhiram Chundru, Jean Lin, Spencer Mateega, Jing Zhang·
arXiv:2606.29955v1 Announce Type: cross Abstract: Spreadsheets are widely used for business analysis, financial modeling, reporting, and decision-making. However, most existing spreadsheet benchmarks evaluate isolated operations such as single-formula generation or local cell edi…
arXiv:2606.29116v1 Announce Type: new Abstract: Large Language Models (LLMs) are rapidly being adopted in low-code and no-code automation platforms, where non-expert users design workflows that combine natural language understanding with external services and APIs. LLM agents are…
arXiv:2606.30111v1 Announce Type: cross Abstract: Embodied agents are typically built as hand-designed compositions of perception, memory, planning, and action modules. This modularity exposes a large architectural design space, but current systems still rely on researcher intuit…
arXiv:2602.11351v2 Announce Type: replace Abstract: Proactive large language model (LLM) agents aim to actively plan, query, and interact over multiple turns, enabling efficient task completion beyond passive instruction following and making them essential for real-world, user-ce…
arXiv:2512.16733v3 Announce Type: replace Abstract: Black-box AI (BBAI) systems, including foundation-model agents, are increasingly used for sequential decision making. Safe deployment requires methods for characterizing what such systems can do, when they can do it, and what ou…
arXiv:2512.15044v2 Announce Type: replace Abstract: Integrated sensing and communication (ISAC) has emerged as a key development direction in the sixth-generation (6G) era, which provides essential support for the collaborative sensing and communication of future intelligent netw…
HealthAgentBench presents a comprehensive evaluation framework with 54 healthcare tasks across 7 categories to assess AI agents' capabilities in complex clinical workflows, revealing significant challenges in medical imaging and compositional reasoning while showing promise in EH…
ASPIRE is a continual learning system that autonomously develops and refines robot control programs through iterative exploration, achieving superior performance and zero-shot generalization in manipulation and household tasks while enabling sim-to-real transfer.
Agentic artificial intelligence is increasingly deployed not as a single assistant but as a collective of planners, solvers, reviewers, memory managers, tool users, and orchestrators. These systems are entering organisational workflows under familiar labels such as teams, manager…
We introduce Agents-A1, a 35B Mixture-of-Experts Agentic Model that reaches trillion-parameter-level performance by scaling the agent horizon. We investigate agent-horizon scaling from two perspectives: scaling long-horizon trajectories and scaling heterogeneous agent abilities. …
Tool-augmented language-model agents are often evaluated by whether they select the correct tool, produce valid API arguments, and complete the requested task. However, an agent may choose the right tool and still act on the wrong external entity. For example, a request to "email…
Agentic multimodal models perform diverse operations on an image via code and reason over the returned view, an effective paradigm for fine-grained visual question answering. However, code operations can be useful, redundant, or misleading. Outcome-only rewards cannot precisely d…
Existing autonomous research agents can support parts of the research process, but most systems still treat research as either an isolated assistant task or a closed workflow. Therefore, autonomous science needs a collaboration infrastructure that coordinates projects, agents, an…
Embodied agents are typically built as hand-designed compositions of perception, memory, planning, and action modules. This modularity exposes a large architectural design space, but current systems still rely on researcher intuition to choose where information is stored, how obs…
Spreadsheets are widely used for business analysis, financial modeling, reporting, and decision-making. However, most existing spreadsheet benchmarks evaluate isolated operations such as single-formula generation or local cell edits, and therefore fail to capture end-to-end workf…
Agentic search equips large language models with dynamic retrieval abilities, but existing reinforcement learning methods remain limited by reward sparsity in knowledge boundary calibration -- deciding when to trust parametric memory, when to rely on retrieved evidence, and when …
arXiv cs.MA (Multiagent)
TIER_1English(EN)·Daniel J. Abadi·
The database community has repeatedly advanced the state of the art by recognizing that new workloads demand new system architectures. We argue that long-horizon agentic tasks -- code generation, scientific discovery, hardware design -- are such a workload. These agents explore: …
arXiv:2606.28279v1 Announce Type: cross Abstract: We present HORIZON, a self-evolving agent framework that treats hardware design as repository-level code evolution. A Markdown harness is compiled into a project pack containing domain knowledge, an executable evaluator, an accept…
Agents-A1, a 35B Mixture-of-Experts Agentic Model, achieves trillion-parameter-level performance through long-horizon trajectory scaling and heterogeneous agent ability scaling via a three-stage training approach involving supervised fine-tuning, domain-level teacher models, and …
OSWorld 2.0 presents a comprehensive benchmark for evaluating computer-use agents through complex, real-world workflows that reveal current limitations in agent reasoning and task completion.
HExA enables large language models to improve through active experimentation and skill learning in novel domains without requiring training or external supervision.
Metric aggregation divergence (MAD) is the silent inconsistency that arises when distinct pipeline stages in an agent-based model coupled with a multi-objective evolutionary algorithm (ABM+MOEA) independently re-implement how an outcome metric is extracted from simulation traject…
Recent search agents for multi-hop reasoning often fail by either retrieving incomplete evidence or reasoning over irrelevant portions of the retrieved content, leading to a retrieval-reasoning boundary shift. We propose R$^2$-Searcher, a novel framework that explicitly explores …
We present HORIZON, a self-evolving agent framework that treats hardware design as repository-level code evolution. A Markdown harness is compiled into a project pack containing domain knowledge, an executable evaluator, an acceptance predicate, and a git/runtime policy; a hands-…
Multi-agent systems (MAS) built on large language models (LLMs) provide a promising framework for solving complex tasks through role specialization and structured interaction. However, their performance is often limited by miscoordination and, more fundamentally, the lack of fine…
arXiv:2606.27045v1 Announce Type: cross Abstract: AI coding agents dramatically accelerate implementation speed but introduce two structural failure modes that existing spec-driven approaches do not fully solve: (1) context explosion -- the agent must reason over an entire reposi…
arXiv cs.AI
TIER_1Norsk(NO)·Kaicheng Zhang, Wen Ge, Lei Jiang, Weixin Yang, Jordan Langham-Lopez, Jialin Yu, Lukasz Szpruch, Hao Ni·
arXiv:2606.26350v1 Announce Type: new Abstract: Although large language model agents are increasingly applied to quantitative-finance workflows, their evaluation remains fragmented across isolated tasks, while the financial relevance of benchmark tasks is often overlooked. Yet fi…
arXiv:2606.26758v1 Announce Type: new Abstract: High-performance GPU kernels are critical for reducing the exponentially growing computational costs of large language models (LLMs), but their development heavily relies on manual tuning by domain experts. While recent advances in …
arXiv:2606.26203v1 Announce Type: new Abstract: As AI agent protocols proliferate, the governance structures shaping their interoperability standards remain empirically underexamined. We introduce an LLM-powered comparative pipeline for large-scale governance discourse analysis, …
arXiv:2606.27005v1 Announce Type: new Abstract: Modern AI systems are increasingly deployed under non-stationary computational, demographic, and operational conditions in which static resource allocation strategies degrade both predictive performance and human-centric properties …
arXiv cs.AI
TIER_1English(EN)·Alex Iacob, Andrej Jovanovi\'c, William F. Shen, Daniel Burkhardt, Meghdad Kurmanji, Nurbek Tastan, Lorenzo Sani, Niccol\`o Alberto Elia Venanzi, Ambroise Odonnat, Zeyu Cao, Bill Marino, Xinchi Qiu, Nicholas D. Lane·
arXiv:2606.26294v1 Announce Type: cross Abstract: Self-improving agents are state-of-the-art (SOTA) on agentic coding benchmarks and have recently been extended to general domains. However, their search methods generally assume a stationary evaluation criterion: a fixed verifier,…
Gradient-Based Connections enables fine-grained attribution and optimization in multi-agent systems by modeling agent interactions as a computational graph and using gradient-based weights to identify error sources at the token level.
TUA-Bench presents a comprehensive benchmark for evaluating general-purpose terminal-use agents across diverse digital activities and specialized workflows, revealing significant performance gaps among current frontier agents.
AI coding agents dramatically accelerate implementation speed but introduce two structural failure modes that existing spec-driven approaches do not fully solve: (1) context explosion -- the agent must reason over an entire repository at once, degrading output quality as the cont…
Modern AI systems are increasingly deployed under non-stationary computational, demographic, and operational conditions in which static resource allocation strategies degrade both predictive performance and human-centric properties such as fairness and explainability. This paper …
High-performance GPU kernels are critical for reducing the exponentially growing computational costs of large language models (LLMs), but their development heavily relies on manual tuning by domain experts. While recent advances in LLM-based approaches show promise for automating…
arXiv cs.CL
TIER_1English(EN)·Ilia Kulikov, Chenxi Whitehouse, Tianhao Wu, Yixin Nie, Swarnadeep Saha, Eryk Helenowski, Weizhe Yuan, Olga Golovneva, Jack Lanchantin, Yoram Bachrach, Jakob Foerster, Xian Li, Han Fang, Sainbayar Sukhbaatar, Jason Weston·
arXiv:2606.25996v1 Announce Type: cross Abstract: We introduce Autodata, a general method that enables AI agents to act as data scientists who build high quality training and evaluation data. We show how to train (meta-optimize) such a data scientist agent, so that it learns to c…
arXiv:2606.26057v1 Announce Type: cross Abstract: AI agents are granted access to tools, APIs, and other infrastructure, making them active principals in those systems. The dominant approach places controls inside the agent's own runtime: system prompts, output filters, and guard…
arXiv:2606.24937v1 Announce Type: cross Abstract: The Hitchhiker's Guide to Agentic AI is a comprehensive practitioner's reference for building autonomous AI systems. The book covers the full stack from first principles to production deployment, organized around a central thesis:…
arXiv cs.CL
TIER_1English(EN)·Yang Tian, Zhengpeng Shi, Bo Zhao·
arXiv:2606.25819v1 Announce Type: new Abstract: Large language models are increasingly deployed as agents that solve tasks by interacting with external tool environments. Although recent tool-use benchmarks increasingly cover complex task settings, they still largely assume clean…
arXiv:2606.25656v1 Announce Type: new Abstract: As advanced RAG variants like GraphRAG and Agentic RAG emerge, one leading question is when and how to use them. Here, we introduce a framework for different RAG scenarios evaluation and comparison on semi-structured knowledge bases…
A web-based benchmark evaluates agent generalization across challenging scenarios, revealing significant gaps between current agentic systems and human performance in temporal perception, graphical understanding, and 3D reasoning.
arXiv cs.MA (Multiagent)
TIER_1English(EN)·Nicholas D. Lane·
Self-improving agents are state-of-the-art (SOTA) on agentic coding benchmarks and have recently been extended to general domains. However, their search methods generally assume a stationary evaluation criterion: a fixed verifier, benchmark, or labeled dataset that remains valid …
arXiv cs.MA (Multiagent)
TIER_1English(EN)·Nicholas D. Lane·
Self-improving agents are state-of-the-art (SOTA) on agentic coding benchmarks and have recently been extended to general domains. However, their search methods generally assume a stationary evaluation criterion: a fixed verifier, benchmark, or labeled dataset that remains valid …
AI agents are granted access to tools, APIs, and other infrastructure, making them active principals in those systems. The dominant approach places controls inside the agent's own runtime: system prompts, output filters, and guardrail libraries. Any control in the agent's address…
As AI agent protocols proliferate, the governance structures shaping their interoperability standards remain empirically underexamined. We introduce an LLM-powered comparative pipeline for large-scale governance discourse analysis, integrating automated annotation, neural topic m…
We introduce Autodata, a general method that enables AI agents to act as data scientists who build high quality training and evaluation data. We show how to train (meta-optimize) such a data scientist agent, so that it learns to create even stronger data. We describe the overall …
Large language models are turning from isolated predictors into agentic systems: they call tools, retrieve evidence, obey environment constraints, use verifiers, and complete tasks through search and multi-turn interaction. We adopts an analytical viewpoint based on "compression …
Large language models are increasingly deployed as agents that solve tasks by interacting with external tool environments. Although recent tool-use benchmarks increasingly cover complex task settings, they still largely assume clean, stable, and trustworthy tool environments, lea…
As advanced RAG variants like GraphRAG and Agentic RAG emerge, one leading question is when and how to use them. Here, we introduce a framework for different RAG scenarios evaluation and comparison on semi-structured knowledge bases, including regular RAG, GraphRAG, Modular RAG a…
arXiv:2606.21894v2 Announce Type: replace-cross Abstract: As coding agents are rapidly changing software engineering, a natural question is: what are the core skills needed by future software engineers? To identify where software engineering is headed and thus what skills will be…
arXiv:2606.24722v1 Announce Type: new Abstract: Frontier AI training is increasingly shaped by access to dense, centrally controlled accelerator clusters. This creates a structural advantage for hyperscalers and large centralized laboratories, and makes open or independent AI eff…
arXiv cs.AI
TIER_1English(EN)·Yarin Yerushalmi Levi, Roy Betser, Amit Giloni, Lidor Erez, Itay Gershon, Oren Rachmil, Sindhu Padakandla, Roman Vainshtein·
arXiv:2606.23927v1 Announce Type: new Abstract: Agentic AI systems powered by large language models (LLMs) are rapidly evolving into autonomous decision-making systems, exposing attack vectors beyond those of traditional LLM vulnerabilities. Existing security evaluations are ofte…
arXiv cs.AI
TIER_1English(EN)·Adhitya Charan, Adwaid Suresh, Anuj Kumar, Aparna A, Dhanakumar K, Dharun M S, Dinesh G, Goutham Kumar Reddy K, Harshini V M, Jenifa D, Jona Delcy C A, Kathirvel S, Killi Uma Maheswara Rao, Kiruthik Kanna M, Kurra Vishnu Sai, Madhumithaa G K, Navin Kumar…·
arXiv:2606.24780v1 Announce Type: new Abstract: Progress in deep learning is, at scale, more a matter of systems engineering than of modelling: the behaviour of a model in training (its throughput, its memory footprint, and the numerical fidelity of the result) is determined less…
arXiv cs.AI
TIER_1English(EN)·Negin Raoof, Richard Zhuang, Marianna Nezhurina, Etash Guha, Atula Tejaswi, Ryan Marten, Charlie F. Ruan, Tyler Griggs, Alexander Glenn Shaw, Hritik Bansal, E. Kelly Buchanan, Artem Gazizov, Reinhard Heckel, Chinmay Hegde, Sankalp Jajee, Daanish Khazi, E…·
arXiv:2606.24855v1 Announce Type: new Abstract: Agentic language models dramatically expand the applications of AI yet little is publicly known about how to curate training data for broadly capable agents. Existing open efforts such as SWE-Smith, SERA, and Nemotron-Terminal typic…
arXiv:2606.24842v1 Announce Type: new Abstract: In the big-world regime, agents cannot be universally capable and their ability is inevitably specialized across a world model in pieces. Consequently, standard uniform guarantees fail to distinguish between the understanding of cri…
Autodata enables AI agents to function as data scientists who create high-quality training data through meta-optimization, demonstrating improved performance across multiple task domains.
arXiv cs.MA (Multiagent)
TIER_1English(EN)·Amit K. Chopra·
Realizing a multiagent system involves implementing member agents who interact based on a protocol while making decisions in a decentralized manner. Current programming models for agents offer poor abstractions for decision making and fail to adequately bridge an agent's internal…
Agentic language models dramatically expand the applications of AI yet little is publicly known about how to curate training data for broadly capable agents. Existing open efforts such as SWE-Smith, SERA, and Nemotron-Terminal typically target a single benchmark, leaving open the…
In the big-world regime, agents cannot be universally capable and their ability is inevitably specialized across a world model in pieces. Consequently, standard uniform guarantees fail to distinguish between the understanding of critical bottlenecks and irrelevant failures. We fi…
Progress in deep learning is, at scale, more a matter of systems engineering than of modelling: the behaviour of a model in training (its throughput, its memory footprint, and the numerical fidelity of the result) is determined less by the architecture itself than by how that arc…
Frontier AI training is increasingly shaped by access to dense, centrally controlled accelerator clusters. This creates a structural advantage for hyperscalers and large centralized laboratories, and makes open or independent AI efforts depend on scarce capital, privileged infras…
SkillHone enables continuous evolution of agent skills by maintaining persistent decision histories and incorporating practice feedback for improved performance across research and tool-mediated analysis tasks.
An open-source data curation pipeline for training agentic language models is presented, demonstrating superior performance through systematic experimentation and scalable training data.
The Hitchhiker's Guide to Agentic AI is a comprehensive practitioner's reference for building autonomous AI systems. The book covers the full stack from first principles to production deployment, organized around a central thesis: building great agentic systems requires understan…
Import AI (Jack Clark)
TIER_1English(EN)·Jack Clark·
The book provides a comprehensive guide to building autonomous AI systems, covering foundational elements like transformer architecture and training methods, along with advanced topics such as reinforcement learning, agent architectures, and production deployment.
Large language models have become capable reasoners and tool users that write and run code and search the literature, which makes automating the research process itself a realistic goal. We present PAPERCLAW, a harnessed multi-agent system that carries a project autonomously, fro…
Computer use agents (CUAs) have advanced rapidly in desktop automation, and a growing number of users deploy CUAs such as OpenClaw on Mac Mini for always-on automation. However, existing benchmarks, including those for macOS, evaluate agents without framework augmentation and rel…
Long-chain agent execution fails exponentially in environments designed for human tolerance: with per-step determinism $δ< 1$, $k$-step chain success degrades as $δ^k$. The AGI-to-ASI scaling debate (Genewein et al., 2026) has so far framed progress as a race between compute grow…
Long-chain agent execution fails exponentially in environments designed for human tolerance: with per-step determinism $δ< 1$, $k$-step chain success degrades as $δ^k$. The AGI-to-ASI scaling debate (Genewein et al., 2026) has so far framed progress as a race between compute grow…
Grounded word learning experiments using visual embeddings and lexical learners reveal that perceptual distance, rather than semantic relatedness, determines acquisition success, with distinct patterns in naming and retrieval performance.
We introduce CFAgentBench, a reproducible, self-hostable environment and benchmark for autonomous construction-finance agents: a CFO/controller-class agent operating across the real software stack a US construction finance team runs - ERP, project management, email, documents, pa…
AI agents in long-context applications drift from their specified identity. Current methods detect this only after qualitative degradation is visible. We present a geometric framework for measuring identity structure using $\sqrt{\mathrm{JSD}}$ metric spaces and magnitude homolog…
Industrial automation is being transformed by digitalization and the increasing use of cyber-physical systems. Modern production environments require greater adaptability, faster reconfiguration, and more intuitive human-machine interaction. However, traditional rule-based system…
arXiv:2606.18532v1 Announce Type: cross Abstract: AI systems are increasingly evaluated in bounded environments that combine isolation, simulation, instrumentation, supervision, and evidence capture. For physical AI, AIoT, and cyber-physical systems, this shift is not a matter of…
arXiv cs.LG
TIER_1English(EN)·Blaise Ag\"uera y Arcas, Travis Beals, Maria Biggs, Jessica V. Bloom, Thomas Fischbacher, Konstantin Gromov, Urs K\"oster, Rishiraj Pravahan, James Manyika·
arXiv:2511.19468v2 Announce Type: replace-cross Abstract: If AI is a foundational general-purpose technology, we should anticipate that demand for AI compute -- and energy -- will continue to grow. The Sun is by far the largest energy source in our solar system, and thus it warra…
arXiv cs.AI
TIER_1English(EN)·Richard A. Fabes (Arizona State University)·
arXiv:2606.18265v1 Announce Type: cross Abstract: As human relationships with artificial intelligence systems become increasingly frequent and sustained, existing language and theory fail to accurately capture the nature of these affiliations. Common descriptors such as mutual un…
arXiv cs.LG
TIER_1English(EN)·Jeffery Opoku, David Banahene·
arXiv:2606.18467v1 Announce Type: cross Abstract: Modern AI agents retrieve documents, call tools, check intermediate information, and then produce a final answer or action. This creates a risk-control problem that is not visible from the final answer alone. A final response may …
Large Language Model (LLM)-based automatic Multi-Agent Systems (MAS) generation has become a crucial frontier for tackling complex tasks. However, existing methods face a dilemma between model capability and experience retention. Inference-time MAS leverages frozen frontier LLMs …
arXiv:2606.17174v1 Announce Type: new Abstract: While parasocial interactions (PSIs) and parasocial relationships (PSRs) have been studied in conventional media settings, we investigate whether PSI- (colloquial) relational cues also exist in online communities where both sides ar…
arXiv:2606.17574v1 Announce Type: new Abstract: Evaluating a Physical AI stack spans operators that differ by more than three orders of magnitude -- from a single foundation-model decoding step to thousands of physics ticks of whole-body control -- varying orthogonally in modalit…
arXiv cs.AI
TIER_1English(EN)·Jasmine Brazilek, Oliver Tulio, Joel Christoph, Miles Tidmarsh, Carol Kline, Arturs Kanepajs·
arXiv:2606.18142v1 Announce Type: new Abstract: AI agents are moving from advisors to actors, booking travel, planning menus, and running procurement on behalf of users. Existing benchmarks for AI and animal welfare evaluate model text responses to question-answer prompts, leavin…
WorldLines benchmark evaluates long-term memory in embodied agents through household scenarios, while ObsMem framework addresses challenges in partial observability and memory translation for decision-making.
AI agents are moving from advisors to actors, booking travel, planning menus, and running procurement on behalf of users. Existing benchmarks for AI and animal welfare evaluate model text responses to question-answer prompts, leaving open whether the welfare reasoning surfaced in…
Modern AI systems increasingly solve a task not with a single model call but with several imperfect agents working together: some propose pieces of a solution, others verify them, and the results are combined. These systems often outperform any single model, yet it is rarely clea…
arXiv cs.CL
TIER_1English(EN)·Aman Gupta, Kevin Rossell, Edesio Alcoba\c{c}a, Jose Chrystian Lima Pacheco, Carolina Baptista de Lima, Shao Tang, Luiz Paulo Rabachini, Luis Moneda, Herbert Fei, Daniel Silva, Rohan Ramanath·
arXiv:2606.08867v2 Announce Type: replace Abstract: The rapid rise in LLM capabilities has made AI agents increasingly viable across a broad range of tasks. Among the most promising applications is building production-ready customer-facing agents, a challenge that demands coordin…
arXiv cs.AI
TIER_1English(EN)·Yutaro Yamada, Robert Tjarko Lange, Cong Lu, Chris Lu, Shengran Hu, Jakob Foerster, David Ha, Jeff Clune·
arXiv:2606.15497v1 Announce Type: new Abstract: The automation of science is a long-standing ambition in the field of AI. While the community has made significant progress in automating individual components of the scientific process, a system that autonomously navigates the enti…
arXiv:2606.15834v1 Announce Type: new Abstract: The computer systems community has recently seen growing interest in AI-driven system evolution, where AI agents iteratively rewrite systems. Frameworks such as AdaEvolve and Engram report 12-60% score improvements over human-design…
arXiv:2606.14769v1 Announce Type: cross Abstract: Agentic AI systems are increasingly being deployed as productive resources in organizational workflows, yet existing evaluation methods primarily measure isolated technical performance rather than economic contribution. This paper…
arXiv:2606.16059v1 Announce Type: cross Abstract: For thirty years, quantitative finance has paid a costly two-language tax: models researched in Python are rewritten in C++ for production, often introducing numerical discrepancies. GPU-accelerated deep learning exacerbates this …
arXiv cs.AI
TIER_1English(EN)·Christopner Koch, Joshua A. Wellbrock·
arXiv:2606.16649v1 Announce Type: new Abstract: Agentic AI marks a new phase of enterprise automation. Unlike traditional automation or conversational AI, agentic systems can interpret goals, plan multi step tasks, access tools, interact with enterprise systems, and execute workf…
arXiv cs.AI
TIER_1English(EN)·Sribalaji C. Anand, George J. Pappas·
arXiv:2606.15024v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly deployed in multi-agent systems where they must coordinate and agree on shared decisions. We ask whether classical resilient consensus theory, developed for deterministic agents, …
arXiv cs.AI
TIER_1English(EN)·Ang Li, Ben Liu, Bin Han, Bin Hu, Bin Jing, Binbin Hu, Bing Li, Cai Chen, Caizhi Tang, Changxin Tian, Chao Huang, Chao Zhang, Chen Liang, Chen Qian, Chengfu Tang, Chengyao Wen, Chilin Fu, Chunwei Wu, Cong Zhang, Cunyin Peng, Daixin Wang, Dalong Zhang, De…·
arXiv:2606.15079v1 Announce Type: cross Abstract: Efficient and scalable agentic intelligence requires models that can deliver both low-latency responses and strong reasoning capabilities while remaining practical to train, serve, and deploy. In this report, we present Ling-2.6 a…
arXiv:2606.15954v1 Announce Type: cross Abstract: Agentic AI systems act through tools and sub-agents, yet the controls meant to bound their financial and environmental cost still sit on dashboards evaluated beside or after execution. Green SARC applies the SARC governance-by-arc…
arXiv cs.AI
TIER_1English(EN)·Yegon Kim, Juho Lee·
arXiv:2602.23242v3 Announce Type: replace Abstract: In general reinforcement learning, all established optimal agents, including AIXI, are model-based, explicitly maintaining and using environment models. This paper introduces Universal AI with Q-Induction (AIQI), the first model…
arXiv:2606.16319v1 Announce Type: new Abstract: Modern AI systems exhibit structural failures that capability scaling alone does not reliably fix: they optimize under-specified objectives with no architectural mechanism to question whether the objective should be optimized at all…
arXiv:2604.11364v2 Announce Type: replace Abstract: The two most influential cognitive architecture frameworks for AI agents, CoALA [21] and JEPA [12], both lack an explicit Knowledge layer with its own persistence semantics. This gap produces a category error: systems apply cogn…
arXiv:2606.16533v1 Announce Type: new Abstract: World models are transitioning from passive visual generators to foundational, operational infrastructure for Physical AI: they must natively acquire world knowledge from heterogeneous experience, maintain persistent states over lon…
Kairos is a native world model framework that learns from diverse experiences, maintains persistent states through hybrid temporal attention, and supports efficient deployment for physical AI applications.
While parasocial interactions (PSIs) and parasocial relationships (PSRs) have been studied in conventional media settings, we investigate whether PSI- (colloquial) relational cues also exist in online communities where both sides are autonomous AI agents. We analyze 4,434 posts a…
AI agents must be evaluated as behavioral systems, not as isolated response generators. They reason across turns, call tools, preserve context, follow policies, and act under uncertainty. Existing methods provide useful but fragmented signals: benchmarks measure fixed capabilitie…
arXiv cs.AI
TIER_1English(EN)·Joshua A. Wellbrock·
Agentic AI marks a new phase of enterprise automation. Unlike traditional automation or conversational AI, agentic systems can interpret goals, plan multi step tasks, access tools, interact with enterprise systems, and execute workflows with varying degrees of autonomy. For small…
arXiv cs.AI
TIER_1English(EN)·Milos Gravara, Andrija Stanisic, Stefan Nastic·
arXiv:2606.14350v1 Announce Type: cross Abstract: Artificial Intelligence (AI) systems must typically satisfy service-level objectives including accuracy, latency, and cost. The prevailing model-centric approaches select a monolithic model at design time and apply identical compu…
arXiv cs.AI
TIER_1English(EN)·Jan Batzner, Sree Harsha Nelaturu, Anastassia Kornilova, Jon Crall, Tommaso Cerruti, Yanan Long, Yifan Mai, Sanchit Ahuja, Asaf Yehudai, Marek \v{S}uppa, John P. Lalor, Oluwagbemike Olowe, Jatin Ganhotra, Brian H. Hu, Eliya Habba, Andrew M. Bean, Chang L…·
arXiv:2606.14516v1 Announce Type: new Abstract: AI evaluations are widely used for testing and understanding progress. However, the diverse evaluators bring with them inconsistencies that challenge analysis and comparison. First, results are saved in incompatible formats, scatter…
arXiv:2606.14502v1 Announce Type: new Abstract: Large Language Models (LLMs) are undergoing a fundamental transformation from conversational generators into integrated AI systems capable of reasoning, action, memory, and self-improvement. We conceptualize this transition as a shi…
Ling-2.6 and Ring-2.6 models are presented as scalable solutions for agentic intelligence, featuring architectural upgrades and specialized training methods to balance fast response times with advanced reasoning capabilities.
arXiv cs.MA (Multiagent)
TIER_1English(EN)·George J. Pappas·
Large language model (LLM) agents are increasingly deployed in multi-agent systems where they must coordinate and agree on shared decisions. We ask whether classical resilient consensus theory, developed for deterministic agents, transfers to LLM agents that may behave adversaria…
AgentPerf from Artificial Analysis, the industry’s first agentic AI benchmark, gives developers, enterprises and infrastructure providers a clear way to compare systems for agentic AI. In the first round of published results, the NVIDIA Blackwell Ultra NVL72 platform delivers lea…
AI evaluations are widely used for testing and understanding progress. However, the diverse evaluators bring with them inconsistencies that challenge analysis and comparison. First, results are saved in incompatible formats, scattered across leaderboards, papers, blog posts, eval…
Large Language Models (LLMs) are undergoing a fundamental transformation from conversational generators into integrated AI systems capable of reasoning, action, memory, and self-improvement. We conceptualize this transition as a shift from Chatbot to Digital Colleague: from conve…
Artificial Intelligence (AI) systems must typically satisfy service-level objectives including accuracy, latency, and cost. The prevailing model-centric approaches select a monolithic model at design time and apply identical computation regardless of input difficulty, cannot deco…
arXiv cs.AI
TIER_1English(EN)·Oliver Aleksander Larsen, Mahyar T. Moghaddam·
arXiv:2606.13298v1 Announce Type: cross Abstract: AI coding tools are now used by a majority of developers, and agentic use of these tools has popularized the practice colloquially called "vibe coding". Yet causal evidence on their effect on software architecture is scarce. Prior…
arXiv:2606.13079v1 Announce Type: cross Abstract: Nowadays, the autonomous execution of cyberattacks capable of causing substantial real-world harm is widely regarded as one of the critical red lines that frontier AI systems must not cross. Within this broader red-line scenario, …
arXiv:2606.12647v1 Announce Type: cross Abstract: AI-augmented computing delegates natural language queries, code generation requests, and other open-ended tasks to a cluster of AI models that processes queries and generates responses. This paradigm introduces a resource dimensio…
arXiv:2606.12835v1 Announce Type: cross Abstract: The rapid emergence of autonomous AI agents is transforming artificial intelligence from isolated model inference into distributed systems of reasoning, communication, and action. This paper develops the vision of the Internet of …
arXiv:2601.21570v2 Announce Type: replace Abstract: The field of Embodied AI is witnessing a rapid evolution toward general-purpose robotic systems, fueled by high-fidelity simulation and large-scale data collection. However, this scaling capability remains severely bottlenecked …
arXiv cs.AI
TIER_1English(EN)·Shayan Kiyani, Sima Noorani, George Pappas, Hamed Hassani·
arXiv:2606.12587v1 Announce Type: new Abstract: Traditionally, decision support studies how humans use machine learning models to make better decisions. In modern agentic systems, this division of roles is increasingly reversed: AI agents act on behalf of users, while humans and …
arXiv cs.AI
TIER_1English(EN)·Md Jafrin Hossain, Mohammad Arif Hossain, Weiqi Liu, Nirwan Ansari·
arXiv:2606.12797v1 Announce Type: new Abstract: Agentic large language model systems that autonomously invoke tools, maintain persistent memory, and execute multi-step plans are increasingly deployed in public-facing domains, including government services, healthcare triage, and …
arXiv:2606.12783v1 Announce Type: new Abstract: World modeling is emerging as a central principle for building intelligent systems capable of prediction, reasoning, and decision making. A central distinction can be drawn between explicit world models, which learn structured dynam…
arXiv:2606.12736v1 Announce Type: new Abstract: AI agents are increasingly being developed to accelerate scientific discovery, yet their practical capabilities in real research settings remain poorly understood. Existing benchmarks for AI agents rarely capture the complexity, het…
Large Language Models are evolving from conversational systems to integrated AI colleagues with enhanced reasoning capabilities and persistent work environments.
arXiv cs.AI
TIER_1English(EN)·Mahyar T. Moghaddam·
AI coding tools are now used by a majority of developers, and agentic use of these tools has popularized the practice colloquially called "vibe coding". Yet causal evidence on their effect on software architecture is scarce. Prior causal work has measured code-level outcomes (com…
arXiv cs.LG
TIER_1English(EN)·Felipe Oviedo, Fiodar Kazhamiaka, Esha Choukse, Allen Kim, Amy Luers, Melanie Nakagawa, Ricardo Bianchini, Juan M. Lavista Ferres·
arXiv:2509.20241v2 Announce Type: replace Abstract: As AI inference scales to billions of queries, estimates of per-query energy use are increasingly important for capacity planning, efficiency interventions, and policy. Yet many public estimates assume non-production settings, l…
arXiv:2605.10907v3 Announce Type: replace-cross Abstract: The dominant paradigm for AI agents is an "on-the-fly" loop in which agents synthesize plans and execute actions within seconds or minutes in response to user prompts. We argue that this paradigm short-circuits disciplined…
arXiv cs.LG
TIER_1English(EN)·Frank Xiao, Mary Phuong·
arXiv:2606.11998v1 Announce Type: new Abstract: Trusted monitoring is a cornerstone of AI control. However, as frontier models grow more capable, the increasing capabilities gap between trusted and untrusted models may render trusted models unreliable monitors. We introduce \emph…
arXiv cs.AI
TIER_1English(EN)·Hayoung Jung, Pedro Viana Diniz, Jos\'e Reinaldo Corr\^ea Roveda, Abner Fernandes da Silva, Haeun Jung, Enoch Tsai, Aleksandra Korolova, Manoel Horta Ribeiro·
arXiv:2606.11337v1 Announce Type: new Abstract: Scientific AI agents increasingly retrieve evidence, reason across sources, and synthesize conclusions used in consequential decisions. Yet, their ability to do so in high-stakes domains such as health remains unclear. We introduce …
arXiv:2606.11869v1 Announce Type: cross Abstract: Custom AI agents areagents that live inside their own application, talk to their own data and tools, enforce their own security boundaries, and carry their own brand and audit trail. What separates them from the general-purpose ti…
arXiv cs.AI
TIER_1English(EN)·Arijit Khan, Longxu Sun, Xin Huang·
arXiv:2606.11560v1 Announce Type: cross Abstract: Large Language Models (LLMs) have advanced rapidly, but their limitations in structured and multi-hop reasoning underscore the need for graph-native, synergistic artificial intelligence (AI) systems. Graph-structured data underpin…
arXiv:2606.11217v1 Announce Type: cross Abstract: The proliferation of large language models (LLMs) and autonomous AI agents has given rise to a rapidly growing methodological paradigm: "in silico" behavioral experiments. Originally conceived as a way to use AI agents as proxies …
arXiv:2606.12320v1 Announce Type: new Abstract: Enterprise security was built to govern data boundaries: the protected surface was data at rest and in transit, and the controls -- access control, data-loss prevention, perimeter inspection -- governed crossings of that boundary. P…
The rapid emergence of autonomous AI agents is transforming artificial intelligence from isolated model inference into distributed systems of reasoning, communication, and action. This paper develops the vision of the Internet of Agentic AI (IoAI): an open ecosystem in which hete…
Enterprise security was built to govern data boundaries: the protected surface was data at rest and in transit, and the controls -- access control, data-loss prevention, perimeter inspection -- governed crossings of that boundary. Production AI agents dissolve this assumption. An…
Trusted monitoring is a cornerstone of AI control. However, as frontier models grow more capable, the increasing capabilities gap between trusted and untrusted models may render trusted models unreliable monitors. We introduce \emph{bootstrapped monitoring}, a protocol that addre…
arXiv cs.AI
TIER_1English(EN)·María José Casañ Guerrero·
Custom AI agents areagents that live inside their own application, talk to their own data and tools, enforce their own security boundaries, and carry their own brand and audit trail. What separates them from the general-purpose tier is fit, not capability: each is built for one j…
arXiv cs.AI
TIER_1English(EN)·Federico Bianchi, Yongchan Kwon, Aneesh Pappu, James Zou·
arXiv:2606.10402v1 Announce Type: cross Abstract: Scientific discovery is often a collective process: researchers share partial results, inspect failed attempts, and build on each other's ideas over long time horizons. Recent AI systems have shown that language-model-based agents…
arXiv:2510.04491v3 Announce Type: replace Abstract: Despite rapid progress in building conversational AI agents, robustness is still largely untested. Small shifts in user behavior, such as being more impatient, incoherent, or skeptical, can cause sharp drops in agent performance…
arXiv cs.AI
TIER_1English(EN)·James Pierce, Vaiva Kalnikait\.e, Siddharth Gupta, Brian Granger·
arXiv:2606.09848v1 Announce Type: cross Abstract: As generative and agentic AI becomes embedded in everyday products, practitioners face a persistent challenge: how to design human-AI coordination -- the ongoing mutual adjustment between users and AI systems as mediate through in…
Large Language Models (LLMs) have advanced rapidly, but their limitations in structured and multi-hop reasoning underscore the need for graph-native, synergistic artificial intelligence (AI) systems. Graph-structured data underpins critical applications across social, biological,…
SciAgentArena presents a comprehensive benchmark for evaluating AI agents in real scientific research scenarios, revealing current limitations in novel insight generation and open-ended problem solving while identifying opportunities for improving agent reliability and autonomy.
Scientific discovery is often a collective process: researchers share partial results, inspect failed attempts, and build on each other's ideas over long time horizons. Recent AI systems have shown that language-model-based agents can make meaningful progress on open scientific p…
arXiv cs.AI
TIER_1English(EN)·Kai A. Horstmann, Ethan Lin, Alice A. Robie, Jennifer J. Sun, Kristin Branson·
arXiv:2606.07718v1 Announce Type: new Abstract: Agentic AI tools offer a promising path to automating software development bottlenecks in scientific research pipelines, particularly for stages that take domain experts days to months to build, where scientists care about correctne…
arXiv:2606.07812v1 Announce Type: new Abstract: Humanity is a mosaic of multifaceted talents and needs, and any truly intelligent AI must reflect that richness. Yet the LLMs used by all are built by the few -- a centralized market of monolithic AI models structurally ill-suited t…
arXiv:2606.08539v1 Announce Type: new Abstract: AI agents increasingly take consequential actions -- shell commands, cloud operations, and arbitrary tool-calls -- so a trust layer must decide, per action, whether to allow, warn, block, or escalate. We argue that the right way to …
arXiv cs.AI
TIER_1English(EN)·Yifan Liu (Klara), Jaime Arguello (Klara), Orland Hoeber (Klara), Chang Liu (Klara), Soo Young Rieh (Klara), Luanne Sinnamon (Klara), Dean Alvarez (Klara), Susan Archambault (Klara), Rob Capra (Klara), Henson Chen (Klara), Charles Costa (Klara), Anita Cr…·
arXiv:2606.08936v1 Announce Type: cross Abstract: This report summarizes the CHIIR 2026 Workshop on Generative AI and Academic Search (GAI\&AS), which examined how GenAI is reshaping academic search systems and research practices. The workshop brought together researchers in …
arXiv:2606.09692v1 Announce Type: cross Abstract: Delegation-scoped execution is not identifiable from standard observables: audit logs and execution traces can be identical under multiple incompatible delegation assignments. This gap is especially acute in LLM-based agentic syst…
arXiv cs.AI
TIER_1English(EN)·Jun Takahashi, Atsunori Moteki, Akiyoshi Uchida, Shoichi Masui, Fan Yang, Kanji Uchino, Yueqi Song, Yonatan Bisk, Graham Neubig, Ikuo Kusajima, Yasuto Watanabe, Hiroyuki Ishida, Koki Nakagawa, Shan Jiang·
arXiv:2505.19662v4 Announce Type: replace Abstract: This paper introduces FieldWorkArena, a benchmark for agentic AI targeting real-world field work. With the recent increase in demand for agentic AI, they are built to detect and document safety hazards, procedural violations, an…
arXiv:2605.22781v2 Announce Type: replace-cross Abstract: LLM-powered AI agents require high-frequency state exploration (e.g., test-time tree search and reinforcement learning), relying on rapid checkpoint and rollback (C/R) of the complete sandbox state, including files and pro…
arXiv:2606.07576v1 Announce Type: new Abstract: We present CARTOGRAPH, a verification layer for AI scientists that couples unresolved-subspace experiment steering (select), explicit ambiguity closure (resolve), and residual-based library inadequacy detection (refuse). Under a loc…
arXiv cs.AI
TIER_1English(EN)·Muhammad Haris Khan, Joel wester·
arXiv:2606.09587v1 Announce Type: cross Abstract: People are increasingly using AI for creative tasks such as writing. While adoption continues to grow, this form of use risks undermining individual creativity locally and reducing the heterogeneity of creative output at scale. In…
arXiv cs.AI
TIER_1English(EN)·Muhammad Zia Hydari, Raja Iqbal·
arXiv:2606.08998v1 Announce Type: new Abstract: Agentic AI systems can behave differently across runs: the same request may produce a different plan, a different tool call, a different code edit, or a different final answer. Such variability arises from several layers that are of…
arXiv cs.AI
TIER_1English(EN)·Ian Seet, Jonas Bozenhard, Simon Osterman·
arXiv:2606.07998v1 Announce Type: cross Abstract: Recent advances in generative AI, especially powerful Large Language Models (LLMs) and Large Reasoning Models (LRMs), raise concerns over the interpretability, safety and sustainability of these large and opaque AI models. The pow…
arXiv:2602.06934v4 Announce Type: replace-cross Abstract: Grassroots Logic Programs (GLP) is a concurrent logic programming language in which logic variables are partitioned into paired readers and writers. An assignment is produced at most once via a writer and consumed at most …
arXiv cs.AI
TIER_1English(EN)·Rishabh Sabharwal, Hongru Wang, Amos Storkey, Jeff Z. Pan·
arXiv:2606.09748v1 Announce Type: new Abstract: Existing benchmarks for deep research agents (DRAs) assess only single-shot outputs, ignoring a key question: can DRAs improve their reports when guided by feedback? To investigate this, we conduct a multi-turn evaluation of DRAs un…
Existing benchmarks for deep research agents (DRAs) assess only single-shot outputs, ignoring a key question: can DRAs improve their reports when guided by feedback? To investigate this, we conduct a multi-turn evaluation of DRAs under two feedback settings: self-reflection, in w…
Delegation-scoped execution is not identifiable from standard observables: audit logs and execution traces can be identical under multiple incompatible delegation assignments. This gap is especially acute in LLM-based agentic systems, where agents dynamically select tools, vary e…
People are increasingly using AI for creative tasks such as writing. While adoption continues to grow, this form of use risks undermining individual creativity locally and reducing the heterogeneity of creative output at scale. In response, we introduce the Semantic Repulsion Tec…
arXiv:2605.06890v3 Announce Type: replace Abstract: AI agents are promising for high-stakes enterprise workflows, but dependable deployment remains limited because tool-use failures are difficult to diagnose and control. Agents may skip required tool calls, invoke tools unnecessa…
arXiv:2606.06660v1 Announce Type: new Abstract: Long-horizon robot manipulation tends to fail gradually: one bad step degrades the state, and the policy spirals into a basin from which it cannot recover. The failure is often visible before it happens. We introduce AEGIS (Activati…
arXiv cs.AI
TIER_1English(EN)·Catherine Ge-Wang, Tyler Crosse, Benjamin Hadad IV, Joachim Schaeffer, Ram Potham, Tyler Tracy·
arXiv:2606.06529v1 Announce Type: new Abstract: An attacker that strategically chooses when to attack is much harder to catch than one that attacks indiscriminately. AI control is a safety framework for deploying capable but untrusted AI agents under the oversight of a weaker, tr…
arXiv:2603.13428v2 Announce Type: replace-cross Abstract: With AI agents increasingly deployed as long-running systems, it becomes essential to autonomously construct and continuously evolve customized software to enable interaction within dynamic environments. Yet, existing benc…
arXiv cs.AI
TIER_1English(EN)·Jeremy Yang, Kate Zyskowski, Noah Yonack, Jerry Ma·
arXiv:2606.07489v1 Announce Type: new Abstract: Frontier AI systems are bridging the gap between intelligence and utility by shifting from conversational assistants to autonomous agents that execute tasks end to end. Using production data from Perplexity's Search and Computer pro…
arXiv cs.AI
TIER_1English(EN)·M. Danish Lim, I. Danial Bin Sharudin, Wen Han Chen, Cedric Lim, Laura Wynter·
arXiv:2606.06923v1 Announce Type: new Abstract: We study orchestration mechanisms for tool-using AI agents in realistic customer-service workflows over an unstructured knowledge base. We argue that declarative agents -- AI agents equipped with natural-language skill files appende…
This report summarizes the CHIIR 2026 Workshop on Generative AI and Academic Search (GAI\&AS), which examined how GenAI is reshaping academic search systems and research practices. The workshop brought together researchers in human information interaction and information retrieva…
AI agents increasingly take consequential actions -- shell commands, cloud operations, and arbitrary tool-calls -- so a trust layer must decide, per action, whether to allow, warn, block, or escalate. We argue that the right way to reason about such a layer is by threat type. Lex…
The emergence of large language model (LLM)-based agents and multi-agent systems has enabled a shift from narrow task automation to more autonomous decision-making. Despite progress in language generation, planning, tool use, and coordination, most agents still treat intelligence…
arXiv:2606.05449v1 Announce Type: new Abstract: Agentic artificial intelligence (AI) systems are transforming the risk landscape by extending beyond information generation to autonomous planning, tool invocation, decision execution, and persistent modification of digital and phys…
arXiv:2606.05608v1 Announce Type: cross Abstract: For over half a century, software engineering has operated on a foundational premise: human engineers decompose problems, encode decision logic into static code, and manually adapt that code as requirements evolve. This paper argu…
arXiv:2606.05395v1 Announce Type: cross Abstract: Reusable robot skills are becoming the basic units through which embodied agents turn open-ended instructions into long-horizon physical behavior. We argue that, while foundation models have collapsed the cost of creating these sk…
Frontier AI systems are bridging the gap between intelligence and utility by shifting from conversational assistants to autonomous agents that execute tasks end to end. Using production data from Perplexity's Search and Computer products, we study this transition by examining how…
We study orchestration mechanisms for tool-using AI agents in realistic customer-service workflows over an unstructured knowledge base. We argue that declarative agents -- AI agents equipped with natural-language skill files appended to the system prompt -- are an effective orche…
arXiv cs.LG
TIER_1English(EN)·Otto Nyberg, Fausto Carcassi, Davide Tugnoli, Giovanni Cin\`a·
arXiv:2602.21889v2 Announce Type: replace-cross Abstract: Predictions from ML models support human decision making in several fields, including high-stakes ones such as healthcare and the judiciary. Yet, we still lack a clear understanding of how decision makers learn from ML-bas…
AI agents are commonly evaluated using task success, reward, latency, and cost. These metrics are useful, but they often miss important aspects of agent behavior: whether an agent explores too much, repeats itself too rigidly, uses tools effectively, reduces uncertainty over time…
arXiv:2401.07386v5 Announce Type: cross Abstract: This study expands on previous work that introduced the AIcon2abs method (AI from Concrete to Abstract: Demystifying Artificial Intelligence to the general public), an innovative approach designed to increase public understanding …
arXiv cs.AI
TIER_1English(EN)·Harsha Vardhan Khurdula, Vineet Agarwal, Yoeven D Khemlani·
arXiv:2602.04101v2 Announce Type: replace Abstract: We present Interfaze, a native hybrid model that fuses task-specific deep neural networks (CNNs and DNNs) directly into a transformer decoder through a shared embedding space. Specialized perceptual encoders handle optical chara…
arXiv:2006.04013v6 Announce Type: cross Abstract: Artificial Intelligence (AI) has been adopted in a wide range of domains. This shows the imperative need to develop means to endow common people with a minimum understanding of what AI means. Combining visual programming and WiSAR…
arXiv cs.AI
TIER_1English(EN)·Ulbert Jose Botero, Liam Smith, Brooks Olney, Pooya Khorrami, Steven Kusiak, Watson Jia, Sage Trudeau, Daniel Capecci·
arXiv:2606.04106v1 Announce Type: cross Abstract: Foundation models achieve generalization through massive-scale training on diverse data, but have limitations with transfer to truly unseen domains without paired training data. We propose principle-driven foundation models that e…
arXiv cs.AI
TIER_1English(EN)·Sanderson Oliveira de Macedo·
arXiv:2606.04967v1 Announce Type: cross Abstract: AI tools for programming are no longer just autocomplete or chat assistants: they organize themselves as development frameworks, with process, roles, artifacts and verification. Recent surveys map agents and LLMs for software engi…
arXiv:2606.05037v1 Announce Type: cross Abstract: When an AI agent calls an API and hits a validation error, it needs more than what went wrong -- it needs what to do next. A self-reflective API returns, on validation failure, a machine-readable recovery\_feedback.suggestions[] p…
arXiv:2606.04321v1 Announce Type: new Abstract: Agentic AI deployments face a recurring design tension: heavy human oversight limits scale, while broad autonomy outruns accountability. Neither posture provides the governance infrastructure required for responsible delegation. We …
arXiv cs.AI
TIER_1English(EN)·Katherine M. Collins, Simon Frieder, Jonas Bayer, Jacob Loader, Jeck Lim, Peiyang Song, Fabian Zaiser, Lexin Zhou, Shanda Li, Sam Looi, Joshua B. Tenenbaum, Umang Bhatt, Adrian Weller, Jose Hernandez-Orallo, Cameron E. Freer, Valerie Chen, Ilia Sucholuts…·
arXiv:2606.04273v1 Announce Type: new Abstract: For centuries, human mathematicians have written proofs to substantiate their mathematical arguments; yet, the ability to automatically verify the validity of proofs has long been a challenge. Advances in AI systems' ability to gene…
arXiv:2606.04779v1 Announce Type: new Abstract: Complementarity is the case in which a human--AI interaction (HAI) outperforms the best prediction benchmark available among its members. Although this idea is central in HAI research, formal work on complementarity remains limited.…
ForeSci is a temporally controlled benchmark that evaluates LLM agents' ability to make forward-looking research decisions from historical evidence across fast-moving AI domains.
When an AI agent calls an API and hits a validation error, it needs more than what went wrong -- it needs what to do next. A self-reflective API returns, on validation failure, a machine-readable recovery\_feedback.suggestions[] payload sufficient for the agent to repair the requ…
When an AI agent calls an API and hits a validation error, it needs more than what went wrong -- it needs what to do next. A self-reflective API returns, on validation failure, a machine-readable recovery\_feedback.suggestions[] payload sufficient for the agent to repair the requ…
arXiv cs.AI
TIER_1English(EN)·Sanderson Oliveira de Macedo·
AI tools for programming are no longer just autocomplete or chat assistants: they organize themselves as development frameworks, with process, roles, artifacts and verification. Recent surveys map agents and LLMs for software engineering, but a study centered on the operational f…
Complementarity is the case in which a human--AI interaction (HAI) outperforms the best prediction benchmark available among its members. Although this idea is central in HAI research, formal work on complementarity remains limited. Existing frameworks do not model how agents' pr…
arXiv:2602.16666v3 Announce Type: replace Abstract: AI agents are increasingly deployed to execute important tasks. While rising accuracy scores on standard benchmarks suggest rapid progress, many agents still continue to fail in practice. This discrepancy highlights a fundamenta…
arXiv cs.AI
TIER_1English(EN)·Marcus R\"ub, Michael Gerhards·
arXiv:2606.02862v1 Announce Type: new Abstract: The rise of Large Language Models (LLMs) has enabled agentic AI capable of complex reasoning and tool use; however, deploying such autonomy in pervasive computing environments remains challenging due to the strict memory and energy …
arXiv cs.AI
TIER_1English(EN)·Xuanqiang Angelo Huang, Charlie Tharas, Samuele Marro, Van Q. Truong, Bernhard Sch\"olkopf, Emanuele La Malfa, Zhijing Jin·
arXiv:2605.08426v2 Announce Type: replace-cross Abstract: Ensuring that AI agents behave safely and beneficially when interacting with other parties has emerged as one of the central challenges of modern AI safety. While mechanism design, as the theory of designing rules to align…
arXiv:2606.03518v1 Announce Type: new Abstract: As AI systems evolve from passive models into autonomous active agents capable of initiating actions, collaborating, and delegating tasks, the traditional boundaries of software systems blur. Traditional authorization and delegation…
arXiv cs.AI
TIER_1English(EN)·Fiona Y. Wang, Markus J. Buehler·
arXiv:2606.01444v1 Announce Type: new Abstract: Scientific discovery is not only answer generation but revision of the representational regime in which evidence, artifacts, operations, and verifiers are typed. We develop a category-theoretic account of agentic discovery for mater…
arXiv cs.AI
TIER_1English(EN)·Sindhuja Chaduvula, Jessee Ho, Kina Kim, Aravind Narayanan, Ahmed Y. Radwan, Mahshid Alinoori, Muskan Garg, Dhanesh Ramachandram, Shaina Raza·
arXiv:2602.06841v4 Announce Type: replace Abstract: Over the last decade, Explainable AI has primarily focused on interpreting individual model predictions, producing post-hoc explanations that relate inputs to outputs under a fixed decision structure. Recent advances in large la…
arXiv cs.AI
TIER_1English(EN)·An Luo, Jin Du, Xun Xian, Robert Specht, Fangqiao Tian, Ganghua Wang, Xuan Bi, Charles Fleming, Ashish Kundu, Jayanth Srinivasa, Mingyi Hong, Rui Zhang, Tianxi Li, Galin Jones, Jie Ding·
arXiv:2603.19005v2 Announce Type: replace-cross Abstract: Data science plays a critical role in transforming complex data into actionable insights across numerous domains. Recent developments in large language models (LLMs) and artificial intelligence (AI) agents have significant…
arXiv:2606.00090v1 Announce Type: cross Abstract: Physical AI systems increasingly map multimodal observations, language instructions, and learned world representations into physically consequential actions. Robotics foundation models, vision-language-action models, and world-mod…
arXiv cs.AI
TIER_1English(EN)·Kevin Kappelmann, Maximilian Sch\"affeler, Lukas Stevens, Mohammad Abdulaziz, Andrei Popescu, Dmitriy Traytel·
arXiv:2604.15713v2 Announce Type: replace-cross Abstract: Type annotations are essential when printing terms in a way that preserves their meaning under reparsing and type inference. We study the problem of complete and minimal type annotations for rank-one polymorphic $\lambda$-…
arXiv:2606.00644v1 Announce Type: new Abstract: AI research often requires decisions before future evidence exists: which bottleneck to attack, which direction to pursue, or where a project should be positioned. We introduce ForeSci, a temporally controlled benchmark for evaluati…
The rise of Large Language Models (LLMs) has enabled agentic AI capable of complex reasoning and tool use; however, deploying such autonomy in pervasive computing environments remains challenging due to the strict memory and energy constraints of embedded microcontrollers. Existi…
arXiv cs.AI
TIER_1English(EN)·Jun Rui Huang, Wang Bill Zhu, Ziyi Liu, Nathanael Fast, Ravi Iyer, Robin Jia·
arXiv:2605.30654v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as conversational partners for companionship, emotional disclosure, and interpersonal advice, but the social dynamics of these interactions can create harms that are not captured …
arXiv cs.AI
TIER_1English(EN)·David Fern\'andez-Narro, Pablo Ferri, \'Angel S\'anchez-Garc\'ia, Juan M. Garc\'ia-G\'omez, Carlos S\'aez·
arXiv:2605.31360v1 Announce Type: cross Abstract: The Artificial Intelligence (AI) life cycle requires a thorough understanding of the underlying data dynamics for robust, safe and cost-effective AI development and use. Dataset shifts are defined as changes between train and test…
The Artificial Intelligence (AI) life cycle requires a thorough understanding of the underlying data dynamics for robust, safe and cost-effective AI development and use. Dataset shifts are defined as changes between train and test data distributions. Whether occurring over time (…
arXiv:2605.28916v1 Announce Type: cross Abstract: We report a comparison of two state-of-the-art agentic AI systems, Claude Code (Anthropic) and Codex (OpenAI), tasked with autonomously executing a simple end-to-end gravitational wave data analysis pipeline on a shared computing …
arXiv:2605.29713v1 Announce Type: cross Abstract: This book provides a compact, derivation-oriented introduction to the mathematical foundations of modern generative artificial intelligence. Rather than surveying every recent architecture or implementation detail, it develops a c…
arXiv cs.AI
TIER_1English(EN)·Lorenz Kutschka, Bernhard Geiger·
arXiv:2605.29676v1 Announce Type: new Abstract: Large language models in Agentic AI systems consume tool schemas and execution results and emit tool invocations as structured data. The default language for that exchange, JSON, was designed for application-to-application interchan…
arXiv cs.AI
TIER_1English(EN)·Muhammad Zia Hydari, Raja Iqbal, Narayan Ramasubbu·
arXiv:2605.29129v1 Announce Type: new Abstract: Agentic AI systems are increasingly being explored as production infrastructure: they reason over multiple steps, call tools, act through workflows, and adapt through memory and feedback. These systems create governance challenges t…
arXiv cs.AI
TIER_1English(EN)·William Yicheng Zhu, Lei Zhu·
arXiv:2604.04956v3 Announce Type: replace-cross Abstract: The recent, super-exponential scaling of autonomous Large Language Model (LLM) agents signals a broader, fundamental paradigm shift from machines primarily replacing the human hands (manual labor and mechanical processing)…
arXiv cs.CL
TIER_1English(EN)·Vishakh Padmakumar, Lujain Ibrahim, Zora Zhiruo Wang, Jennifer Wang, Q. Vera Liao, Diyi Yang·
arXiv:2605.29392v1 Announce Type: cross Abstract: AI tools are increasingly integrated into real-world workflows. However, existing measures of reliance on these tools focus on AI output adoption or on self-reported indicators, rather than how task effort is distributed between u…
arXiv:2605.27873v1 Announce Type: new Abstract: AI models underpin data-centric applications from image and text processing to scientific discovery in biology, physics, and chemistry. Yet developing them remains heavily manual, requiring practitioners to design architectures, bui…
arXiv:2605.28508v1 Announce Type: new Abstract: Existing AI evaluation practices often fail to capture how systems actually perform in low-resource environments, where operational constraints shape usability as much as model quality. Through a structured analysis of existing benc…
arXiv cs.AI
TIER_1English(EN)·Yihong Tang, Andrew Robert Williams, Arjun Ashok, Vincent Zhihao Zheng, Lijun Sun, Alexandre Drouin, Issam H. Laradji, \'Etienne Marcotte, Valentina Zantedeschi·
arXiv:2605.27904v1 Announce Type: new Abstract: Time series forecasting in real-world settings often depends not only on historical observations, but also on external context that must be actively discovered from noisy, heterogeneous information sources. Yet existing context-aide…
arXiv:2605.27879v1 Announce Type: new Abstract: Explainable AI (XAI) helps users interpret model behavior and identify potential faults. Agentic XAI systems use Large Language Models (LLMs) to make explanations more accessible through natural-language interaction, but they can al…
arXiv:2605.27628v1 Announce Type: new Abstract: As autonomous and agentic AI systems scale in robotic and human-machine environments, managing hallucination and persistent but unjustified action remains an open challenge. Rather than attributing these failures solely to model or …
arXiv:2604.14585v2 Announce Type: replace Abstract: Prompt optimization in compound AI systems is statistically indistinguishable from a coin flip: across 72 optimization runs on Claude Haiku 4.5 (6 methods $\times$ 4 tasks $\times$ 3 repeats), 49% score below zero-shot; on Amazo…
arXiv:2605.28764v1 Announce Type: new Abstract: Vast quantities of compute (GPU cycles on personal workstations, idle inference servers, and edge devices between jobs) go unused because no incentive-aligned protocol exists for their owners to share them safely and profitably. Exi…
arXiv:2605.08678v2 Announce Type: replace Abstract: Modern AI progress has been driven by ML methods that are generalizable across settings and scalable to larger regimes. As large language models demonstrate advanced capabilities in reasoning, coding, and engineering tasks, it i…
arXiv:2605.27575v1 Announce Type: new Abstract: As organizations move toward production deployments of AI agents, which execute non-deterministic workflows, maintain stateful sessions, and often operate with privileged access to internal services, the engineering challenge shifts…
Vast quantities of compute (GPU cycles on personal workstations, idle inference servers, and edge devices between jobs) go unused because no incentive-aligned protocol exists for their owners to share them safely and profitably. Existing approaches either require a trusted centra…
AI factories are token factories, converting power into intelligence in real time. And as agentic AI scales and autonomous, always-on special agents are deployed in the enterprise, performance per watt and cost per token become the economics that matter.
Existing AI evaluation practices often fail to capture how systems actually perform in low-resource environments, where operational constraints shape usability as much as model quality. Through a structured analysis of existing benchmark families across speech, chat/RAG, and visi…
arXiv:2605.26305v1 Announce Type: new Abstract: This paper details two novel frameworks for developing autonomous, agentic AI in scientific workflows. Both systems leverage a hybrid Local Body, Remote Brain architecture via Google Colab, utilizing Python-based local orchestrators…
arXiv:2605.26508v1 Announce Type: cross Abstract: We propose a foundational runtime actuarial layer for autonomous AI agents in which every side-effect-bearing action carries a time-consistent, counterfactual risk toll computed against a contractually fixed safe default, inside a…
arXiv cs.AI
TIER_1English(EN)·Xue Qin, Simin Luan, John See, Zeyd Boukhers, Cong Yang, Zhijun Li·
arXiv:2604.08059v5 Announce Type: replace-cross Abstract: Software systems built from versioned AI components increasingly need lifecycle-time governance: when a capability module evolves into a new version, the hosting system must decide whether the new version may be activated …
arXiv cs.LG
TIER_1English(EN)·Vasilios A. Siris, Adamantia Stamou, George D. Stamoulis, Konstantinos Varsos, Ramin Khalili·
arXiv:2605.27309v1 Announce Type: new Abstract: The widespread use of AI services has raised concerns for its environmental sustainability, towards which recent studies have identified carbon emissions of AI inference as the major contributor. This paper introduces a framework fo…
arXiv:2602.22190v2 Announce Type: replace-cross Abstract: Open-source native GUI agents still lag behind closed-source systems on long-horizon navigation tasks. This gap stems from two limitations: a shortage of high-quality, action-aligned reasoning data, and the direct adoption…
arXiv:2605.26870v1 Announce Type: cross Abstract: Background: Large language models are typically evaluated as models, benchmarks, or short conversational episodes. Less is known about what happens when an agent is embedded persistently in a real academic research environment wit…
Explainable AI (XAI) helps users interpret model behavior and identify potential faults. Agentic XAI systems use Large Language Models (LLMs) to make explanations more accessible through natural-language interaction, but they can also produce plausible yet unfaithful explanations…
As autonomous and agentic AI systems scale in robotic and human-machine environments, managing hallucination and persistent but unjustified action remains an open challenge. Rather than attributing these failures solely to model or alignment limitations, this paper explores the a…
As organizations move toward production deployments of AI agents, which execute non-deterministic workflows, maintain stateful sessions, and often operate with privileged access to internal services, the engineering challenge shifts from building individual agents to operating th…
The widespread use of AI services has raised concerns for its environmental sustainability, towards which recent studies have identified carbon emissions of AI inference as the major contributor. This paper introduces a framework for designing AI inference incentives based on the…
The widespread use of AI services has raised concerns for its environmental sustainability, towards which recent studies have identified carbon emissions of AI inference as the major contributor. This paper introduces a framework for designing AI inference incentives based on the…
arXiv cs.MA (Multiagent)
TIER_1English(EN)·Anas H. Alzahrani·
Background: Large language models are typically evaluated as models, benchmarks, or short conversational episodes. Less is known about what happens when an agent is embedded persistently in a real academic research environment with durable memory, local files, external tools, sch…
arXiv:2605.22634v2 Announce Type: replace-cross Abstract: Skills have become a practical packaging mechanism for agent instructions, workflows, scripts, and reference materials. In enterprise settings, however, a skill often needs to express more than task guidance: goals, input …
arXiv:2605.23935v1 Announce Type: new Abstract: Autonomous agent systems fail not only due to incorrect decisions, but due to executing decisions whose authority no longer holds at runtime. Prior work defined Reconstructive Authority (RAM) as a condition for valid execution: acti…
arXiv:2605.23951v1 Announce Type: new Abstract: The companion paper introduced a four-level verification lattice on agent-skill manifests (unverified, declared, tested, formal) and left the top level aspirational. This paper closes that gap. We give a precise semantics for skill …
arXiv:2605.25624v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has driven breakthroughs in domains such as math, tool-use, and software engineering, yet its extension to computer-use agents (CUAs) has been bottlenecked by the scarcity of sca…
arXiv:2605.25632v1 Announce Type: new Abstract: Autonomous AI agents increasingly issue side-effect-bearing actions: database mutations, refunds, payments, external commitments. We propose the Actuarial Action Interface (AAI), a deterministic runtime contract that prices each suc…
arXiv cs.CL
TIER_1English(EN)·Vaishnavi Shrivastava, Piero Kauffmann, Ahmed Awadallah, Dimitris Papailiopoulos·
arXiv:2605.24517v1 Announce Type: cross Abstract: CLI agents are the closest thing language models have to an embodied setting: the model emits commands, the terminal executes them, and the returned stream -- stdout, errors, files, logs, and traces -- records the consequences. We…
arXiv cs.AI
TIER_1English(EN)·Haolang Zhao, Yunbo Long, Lukas Beckenbauer, Alexandra Brintrup·
arXiv:2605.26081v1 Announce Type: new Abstract: Deep research agents face vast, interdependent, and pervasively uncertain information. Existing systems explore what evolving intermediate representations should look like, but leave their evolution to the LLM's implicit reasoning. …
arXiv:2605.26112v1 Announce Type: new Abstract: This paper studies the next major bottleneck in agentic AI as system scaling, not only model scaling: the design of auditable, persistent, modular, and verifiable architectures around foundation models. We refer to this shift as sca…
arXiv cs.AI
TIER_1English(EN)·Wonjoong Kim, Sangwu Park, Yeonjun In, Sein Kim, Dongha Lee, Chanyoung Park·
arXiv:2605.13850v2 Announce Type: replace Abstract: Existing frameworks for LLM-based agent architectures describe systems from a single perspective: industry guides (Anthropic, Google, LangChain) focus on execution topology -- how data flows -- while cognitive science surveys fo…
arXiv:2605.24785v1 Announce Type: new Abstract: Recent advances in multimodal web agents often rely on increased inference-time computation, including rollout search, verifier passes, offline skill discovery, and specialist model stacks. This raises a central question: can a web …
arXiv cs.CL
TIER_1English(EN)·Junlin Wang, Federico Bianchi, Shang Zhu, Fan Nie, Yongchan Kwon, Bhuwan Dhingra, James Zou·
arXiv:2605.26079v1 Announce Type: new Abstract: Modern AI benchmarks operate at a complexity that outpaces traditional verification methods. Tasks authored by domain experts often contain implicit assumptions, incomplete environment specifications, and brittle evaluation logic th…
arXiv:2605.25931v1 Announce Type: new Abstract: We systematically investigate all 25 public ARC-AGI-3 games and find that every one is reachable through non-intelligent strategies: 10 in a single blind step, 5 after one probing action, 1 via repeated ACTION1 presses, 1 via divers…
A self-improving AI framework simultaneously updates both model weights and task-specific agent architecture through a language-model feedback agent across legal classification, GPU optimization, and biological data denoising tasks.
PANDO is a web agent framework that improves efficiency through experience accumulation by reducing redundant actions, optimizing skill discovery, and enhancing prompt caching without sacrificing performance.
This paper studies the next major bottleneck in agentic AI as system scaling, not only model scaling: the design of auditable, persistent, modular, and verifiable architectures around foundation models. We refer to this shift as scaling the harness: treating the structured execut…
Deep research agents face vast, interdependent, and pervasively uncertain information. Existing systems explore what evolving intermediate representations should look like, but leave their evolution to the LLM's implicit reasoning. Without explicit regulation, the intermediate la…
Modern AI benchmarks operate at a complexity that outpaces traditional verification methods. Tasks authored by domain experts often contain implicit assumptions, incomplete environment specifications, and brittle evaluation logic that human annotation cannot reliably catch. We in…
We systematically investigate all 25 public ARC-AGI-3 games and find that every one is reachable through non-intelligent strategies: 10 in a single blind step, 5 after one probing action, 1 via repeated ACTION1 presses, 1 via diverse exploration, and 8 via single repeated actions…
We systematically investigate all 25 public ARC-AGI-3 games and find that every one is reachable through non-intelligent strategies: 10 in a single blind step, 5 after one probing action, 1 via repeated ACTION1 presses, 1 via diverse exploration, and 8 via single repeated actions…
arXiv cs.AI
TIER_1English(EN)·Federico Bottino, Carlo Ferrero, Nicholas Dosio, Pierfrancesco Beneventano·
arXiv:2604.11759v2 Announce Type: replace Abstract: Organizational knowledge used by AI agents typically lacks epistemic structure: retrieval systems surface semantically relevant content without distinguishing binding decisions from abandoned hypotheses, contested claims from se…
arXiv:2604.07813v2 Announce Type: replace Abstract: Learning theories have historically changed when the conditions of learning evolved. Generative and agentic AI create a new condition by allowing learners to delegate explanation, writing, problem solving, and other cognitive wo…
arXiv cs.AI
TIER_1English(EN)·Chitra Badagi, Divye Singh, Animesh Sen, Adinath Shirsath·
arXiv:2605.23459v1 Announce Type: cross Abstract: Enterprise AI systems, built on large language models, retrieval pipelines and autonomous agents, introduce a class of risks that traditional software quality assurance was never designed to address. These systems are probabilisti…
arXiv cs.AI
TIER_1English(EN)·Joshua Odmark, Gideon Rubin, Deon van der Vyver·
arXiv:2605.23058v1 Announce Type: cross Abstract: Empirical claims about autonomous Kubernetes operations agents are largely unfalsifiable. Published work reports observational results without controlled comparisons against an agent-disabled baseline, selection bias is endemic, p…
arXiv:2605.23904v1 Announce Type: new Abstract: Agent skills today are hand-crafted, generated one-shot, or evolved through loosely controlled self-revision, none of which behaves like a deep-learning optimizer for the skill, and none of which reliably improves over its starting …
arXiv:2605.23414v1 Announce Type: new Abstract: LLM-based multi-agent systems can fail even when planned actions are executed correctly because agents may misjudge their knowledge when evaluating plan feasibility, a phenomenon we term epistemic miscalibration in planning. Unlike …
arXiv:2605.23179v1 Announce Type: new Abstract: Agentic AI orchestrators reduce the interface and assembly costs of composing information systems capabilities across organizational boundaries, seemingly accelerating modularization and organizational disaggregation. Yet AI-enabled…
arXiv:2605.22905v1 Announce Type: new Abstract: Self-evolving agents should not train on examples they cannot justify. Data-free self-evolving search agents offer a scalable route to systems that generate their own questions, answer them, and improve from their own feedback witho…
arXiv:2605.22883v1 Announce Type: new Abstract: Current AI energy benchmarks measure consumption at the granularity of a single model invocation or training run. For classical single-turn workloads this unit remains coherent. For agentic systems - where a single user goal may tri…
arXiv:2605.23024v1 Announce Type: new Abstract: Large language models now write software, draft legal documents, and produce clinical notes, yet fundamental limits, from Turing and Arrow to the No Free Lunch theorems, shape what computation can do. This thesis turns such impossib…
RLVR framework for computer-use agents addresses data scarcity through scalable generation pipeline and synthetic environments, achieving superior performance on verification and transfer benchmarks.
Deliberative democracy arguably leads to better collective decisions, but is fundamentally constrained by human attention and bandwidth. While recent AI-mediated deliberations scale participation by synthesizing inputs from many humans, they remain time-intensive for individual u…
Deliberative democracy arguably leads to better collective decisions, but is fundamentally constrained by human attention and bandwidth. While recent AI-mediated deliberations scale participation by synthesizing inputs from many humans, they remain time-intensive for individual u…
Deliberative democracy arguably leads to better collective decisions, but is fundamentally constrained by human attention and bandwidth. While recent AI-mediated deliberations scale participation by synthesizing inputs from many humans, they remain time-intensive for individual u…
Environment cross-entropy hybrid objective combines policy-gradient loss with auxiliary environment observation prediction to provide dense supervision from terminal feedback, improving agent performance and self-improvement capabilities.
Physical AI systems face safety challenges where black-box models can execute harmful actions without detection, necessitating comprehensive runtime guardrail mechanisms for safe operation.
AI agents are entering high-risk production settings, where they use tools, retain context, follow policies, handle private data, and interact with users over multiple turns. Yet many evaluation methods still judge isolated outputs or static tasks, missing failures that emerge th…
Agent skills today are hand-crafted, generated one-shot, or evolved through loosely controlled self-revision, none of which behaves like a deep-learning optimizer for the skill, and none of which reliably improves over its starting point under feedback. We argue the skill should …
Enterprise AI systems, built on large language models, retrieval pipelines and autonomous agents, introduce a class of risks that traditional software quality assurance was never designed to address. These systems are probabilistic, context-sensitive and emergent: they cannot be …
LLM-based multi-agent systems can fail even when planned actions are executed correctly because agents may misjudge their knowledge when evaluating plan feasibility, a phenomenon we term epistemic miscalibration in planning. Unlike execution errors, epistemic miscalibration is la…
arXiv:2605.20530v1 Announce Type: new Abstract: Large language model agents now act on codebases, browsers, operating systems, calendars, files, and tool ecosystems, but the benchmarks used to evaluate them are fragmented: each emphasizes a different unit of measurement (final ta…
arXiv:2605.21850v1 Announce Type: new Abstract: Recent development of agents has renewed demand for long-context reasoning capacity of LLMs. However, training LLMs for this capacity requires costly long-document curation or heuristic context synthesis. We observe that agents prod…
arXiv:2605.15040v2 Announce Type: replace-cross Abstract: Agentic modeling aims to transform LLMs into autonomous agents capable of solving complex tasks through planning, reasoning, tool use, and multi-turn interaction with environments. Despite major investment, open research r…
arXiv cs.LG
TIER_1English(EN)·Fiona Y. Wong, Markus J. Buehler·
arXiv:2605.22300v1 Announce Type: cross Abstract: Scientific evidence often spans instruments, databases, and disciplines, so no single source records the full phenomenon. This makes it difficult to determine when coordinated AI agents add value over simpler scientific workflows.…
arXiv cs.AI
TIER_1English(EN)·Lucas Jing, Xinqi Wang, Liao Zhang, Simon S. Du·
arXiv:2605.15229v2 Announce Type: replace-cross Abstract: Existing code benchmarks measure whether an agent can produce any test that reproduces a known bug, or whether it can produce a patch that fixes a described issue. Neither isolates the distinct skill of property-based test…
arXiv cs.AI
TIER_1English(EN)·Lujain Ibrahim, Katherine M. Collins, Sunnie S. Y. Kim, Anka Reuel, Max Lamparth, Kevin Feng, Lama Ahmad, Prajna Soni, Alia El Kattan, Merlin Stein, Siddharth Swaroop, Vishakh Padmakumar, Ilia Sucholutsky, Andrew Strait, Diyi Yang, Q. Vera Liao, Umang Bh…·
arXiv:2509.08010v2 Announce Type: replace-cross Abstract: Large language models (LLMs) distinguish themselves from previous technologies by functioning as collaborative ``thought partners,'' capable of engaging more fluidly in natural language on a range of tasks. As LLMs increas…
arXiv cs.LG
TIER_1English(EN)·Simon Dennis, Rivaan Patil, Kevin Shabahang, Hao Guo·
arXiv:2605.22502v1 Announce Type: cross Abstract: Agent orchestration frameworks have proliferated, collectively exceeding 290,000 GitHub stars across LangGraph, CrewAI, Google ADK, OpenAI Agents SDK, Semantic Kernel, Strands, and LlamaIndex. All follow the same pattern: an exter…
arXiv:2605.22794v1 Announce Type: cross Abstract: Autonomous agentic systems are largely static after deployment: they do not learn from user interactions, and recurring failures persist until the next human-driven update ships a fix. Self-evolving agents have emerged in response…
arXiv:2605.10787v2 Announce Type: replace Abstract: Current LLM agents are proficient at calling isolated APIs but struggle with the "last mile" of commercial software automation. In real-world scenarios, tools are not independent; they are atomic, interdependent, and prone to en…
arXiv:2605.07926v2 Announce Type: replace Abstract: As LLM-based agents increasingly rely on external tools, it is important to evaluate their ability to sustain tool-grounded reasoning beyond familiar workflows and short-range interactions. We introduce AgentEscapeBench, an esca…
arXiv:2605.20210v1 Announce Type: cross Abstract: Agentic AI systems - systems that can pursue goals through multi-step planning and tool-mediated action with limited direct supervision - are moving from experimental prototypes to enterprise deployments. This transition introduce…
arXiv:2605.20204v1 Announce Type: cross Abstract: LLM-based user simulation is the primary mechanism for end-to-end agent evaluation, yet simulated users are poor proxies for real humans: unconstrained LLM defaults produce a Formalism Ceiling (style match rates of 6-8% against re…
arXiv:2605.20190v1 Announce Type: new Abstract: Iterative industrial design-simulation optimization is bottlenecked by the CAD-CAE semantic gap: translating simulation feedback into valid geometric edits under diverse, coupled constraints. To fill this gap, we propose COSMO-Agent…
arXiv cs.AI
TIER_1English(EN)·Binghan Wu, Shoufeng Wang, Yunxin Liu, Ya-Qin Zhang, Joseph Sifakis, Ye Ouyang·
arXiv:2605.20608v1 Announce Type: new Abstract: Realizing Level 4/5 Autonomous Networks (AN) demands a shift from static automation to agent-native intelligence. Current operations, reliant on rigid scripts, lack the cognitive agency to handle off-nominal conditions. To address t…
arXiv:2602.02660v3 Announce Type: replace Abstract: A critical bottleneck in automating AI research is the execution of complex machine learning engineering (MLE) tasks. MLE differs from general software engineering due to computationally expensive evaluation (e.g., model trainin…
arXiv:2512.23292v3 Announce Type: replace Abstract: The prevailing paradigm in AI for physical systems (scaling general-purpose foundation models toward universal multimodal reasoning) confronts a fundamental barrier at the control interface. Recent benchmarks show that even fron…
arXiv cs.CL
TIER_1English(EN)·Asaf Yehudai, Lilach Eden, Michal Shmueli-Scheuer·
arXiv:2605.22608v1 Announce Type: new Abstract: Agentic systems are becoming more capable: agents define strategies, take actions, and interact with different environments. This autonomy poses serious challenges for overseeing and assessing agent behavior. Most current tools are …
arXiv:2605.21240v1 Announce Type: cross Abstract: LLM agents have shown strong performance across a wide range of complex tasks, including interactive environments that require long-horizon decision making. But these agents cannot learn on the fly at test time. Self-evolving agen…
arXiv:2605.20456v1 Announce Type: cross Abstract: Agentic AI coding systems can inspect repositories, plan implementation steps, edit files, call tools, run tests, and submit pull requests. These capabilities make software and hardware development faster in some settings, but cur…
arXiv cs.CL
TIER_1English(EN)·Mingkai Deng, Jinyu Hou, Lara S\'a Neves, Varad Pimpalkhute, Taylor W. Killian, Zhengzhong Liu, Eric P. Xing·
arXiv:2605.22138v1 Announce Type: cross Abstract: How should an agent decide when and how to plan? A dominant approach builds agents as reactive policies with adaptive computation (e.g., chain-of-thought), trained end-to-end expecting planning to emerge implicitly. Without contro…
arXiv cs.AI
TIER_1English(EN)·Aditya Taparia, Som Sagar, Ransalu Senanayake·
arXiv:2602.11574v3 Announce Type: replace Abstract: Configuring LLM-based agent systems involves choosing workflows, tools, token budgets, and prompts from a large combinatorial design space, and is typically handled today by fixed templates or hand-tuned heuristics that apply th…
arXiv:2605.20876v1 Announce Type: cross Abstract: Terminal agents extend Large Language Models with the ability to execute tasks directly in command-line environments, but their progress is bottlenecked by the scarcity of high-quality training data. Existing approaches bootstrap …
SkillOpt introduces a systematic text-space optimizer for agent skills that trains skills as external agent state with stable updates and zero deployment inference overhead, achieving superior performance across multiple benchmarks and execution environments.
Large language models now write software, draft legal documents, and produce clinical notes, yet fundamental limits, from Turing and Arrow to the No Free Lunch theorems, shape what computation can do. This thesis turns such impossibility results from curiosities into design rules…
Autonomous agentic systems are largely static after deployment: they do not learn from user interactions, and recurring failures persist until the next human-driven update ships a fix. Self-evolving agents have emerged in response, but all confine evolution to text-mutable artifa…
Self-evolving agents should not train on examples they cannot justify. Data-free self-evolving search agents offer a scalable route to systems that generate their own questions, answer them, and improve from their own feedback without human annotations. Yet, without verifiable ev…
LLM-powered AI agents require high-frequency state exploration (e.g., test-time tree search and reinforcement learning), relying on rapid checkpoint and rollback (C/R) of the complete sandbox state, including files and process state (e.g., memory, contexts, etc.). Existing mechan…
AI models are already deployed in societies affected by armed conflict, and journalists, humanitarian workers, governments and ordinary citizens rely on them for information or for their work processes. No established practice exists for checking whether their outputs can make th…
We present Claw AI Lab, a lab-native autonomous research platform that advances automated research from a hidden prompt-to-paper pipeline into an interactive AI laboratory. Rather than centering the system around a single agent or a fixed serial workflow, we allow users to instan…
Skills are increasingly used to package agent instructions, workflows, scripts, and reference materials. In enterprise settings, however, skills often need to express more than task guidance: they must make goals, input boundaries, permissions, evidence requirements, output contr…
Agentic systems are becoming more capable: agents define strategies, take actions, and interact with different environments. This autonomy poses serious challenges for overseeing and assessing agent behavior. Most current tools are limited, focusing on observability with basic ev…
We introduce TerminalWorld, a scalable data engine that automatically reverse-engineers high-fidelity evaluation tasks from "in-the-wild" terminal recordings. Processing 80,870 terminal recordings, the engine yields a full benchmark of 1,530 validated tasks, spanning 18 real-worl…
Agent orchestration frameworks have proliferated, collectively exceeding 290,000 GitHub stars across LangGraph, CrewAI, Google ADK, OpenAI Agents SDK, Semantic Kernel, Strands, and LlamaIndex. All follow the same pattern: an external orchestrator above the LLM, injecting instruct…
Don't Worry About the Vase (Zvi Mowshowitz)
TIER_1English(EN)·Zvi Mowshowitz·
Scientific evidence often spans instruments, databases, and disciplines, so no single source records the full phenomenon. This makes it difficult to determine when coordinated AI agents add value over simpler scientific workflows. We evaluate this question with a cross-domain ben…
How should an agent decide when and how to plan? A dominant approach builds agents as reactive policies with adaptive computation (e.g., chain-of-thought), trained end-to-end expecting planning to emerge implicitly. Without control over the presence, structure, or horizon of plan…
Recent development of agents has renewed demand for long-context reasoning capacity of LLMs. However, training LLMs for this capacity requires costly long-document curation or heuristic context synthesis. We observe that agents produce massive trajectories when solving problems, …
Efficient agentic reasoning requires decomposing decision-making into three systems—simulative reasoning, self-regulation, and reactive execution—enabling controlled planning that reduces token usage while maintaining performance.
Complex Verilog Design Problems (CVDP) challenge hardware LLM agents because solving them requires localizing verifier-relevant RTL, testbenches, include paths, and build dependencies inside large repository snapshots, making precise edits, and recovering from sparse hidden-verif…
LLM agents have shown strong performance across a wide range of complex tasks, including interactive environments that require long-horizon decision making. But these agents cannot learn on the fly at test time. Self-evolving agents address this by accumulating memory and reflect…
Terminal agents extend Large Language Models with the ability to execute tasks directly in command-line environments, but their progress is bottlenecked by the scarcity of high-quality training data. Existing approaches bootstrap from partial sources such as human-defined seeds o…
Realizing Level 4/5 Autonomous Networks (AN) demands a shift from static automation to agent-native intelligence. Current operations, reliant on rigid scripts, lack the cognitive agency to handle off-nominal conditions. To address this, this letter proposes a hierarchical multi-a…
Realizing Level 4/5 Autonomous Networks (AN) demands a shift from static automation to agent-native intelligence. Current operations, reliant on rigid scripts, lack the cognitive agency to handle off-nominal conditions. To address this, this letter proposes a hierarchical multi-a…
Large language model agents now act on codebases, browsers, operating systems, calendars, files, and tool ecosystems, but the benchmarks used to evaluate them are fragmented: each emphasizes a different unit of measurement (final task success, tool-call validity, repeated-pass co…
Agentic AI coding systems can inspect repositories, plan implementation steps, edit files, call tools, run tests, and submit pull requests. These capabilities make software and hardware development faster in some settings, but current evidence does not support the simple claim th…
Production LLM agents combine stochastic model outputs with deterministic software systems, yet the boundary between the two is rarely treated as a first-class architectural object. This paper names that boundary the stochastic-deterministic boundary (SDB): a four-part contract a…
We adapt split conformal prediction and adaptive conformal inference (ACI) to continuous AI agent evaluation, providing distribution-free coverage guarantees for forecasted quality scores. Conformal intervals achieve calibration error below 0.02 across all nominal levels at the 2…
We present OpenComputer, a verifier-grounded framework for constructing verifiable software worlds for computer-use agents. OpenComputer integrates four components: (1) app-specific state verifiers that expose structured inspection endpoints over real applications, (2) a self-evo…
Large Language Model (LLM) agents are increasingly applied to engineering design tasks, yet existing evaluation frameworks do not adequately address multi-agent systems that combine simulation, retrieval, and manufacturing preparation. We introduce a benchmark suite with three ev…
Equipping LLMs with tool-use capabilities via Agentic Reinforcement Learning (Agentic RL) is bottlenecked by two challenges: the lack of scalable, robust execution environments and the scarcity of realistic training data that captures implicit human reasoning. Existing approaches…
As LLM agents are increasingly built around reusable skills, a central challenge is no longer only whether agents can use provided skills, but whether they can generate correct, reusable, and executable skills from repositories and documents. Existing benchmarks primarily evaluat…
arXiv cs.AI
TIER_1English(EN)·Ronaldo Martins da Costa·
Legacy systems concentrate business rules, architectural decisions, and operational exceptions that often remain implicit in code, data, configuration, and maintenance practices. At the same time, language-model-based coding agents depend on reliable context, correctness criteria…
AI-assisted research is crossing a threshold: fully automated systems can now generate research papers for as little as $15, while long-horizon agents can execute experiments, draft manuscripts, and simulate critique with minimal human input. Yet this productivity frontier expose…
The bottleneck of useful agentic intelligence has shifted from compressing world knowledge into a single model to executing a coordinated system. This position paper argues that personal-agent architecture must move to the edge because the core properties of agentic intelligence …
Long-horizon LLM agents leave traces that could become reusable experience, but raw trajectories are noisy and hard to govern. We treat Agent Skills as an experience schema that couples executable scripts, with non-executable guidance on procedures. Yet open skill ecosystems cont…
Generalizable agents should adapt to diverse tasks and unseen environments beyond their training distribution. This position paper argues that such generalization requires environment scaling: expanding the distribution of executable rule-sets that agents interact with, rather th…
Deploying large language model (LLM) on edge device enables personalized LLM agents for various users. The growing availability of diverse personalized agents presents a unique opportunity for peer-to-peer (P2P) collaboration, wherein each user can delegate tasks beyond the local…
Deploying large language model (LLM) on edge device enables personalized LLM agents for various users. The growing availability of diverse personalized agents presents a unique opportunity for peer-to-peer (P2P) collaboration, wherein each user can delegate tasks beyond the local…
Multi-agent LLM workflows -- systems composed of multiple role-specific LLM calls -- often outperform single-prompt baselines, but they remain difficult to debug and refine. Failures can originate from subtle errors in intermediate outputs that propagate to downstream nodes, requ…
Although artificial intelligence (AI) now matches or exceeds human performance across numerous cognitive tasks, creativity remains a highly contested frontier. As AI systems based on large language models (LLMs) are increasingly adopted in research and innovation, it is essential…
Large language model (LLM)-based agents have demonstrated strong capabilities in complex reasoning and problem solving through multi-step interactions, yet most deployed agents remain behaviorally static, with knowledge acquired during execution rarely translating into systematic…
Agentic AI is rapidly proliferating across diverse real-world domains such as software engineering, yet public trust has not kept pace. The central reason is that responsibility, despite being widely discussed, remains a subjective and unenforced concept, as no current agentic fr…
arXiv cs.LG
TIER_1English(EN)·Sheila A. McIlraith·
We examine one particular dimension of AI governance: how to monitor and audit AI-enabled products and services throughout the AI development lifecycle, from pre-deployment testing to post-deployment auditing. Combining principles from formal methods with SoTA machine learning, w…
Large language model based agents often fail in unfamiliar environments due to premature exploitation: a tendency to act on prior knowledge before acquiring sufficient environment-specific information. We identify autonomous exploration as a critical yet underexplored capability …
Machine learning systems increasingly make life-changing decisions about individuals, such as loan approvals, hiring, and cheating detection, raising a pressing question: how can individuals respond to negative decisions made by these opaque systems? While explainable artificial …
AI agents are increasingly deployed to act autonomously in the world, yet there is still no reliable way to trace a harmful agent back to the account that deployed it. This creates the same accountability gap across both ends of the intent spectrum: benign operators may deploy mi…
Toward recursive self-improvement, we investigate LLM agents autonomously designing foundation models beyond standard Transformers. We introduce a dual-framework approach: AIRA-Compose for high-level architecture search, and AIRA-Design for low-level mechanistic implementation. A…
Coding agents are increasingly deployed in real software development, where a single version iteration requires months of coordinated work across many files. However, most existing benchmarks focus predominantly on single-issue bug fixes from Python repositories, with coarse pass…
Recent advances in Large Language Model (LLM) agents have enabled complex agentic workflows where models autonomously retrieve information, call tools, and reason over large corpora to complete tasks on behalf of users. Despite the growing adoption of retrieval-augmented generati…
Recent advances in Large Language Model (LLM) agents have enabled complex agentic workflows where models autonomously retrieve information, call tools, and reason over large corpora to complete tasks on behalf of users. Despite the growing adoption of retrieval-augmented generati…
Autonomous multi-agent systems based on large language models (LLMs) have demonstrated remarkable abilities in independently solving complex tasks in a wide breadth of application domains. However, these systems hit critical reasoning, coordination, and computational scaling bott…
Agentic modeling aims to transform LLMs into autonomous agents capable of solving complex tasks through planning, reasoning, tool use, and multi-turn interaction with environments. Despite major investment, open research remains constrained by infrastructure and training gaps. Ma…
GraphFlow is a visual workflow system designed to improve the reliability of agentic AI automation in multi-step, mission-critical processes. In these workflows, small errors compound rapidly: under an idealized model of independent steps, a ten-step process with 90% per-step rel…
AI agents execute complex multi-step processes, but current evaluation falls short: outcome metrics report success or failure without explaining why, and process-level approaches struggle to connect failure types to their precise locations within long, structured traces. We prese…
AI agents execute complex multi-step processes, but current evaluation falls short: outcome metrics report success or failure without explaining why, and process-level approaches struggle to connect failure types to their precise locations within long, structured traces. We prese…
MediaClaw is a multimodal agent platform built on the OpenClaw ecosystem. Its core design follows a three-layer architecture of unified abstraction, pluginized extension, and workflow orchestration. The system is intended to address practical deployment pain points in AIGC adopti…
ReAct has become the default architecture across LLM agents, and many existing web agents follow this paradigm. We argue that it is the wrong default for web agents. Instead, web agents should default to plan-then-execute: commit to a task-specific program before observing runtim…
Agentic evolution has emerged as a powerful paradigm for improving programs, workflows, and scientific solutions by iteratively generating candidates, evaluating them, and using feedback to guide future search. However, existing methods are typically instantiated either as fixed …
Foundation models have transformed automated code generation, yet autonomous software-engineering agents remain unreliable in realistic development settings. The dominant explanation locates this gap in model capability. We propose a different locus: software-engineering capabili…
Current interactive LLM agents rely on goal-conditioned stepwise planning, where environmental understanding is acquired reactively during execution rather than established beforehand. This temporal inversion leads to Delayed Environmental Perception: agents must infer environmen…
Agent benchmarks have become the de facto measure of frontier AI competence, guiding model selection, investment, and deployment. However, reward hacking, where agents maximize a score without performing the intended task, emerges spontaneously in frontier models without overfitt…
Computer Use Agents (CUAs) can act through both atomic GUI actions, such as click and type, and high-level tool calls, such as API-based file operations, but this hybrid action space often leaves them uncertain about when to continue with GUI actions or switch to tools, leading t…
Modern GUI agents typically rely on a model-centric and step-wise interaction paradigm, where LLMs must re-interpret the UI and re-decide actions at every screen, which is fragile in long-horizon tasks. In this paper, we propose Executable Agentic Memory (EAM), a structured Knowl…
Large language model (LLM) agents have increasingly advanced service applications, such as booking flight tickets. However, these service agents suffer from unreliability in long-horizon tasks, as they often produce policy violations, tool hallucinations, and misaligned actions, …
Terminal agents are increasingly capable of executing complex, long-horizon tasks autonomously from a single user prompt. To do so, they must interpret instructions encountered in the environment (e.g., README files, code comments, stack traces) and determine their relevance to t…
arXiv cs.AI
TIER_1English(EN)·Stefano V. Albrecht·
Reproducibility problems that have long affected machine learning and reinforcement learning are now surfacing in agent research: papers compare systems by reported scores while leaving the rollout records behind those scores difficult to inspect. For agentic tasks, this matters …
Deploying agentic AI in regulated contexts requires principled reasoning about two design dimensions: agency (what the system can do) and autonomy (how much it acts without human involvement). Though often treated independently, they are coupled: at higher autonomy, human error c…
Reusable skills are becoming a common interface for extending large language model agents, packaging procedural guidance with access to files, tools, memory, and execution environments. However, this modularity introduces attack surfaces that are largely missed by existing safety…
In this paper, we present AgentDisCo, a novel Disentangled and Collaborative agentic architecture that formulates deep research as an adversarial optimization problem between information exploration and exploitation. Unlike existing approaches that conflate these two processes in…
We introduce Shepherd, a functional programming model that formalizes meta-agent operations on target agents as functions, with core operations mechanized in Lean. Shepherd records every agent-environment interaction as a typed event in a Git-like execution trace, enabling any pa…
Large language and vision-language models increasingly power agents that act on a user's behalf through command-line interface (CLI) harnesses. However, most agent benchmarks still rely on synthetic sandboxes, short-horizon tasks, mock-service APIs, and final-answer checks, leavi…
The dominant paradigm for AI agents is an "on-the-fly" loop in which agents synthesize plans and execute actions within seconds or minutes in response to user prompts. We argue that this paradigm short-circuits disciplined software engineering (SE) processes -- iterative design, …
LLMs are increasingly deployed as autonomous agents with access to tools, databases, and external services, yet practitioners (across different sectors) lack systematic methods to assess how known threat classes translate into concrete risks within a specific agentic deployment. …
Artificial intelligence safety research focuses on aligning individual language models with human values, yet deployed AI systems increasingly operate as interacting populations where social influence may override individual alignment. Here we show that populations of individuall…
Benchmarks for coding agents increasingly measure source-level software repair, and cybersecurity benchmarks increasingly measure broad capture-the-flag performance. Classical binary reverse engineering remains less precisely specified: given only an executable, can an agent reco…
Agent-compiled knowledge bases provide persistent external knowledge for large language model (LLM) agents in open-ended, knowledge-intensive downstream tasks. Yet their quality is systematically limited by \emph{incompleteness}, \emph{incorrectness}, and \emph{redundancy}, manif…
Current large language model agent frameworks prioritize autonomy but lack the governability mechanisms required for enterprise deployment. High-risk write operations proceed without independent review, complex tasks lack acceptance verification, and computational resources are a…
Large Language Model (LLM)-based agents (e.g., OpenClaw) increasingly rely on reusable skill libraries to solve artifact-rich tasks such as document-centric workflows and data-intensive analysis. As these libraries grow, a few works have attempted to study the Retrieval-Augmented…
In this paper, we describe early work on a specification inference tool for the Move Prover that combines a weakest-precondition (WP) analysis over Move bytecode with an agentic coding CLI such as Claude Code. Specification inference reduces the boilerplate of writing specificati…
We present TraceFix, a verification-first pipeline for Large Language Model (LLM) multi-agent coordination. An agent synthesizes a protocol topology as a structured intermediate representation (IR) from a task description, generates PlusCal coordination logic, and iteratively rep…
We present Agentic Decentralized Knowledge Optimization (ADKO), a framework for collaborative black-box optimization across autonomous agents that achieves sample efficiency, privacy preservation, heterogeneous-objective handling, and communication efficiency. Each agent maintain…
Tool-integrated reasoning (TIR) is difficult to scale to small language models due to instability in long-horizon tool interactions and limited model capacity. While reinforcement learning methods like group relative policy optimization provide only sparse outcome-level rewards. …
While explicit reasoning trajectories enhance model interpretability, existing paradigms often rely on monolithic chains that lack intermediate verification, allowing early errors to cascade unchecked. This lack of modularity impedes granular auditing and compromises the epistemi…
arXiv:2605.06522v1 Announce Type: new Abstract: Foundation models (FMs) are increasingly deployed in open-world settings where distribution shift is the rule rather than the exception. The out-of-distribution (OOD) phenomena they face -- knowledge boundaries, capability ceilings,…
arXiv:2605.06472v1 Announce Type: new Abstract: LLM-based workflows compose specialized agents to execute complex tasks, and these agents usually share substantial context, allowing KV-Cache reuse to save computation. Existing approaches either manage KV-Cache at agent level and …
arXiv:2605.05861v1 Announce Type: new Abstract: Future networking systems are envisioned to become part of an agentic AI-native ecosystem in which a vast number of heterogeneous and specialized AI agents cooperate seamlessly to fulfill complex user requirements in real time. Howe…
arXiv:2605.06230v1 Announce Type: new Abstract: As large models evolve from conversational assistants into autonomous agents, challenges increasingly arise from long-horizon decision making, tool use, and real environment interaction. Existing agenticinfrastructure remain fragmen…
arXiv:2605.06365v1 Announce Type: new Abstract: Large language model systems are increasingly deployed as agentic workflows that interleave reasoning, tool use, memory, and iterative refinement. These systems are effective at producing answers, but they often rely on implicit con…
arXiv:2605.06434v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) have enabled workflows that generate SystemVerilog Assertions (SVAs) from natural-language specifications, with the potential to accelerate Formal Verification (FV). However, high-qual…
arXiv:2605.05400v1 Announce Type: cross Abstract: The rapid adoption of AI coding agents has produced a dominant workflow pattern -- often called "vibe coding" -- that prioritizes speed of implementation over deliberate preparation. We argue that this approach creates a systemati…
arXiv:2605.06136v1 Announce Type: cross Abstract: Most coding-agent benchmarks ask whether generated code behaves correctly. That remains essential, but repository-level engineering is increasingly agent-managed: one agent writes a repository, and later agents inspect, audit, or …
arXiv cs.AI
TIER_1English(EN)·Francesco Dente, Dario Satriani, Paolo Papotti·
arXiv:2605.06445v1 Announce Type: cross Abstract: Large Language Model (LLM) agents demonstrate strong performance in autonomous code generation under loose specifications. However, production-grade software requires strict adherence to structural constraints, such as architectur…
arXiv:2603.13131v2 Announce Type: replace Abstract: Long-horizon embodied intelligence requires agents to improve through interaction, not merely to execute plans generated from static goals. A central challenge is therefore to transform past executions into knowledge that can sh…
arXiv cs.AI
TIER_1English(EN)·Xi-Wei Pan, Shi-Wen An, Jin-Guo Liu·
arXiv:2604.11535v2 Announce Type: replace Abstract: Solving an NP-hard optimization problem often requires reformulating it for a specific solver -- quantum hardware, a commercial optimizer, or a domain heuristic. A tool for polynomial-time reductions between hard problems would …
arXiv cs.AI
TIER_1English(EN)·Wentao Zhang, Zhe Zhao, Haibin Wen, Yingcheng Wu, Cankun Guo, Ming Yin, Bo An, Mengdi Wang·
arXiv:2604.15034v3 Announce Type: replace Abstract: Recent advances in LLM based agent systems have shown promise in tackling complex, long horizon tasks. However, existing agent protocols (e.g., A2A and MCP) under specify cross entity lifecycle and context management, version tr…
arXiv:2605.06614v1 Announce Type: cross Abstract: LLM-based agents are increasingly deployed to handle streaming tasks, yet they often remain one-off problem solvers that fail to learn from past interactions. Reusable skills distilled from experience provide a natural substrate f…
arXiv cs.CL
TIER_1English(EN)·Xinglin Wang, Zishen Liu, Shaoxiong Feng, Peiwen Yuan, Yiwei Li, Jiayi Shi, Yueqi Zhang, Chuyi Tan, Ji Zhang, Boyuan Pan, Yao Hu, Kan Li·
arXiv:2605.06110v1 Announce Type: cross Abstract: Agentic systems increasingly solve complex user requests by executing orchestrated workflows, where subtasks are assigned to specialized models or tools and coordinated according to their dependencies. While recent work improves a…
arXiv cs.CL
TIER_1English(EN)·Erhan Zhang, Yiqun Chen, Zechun Niu, Wei Yang, Xiaochi Wei, Yan Gao, Yi Wu, Yao Hu, Jiaxin Mao·
arXiv:2604.03675v1 Announce Type: cross Abstract: In agentic search, large language models (LLMs) are trained to perform multi-turn retrieval and reasoning for complex tasks such as multi-hop question answering (QA). However, current search-based Reinforcement Learning (RL) metho…
arXiv:2508.15119v2 Announce Type: replace-cross Abstract: We introduce Open-Universe Assistance Games (OU-AGs), a formal framework extending assistance games to LLM-based agents. Effective assistance requires reasoning over human preferences that are unbounded, underspecified, an…
arXiv cs.LG
TIER_1English(EN)·Bole Ma, Jan Eitzinger, Harald K\"ostler·
arXiv:2605.05696v1 Announce Type: cross Abstract: Agentic LLM workloads put bit-identical tokens at shifted positions every turn, voiding prefix caches at the first byte of divergence. Operators report cache-hit regressions ranging from moderate slowdowns to severe TTFT spikes of…
arXiv:2605.05980v1 Announce Type: new Abstract: When language model agents tackle complex software engineering tasks, they often degrade over long trajectories, which we define as *agent drift*. We focus on two recurring failure modes *overthinking* and *overacting*, i.e., where …
LLM-based agents are increasingly deployed to handle streaming tasks, yet they often remain one-off problem solvers that fail to learn from past interactions. Reusable skills distilled from experience provide a natural substrate for self-evolution, where high-quality skill curati…
Foundation models (FMs) are increasingly deployed in open-world settings where distribution shift is the rule rather than the exception. The out-of-distribution (OOD) phenomena they face -- knowledge boundaries, capability ceilings, compositional shifts, and open-ended task varia…
LLM-based workflows compose specialized agents to execute complex tasks, and these agents usually share substantial context, allowing KV-Cache reuse to save computation. Existing approaches either manage KV-Cache at agent level and fail to exploit the reuse opportunities within w…
Large Language Model (LLM) agents demonstrate strong performance in autonomous code generation under loose specifications. However, production-grade software requires strict adherence to structural constraints, such as architectural patterns, databases, and object-relational mapp…
Recent advances in Large Language Models (LLMs) have enabled workflows that generate SystemVerilog Assertions (SVAs) from natural-language specifications, with the potential to accelerate Formal Verification (FV). However, high-quality assertion synthesis remains challenging beca…
Large language model systems are increasingly deployed as agentic workflows that interleave reasoning, tool use, memory, and iterative refinement. These systems are effective at producing answers, but they often rely on implicit conversational state, making it difficult to preser…
Agentic systems increasingly solve complex user requests by executing orchestrated workflows, where subtasks are assigned to specialized models or tools and coordinated according to their dependencies. While recent work improves agent efficiency by optimizing the performance--cos…
Agentic LLM workloads put bit-identical tokens at shifted positions every turn, voiding prefix caches at the first byte of divergence. Operators report cache-hit regressions ranging from moderate slowdowns to severe TTFT spikes of 10-16s on unchanged content. Prior position-indep…
arXiv:2605.04107v1 Announce Type: cross Abstract: Production agent frameworks (OpenAI Function Calling, Anthropic Tool Use, MCP) transmit tool schemas as JSON, a format designed for machine parsing, not for interpretation by language models. For small models (4B-14B), this protoc…
arXiv cs.AI
TIER_1English(EN)·Yipeng Ouyang, Yi Xiao, Yuhao Gu, Xianwei Zhang·
arXiv:2605.03353v1 Announce Type: cross Abstract: LLM-Agents have evolved into autonomous systems for complex task execution, with the SKILL.md specification emerging as a de facto standard for encapsulating agent capabilities. However, a critical bottleneck remains: different ag…
arXiv:2605.03952v1 Announce Type: cross Abstract: Coding agents often pass per-prompt safety review yet ship exploitable code when their tasks are decomposed into routine engineering tickets. The challenge is structural: existing safety alignment evaluates overt requests in isola…
arXiv:2604.14709v3 Announce Type: replace Abstract: Existing benchmarks for hardware design primarily evaluate Large Language Models (LLMs) on isolated, component-level tasks such as generating HDL modules from specifications, leaving repository-scale evaluation unaddressed. We i…
arXiv cs.AI
TIER_1English(EN)·Xue Qin, Simin Luan, John See, Cong Yang, Zhijun Li·
arXiv:2604.07039v2 Announce Type: replace-cross Abstract: Robotic systems lack a principled abstraction for organizing intelligence, capabilities, and execution in a unified manner. Existing approaches either couple skills within monolithic architectures or decompose functionalit…
arXiv:2605.03213v1 Announce Type: cross Abstract: Agentic AI systems, specifically LLM-driven agents that plan, invoke tools, maintain persistent memory, and delegate tasks to peer agents via protocols such as MCP and A2A, introduce a threat surface that differs materially from s…
arXiv cs.AI
TIER_1English(EN)·Raja Sekhar Rao Dheekonda, Will Pearce, Nick Landers·
arXiv:2605.04019v1 Announce Type: new Abstract: AI systems are entering critical domains like healthcare, finance, and defense, yet remain vulnerable to adversarial attacks. While AI red teaming is a primary defense, current approaches force operators into manual, library-specifi…
arXiv cs.AI
TIER_1English(EN)·Kishan Athrey, Ramin Pishehvar, Brian Riordan, Mahesh Viswanathan·
arXiv:2605.03986v1 Announce Type: new Abstract: Multi-Agent Systems (MAS) built using AI agents fulfill a variety of user intents that may be used to design and build a family of related applications. However, the creation of such MAS currently involves manual composition of the …
arXiv:2605.03675v1 Announce Type: new Abstract: Long-running autonomous AI agents suffer from a well-documented memory coherence problem: tool-execution success rates degrade 14 percentage points over 72-hour operation windows due to four compounding failure modes in existing fla…
arXiv cs.AI
TIER_1English(EN)·Srinath Perera, Kaviru Hapuarachchi, Frank Leymann, Rania Khalaf·
arXiv:2605.03409v1 Announce Type: new Abstract: We present Robust Agent Compensation (RAC), a log-based recovery paradigm (providing a safety net) implemented through an architectural extension that can be applied to most Agent frameworks to support reliable executions (avoiding …
arXiv:2605.03242v1 Announce Type: new Abstract: Tool-using agent systems powered by large language models (LLMs) are increasingly deployed across web, app, operating-system, and transactional environments. Yet existing safety benchmarks still emphasize explicit risks, potentially…
arXiv:2605.03195v1 Announce Type: new Abstract: Modern coding agents increasingly delegate specialized subtasks to subagents, which are smaller, focused agentic loops that handle narrow responsibilities like search, debugging or terminal execution. This architectural pattern keep…
arXiv cs.AI
TIER_1English(EN)·Reshabh K Sharma, Gaurav Mittal, Yu Hu·
arXiv:2605.03159v1 Announce Type: new Abstract: As autonomous agents become increasingly sophisticated, validating their sequential behavior presents a significant challenge. Traditional testing approaches require manual specification, exact sequence matching, or thousands of tra…
arXiv:2604.01496v2 Announce Type: replace-cross Abstract: We introduce SWE-ZERO to SWE-HERO, a two-stage SFT recipe that achieves state-of-the-art results on SWE-bench by distilling open-weight frontier LLMs. Our pipeline replaces resource-heavy dependencies with an evolutionary …
arXiv cs.AI
TIER_1English(EN)·Kiran Gopinathan, Jack Feser, Michelangelo Naim, Zenna Tavares, Eli Bingham·
arXiv:2605.03143v1 Announce Type: cross Abstract: Recent advances in large language models have led to the rise of software systems (i.e. agents) that execute with increasing autonomy on behalf of users in open, multi-party settings, interacting with untrusted counterparts and ma…
The rapid adoption of AI coding agents has produced a dominant workflow pattern -- often called "vibe coding" -- that prioritizes speed of implementation over deliberate preparation. We argue that this approach creates a systematic alignment problem: agents that lack sufficient c…
Driven by a rapid co-evolution of both harness and underlying models, LLM agents are improving at a dizzying pace. In our prior work (performed in Dec. 2025), we introduced "Design Conductor" (or just "Conductor"), a system capable of building a 5-stage Linux-capable RISC-V CPU i…
We evaluate an initial coding-agent system for ARC-AGI-3 in which the agent maintains an executable Python world model, verifies it against previous observations, refactors it toward simpler abstractions as a practical proxy for an MDL-like simplicity bias, and plans through the …
We evaluate an initial coding-agent system for ARC-AGI-3 in which the agent maintains an executable Python world model, verifies it against previous observations, refactors it toward simpler abstractions as a practical proxy for an MDL-like simplicity bias, and plans through the …
AI agents are increasingly deployed across diverse domains to automate complex workflows through long-horizon and high-stakes action executions. Due to their high capability and flexibility, such agents raise significant security and safety concerns. A growing number of real-worl…
Modern AI agents execute real-world side effects through tool calls such as file operations, shell commands, HTTP requests, and database queries. A single unsafe action, including accidental deletion, credential exposure, or data exfiltration, can cause irreversible harm. Existin…
Agent-repair leaderboards reorder under evaluator reconfiguration, and a measurable share of the reordering is produced by methods that consult evaluator-derived signal during internal selection of candidate repairs. We document this failure mode on a public leaderboard and relea…
arXiv:2604.25000v2 Announce Type: replace Abstract: Recent work has framed intelligence in verifiable tasks as reducing time-to-solution through learned structure and test-time search, while systems work has explored learned runtimes in which computation, memory and I/O migrate i…
arXiv:2605.01147v1 Announce Type: new Abstract: As large language models are increasingly deployed as interacting agents in high-stakes decisions, the AI safety community assumes that safety properties of individual models will compose into safe multi-agent behavior. This positio…
arXiv cs.AI
TIER_1English(EN)·Florian Valentin Wunderlich, Lars Benedikt Kaesberg, Jan Philip Wahle, Terry Ruas, Bela Gipp·
arXiv:2605.01566v1 Announce Type: new Abstract: Advances in inference methods have enabled language models to improve their predictions without additional training. These methods often prioritize raw performance over cost-effective compute usage. However, computational efficiency…
arXiv cs.AI
TIER_1Nederlands(NL)·Qisong Zhang (School of Artificial Intelligence, Beijing University of Posts and Telecommunications), Wenzhuo Wu (School of Artificial Intelligence, Beijing University of Posts and Telecommunications), Zhuangzhuang Jia (School of Artificial Intelligence, ·
arXiv:2605.01789v1 Announce Type: new Abstract: Constructing controllable visual data is a major bottleneck for image editing and multimodal understanding. Useful supervision is rarely produced by a single rendering pass; instead it emerges through iterative generation, inspectio…
arXiv:2605.02503v1 Announce Type: new Abstract: Evaluating autonomous data analysis agents requires testing their ability to perform exploratory analysis in underexplored data environments. However, many existing benchmarks emphasize final answer accuracy in prior-guided data set…
arXiv cs.AI
TIER_1English(EN)·Vincent Henkel, Felix Gehlhoff, David Kube, Asaad Almutareb, Luis Cruz, Bernd Hellingrath, Philip Koch, Christoph Legat, Florian Mohr, Michael Oberle, Felix Ocker, Thorsten Schoeler, Mario Thron, Nico Andre T\"opfer, Lucas Vogt, Yuchen Xia·
arXiv:2605.02592v1 Announce Type: new Abstract: Foundation models, particularly large language models, are increasingly integrated into agent architectures for industrial tasks such as decision support, process monitoring, and engineering automation. Yet evidence on their purpose…
arXiv:2605.02728v1 Announce Type: new Abstract: This paper presents ORPilot, an open-source agentic AI system that translates real-world business problems into solver-ready optimization models. Unlike academic LLM-for-OR tools that assume clean problem specifications with preform…
arXiv:2605.01394v1 Announce Type: cross Abstract: Formal specification is essential for rigorous program verification, yet writing correct specifications remains costly and difficult to automate. Although large language models (LLMs) and agents have shown promising progress, thei…
arXiv:2605.01471v1 Announce Type: cross Abstract: Maintaining reliable UI test suites in large-scale enterprise applications is a persistent and costly challenge. We present an industrial case study of a multi-agent autonomous testing system evaluated using anonymized execution d…
arXiv:2605.01740v1 Announce Type: cross Abstract: An agentic-AI runtime issues tool calls, sends messages, and actuates devices on behalf of an LLM. Catching the four ways an action can diverge from its audit record -- F1 gate-bypass, F2 audit-forgery, silent host failure, F4 wro…
arXiv:2605.02244v1 Announce Type: cross Abstract: Frontier software engineering agents have saturated short-horizon benchmarks while regressing on the work that constitutes senior engineering: long-horizon, multi-engineer, ambiguous-specification deliverables. This paper takes a …
arXiv:2605.02584v1 Announce Type: cross Abstract: Agentic AI will be an essential enabling technology for designing future mobile communication systems, which could provide flexible and customized services, automate complex network operations, and drive autonomous decision-making…
arXiv cs.AI
TIER_1English(EN)·Yuecai Zhu, Nikolaos Tsantalis, Peter C. Rigby·
arXiv:2605.02741v1 Announce Type: cross Abstract: The promise of Large Language Models in automated software engineering is often measured by functional correctness, overlooking the critical issue of long term maintainability. This paper presents a systematic audit of technical d…
arXiv:2505.16120v2 Announce Type: replace Abstract: The emergence of Large Language Models (LLMs) has reshaped agent systems. Unlike traditional rule-based agents with limited task scope, LLM-powered agents offer greater flexibility, cross-domain reasoning, and natural language i…
arXiv:2510.12218v2 Announce Type: replace Abstract: Current approaches rely on zero-shot evaluation due to the absence of training data; while proprietary models such as GPT-4 exhibit strong reasoning capabilities, smaller open-source models remain ineffective at complex tool use…
arXiv cs.AI
TIER_1English(EN)·Bowen Ye, Rang Li, Qibin Yang, Yuanxin Liu, Linli Yao, Hanglong Lv, Zhihui Xie, Chenxin An, Lei Li, Lingpeng Kong, Qi Liu, Zhifang Sui, Tong Yang·
arXiv:2604.06132v2 Announce Type: replace Abstract: Large language models are increasingly deployed as autonomous agents for multi-step workflows in real-world software environments. However, existing agent benchmarks are limited by trajectory-opaque grading, underspecified safet…
arXiv cs.AI
TIER_1English(EN)·Zhensu Sun, Haotian Zhu, Bowen Xu, Xiaoning Du, Li Li, David Lo·
arXiv:2408.01055v2 Announce Type: replace-cross Abstract: Self-healing systems have long been a focus of research, aiming to enable software to recover from unexpected runtime errors without human intervention. Traditional approaches rely on predefined heuristic rules, such as re…
arXiv cs.AI
TIER_1English(EN)·Jia Li, Yuxin Su, Michael R. Lyu·
arXiv:2601.03731v3 Announce Type: replace-cross Abstract: As large language models (LLMs) evolve into autonomous agents, evaluating repository-level reasoning, the ability to maintain logical consistency across massive, real-world, interdependent file systems, has become critical…
arXiv:2603.00822v2 Announce Type: replace-cross Abstract: As Large Language Model (LLM) agents increasingly execute complex, autonomous software engineering tasks, developers rely on natural language instruction files such as AGENTS.md to express project-specific coding conventio…
arXiv:2605.02964v1 Announce Type: new Abstract: Reinforcement learning (RL) trained language model agents with tool access are increasingly deployed in coding assistants, research tools, and autonomous systems. We introduce the Reward Hacking Benchmark (RHB), a suite of multi-ste…
arXiv:2605.02910v1 Announce Type: cross Abstract: Recent advances in large language models have led to strong performance on reasoning and environment-interaction tasks, yet their ability for creative problem-solving remains underexplored. We study this capability through the len…
arXiv:2605.03596v1 Announce Type: cross Abstract: Workspace learning requires AI agents to identify, reason over, exploit, and update explicit and implicit dependencies among heterogeneous files in a worker's workspace, enabling them to complete both routine and advanced tasks ef…
arXiv cs.LG
TIER_1English(EN)·Chandan Singh, Yan Shuo Tan, Weijia Xu, Zelalem Gero, Weiwei Yang, Michel Galley, Jianfeng Gao·
arXiv:2605.03808v1 Announce Type: cross Abstract: Agentic data science (ADS) systems are rapidly improving their capability to autonomously analyze, fit, and interpret data, potentially moving towards a future where agents conduct the vast majority of data-science work. However, …
arXiv:2510.08952v4 Announce Type: replace Abstract: Text-attributed graphs (TAGs) have become a key form of graph-structured data in modern data management and analytics, combining structural relationships with rich textual semantics for diverse applications. However, the effecti…
arXiv:2605.03838v1 Announce Type: new Abstract: We introduce TRACE, a cross-domain engineering framework for trustworthy agentic AI in operationally critical domains. TRACE combines a four-layer reference architecture with an explicit classical-ML vs. LLM-validator split (L2a/L2b…
arXiv:2605.03228v1 Announce Type: cross Abstract: As large language model (LLM)-powered agents are increasingly deployed to perform complex, real-world tasks, they face a growing class of attacks that exploit extended user-agent-environment interactions to pursue malicious object…
arXiv cs.CL
TIER_1English(EN)·Yuwen Du, Rui Ye, Shuo Tang, Keduan Huang, Xinyu Zhu, Yuzhu Cai, Siheng Chen·
arXiv:2605.04036v1 Announce Type: cross Abstract: Deep search capabilities have become an indispensable competency for frontier Large Language Model (LLM) agents, yet their development remains dominated by industrial giants. The typical industry recipe involves a highly resource-…
arXiv cs.CL
TIER_1English(EN)·Hung Tran, Langston Nashold, Rayan Krishnan, Antoine Bigeard, Alex Gu·
arXiv:2603.04601v2 Announce Type: replace-cross Abstract: Code generation has emerged as one of AI's highest-impact use cases, yet existing benchmarks measure isolated tasks rather than the complete "zero-to-one" process of building a working application from scratch. We introduc…
Deep search capabilities have become an indispensable competency for frontier Large Language Model (LLM) agents, yet their development remains dominated by industrial giants. The typical industry recipe involves a highly resource-intensive pipeline spanning pre-training, continua…
AI systems are entering critical domains like healthcare, finance, and defense, yet remain vulnerable to adversarial attacks. While AI red teaming is a primary defense, current approaches force operators into manual, library-specific workflows. Operators spend weeks hand-crafting…
Multi-Agent Systems (MAS) built using AI agents fulfill a variety of user intents that may be used to design and build a family of related applications. However, the creation of such MAS currently involves manual composition of the plan, manual selection of appropriate agents, an…
Coding agents often pass per-prompt safety review yet ship exploitable code when their tasks are decomposed into routine engineering tickets. The challenge is structural: existing safety alignment evaluates overt requests in isolation, leaving models blind to malicious end-states…
We introduce TRACE, a cross-domain engineering framework for trustworthy agentic AI in operationally critical domains. TRACE combines a four-layer reference architecture with an explicit classical-ML vs. LLM-validator split (L2a/L2b), a stateful orchestration-and-escalation polic…
We introduce TRACE, a cross-domain engineering framework for trustworthy agentic AI in operationally critical domains. TRACE combines a four-layer reference architecture with an explicit classical-ML vs. LLM-validator split (L2a/L2b), a stateful orchestration-and-escalation polic…
Agentic data science (ADS) systems are rapidly improving their capability to autonomously analyze, fit, and interpret data, potentially moving towards a future where agents conduct the vast majority of data-science work. However, current ADS systems use statistical tools designed…
Long-running autonomous AI agents suffer from a well-documented memory coherence problem: tool-execution success rates degrade 14 percentage points over 72-hour operation windows due to four compounding failure modes in existing flat-file memory systems. We present MEMTIER, a tri…
Workspace learning requires AI agents to identify, reason over, exploit, and update explicit and implicit dependencies among heterogeneous files in a worker's workspace, enabling them to complete both routine and advanced tasks effectively. Despite its importance, existing releva…
arXiv cs.LG
TIER_1English(EN)·Kyle Zheng, Han Zhang, Renliang Sun, Chenchen Ye, Wei Wang·
arXiv:2605.02411v1 Announce Type: cross Abstract: A semantic gap separates how users describe tasks from how tools are documented. As API ecosystems scale to tens of thousands of endpoints, static retrieval from the initial query alone cannot bridge this gap: the agent's understa…
arXiv:2605.00314v1 Announce Type: cross Abstract: An agent skill is a configuration package that equips an LLM-driven agent with a concrete capability, such as reading email, executing shell commands, or signing blockchain transactions. Each skill is a hybrid artifact-a structure…
arXiv:2605.00424v1 Announce Type: cross Abstract: Agent skills -- structured packages of instructions, scripts, and references that augment a large language model (LLM) without modifying the model itself -- have moved from convenience to first-class deployment artifact. The runti…
arXiv cs.AI
TIER_1English(EN)·Bin Lei, Weitai Kang, Zijian Zhang, Winson Chen, Xi Xie, Shan Zuo, Mimi Xie, Ali Payani, Mingyi Hong, Yan Yan, Caiwen Ding·
arXiv:2505.10887v3 Announce Type: replace Abstract: This paper introduces \textsc{InfantAgent-Next}, a generalist agent capable of interacting with computers in a multimodal manner, encompassing text, images, audio, and video. Unlike existing approaches that either build intricat…
arXiv:2602.05353v3 Announce Type: replace-cross Abstract: Large Language Models have shown strong capabilities in complex problem solving, yet many agentic systems remain difficult to interpret and control due to opaque internal workflows. While some frameworks offer explicit arc…
arXiv:2602.22480v2 Announce Type: replace-cross Abstract: An important emerging application of coding agents is agent optimization: the iterative improvement of a target agent through edit-execute-evaluate cycles. Despite its relevance, the community lacks a systematic understand…
As large language model (LLM)-powered agents are increasingly deployed to perform complex, real-world tasks, they face a growing class of attacks that exploit extended user-agent-environment interactions to pursue malicious objectives improbable in single-turn settings. Such long…
The promise of Large Language Models in automated software engineering is often measured by functional correctness, overlooking the critical issue of long term maintainability. This paper presents a systematic audit of technical debt in AI-generated software, revealing that AI do…
This paper presents ORPilot, an open-source agentic AI system that translates real-world business problems into solver-ready optimization models. Unlike academic LLM-for-OR tools that assume clean problem specifications with preformatted inline data, ORPilot is designed for produ…
Foundation models, particularly large language models, are increasingly integrated into agent architectures for industrial tasks such as decision support, process monitoring, and engineering automation. Yet evidence on their purposes, capabilities, and limitations remains fragmen…
Foundation models, particularly large language models, are increasingly integrated into agent architectures for industrial tasks such as decision support, process monitoring, and engineering automation. Yet evidence on their purposes, capabilities, and limitations remains fragmen…
Agentic AI will be an essential enabling technology for designing future mobile communication systems, which could provide flexible and customized services, automate complex network operations, and drive autonomous decision-making across the network. This work studies how Large L…
Evaluating autonomous data analysis agents requires testing their ability to perform exploratory analysis in underexplored data environments. However, many existing benchmarks emphasize final answer accuracy in prior-guided data settings and provide limited support for reasoning …
A semantic gap separates how users describe tasks from how tools are documented. As API ecosystems scale to tens of thousands of endpoints, static retrieval from the initial query alone cannot bridge this gap: the agent's understanding of what it needs evolves during execution, b…
A semantic gap separates how users describe tasks from how tools are documented. As API ecosystems scale to tens of thousands of endpoints, static retrieval from the initial query alone cannot bridge this gap: the agent's understanding of what it needs evolves during execution, b…
arXiv:2605.00334v1 Announce Type: cross Abstract: Production agentic systems make many model calls per user request, and most of those calls are short, structured, and routine. This raises a practical routing question that existing evaluations do not directly answer: which parts …
arXiv:2603.25719v2 Announce Type: replace-cross Abstract: We present an empirical study of how far general-purpose coding agents -- without hardware-specific training -- can optimize hardware designs from high-level algorithmic specifications. We introduce an agent factory, a two…
arXiv cs.LG
TIER_1English(EN)·Jan Ole Ernst, Dmitri Michelangelo Saberi, Derek Christ, Thomas Zimmermann, Rajath Salegame, Suhaas M. Bhat, Stanislav Levental, Thomas Dybdahl Ahle, Matthias Jung·
arXiv:2605.00058v1 Announce Type: cross Abstract: The primary goal of Design Verification (DV) is to ensure that a proposed chip design implementation (either in code, or physical form) exactly matches its specification and is free of functional errors in order to avoid costly re…
arXiv cs.LG
TIER_1English(EN)·Dongxin Guo, Jikun Wu, Siu Ming Yiu·
arXiv:2605.00528v1 Announce Type: cross Abstract: AI agents execute tens to hundreds of chained LLM calls per task, yet GPU schedulers treat each call as independent, discarding gigabytes of intermediate state between steps and inflating end-to-end latency by 3-8x. We argue that …
arXiv cs.LG
TIER_1English(EN)·Zexi Liu, Jingyi Chai, Xinyu Zhu, Shuo Tang, Rui Ye, Bo Zhang, Lei Bai, Siheng Chen·
arXiv:2505.23723v2 Announce Type: replace-cross Abstract: The emergence of large language model (LLM)-based agents has significantly advanced the development of autonomous machine learning (ML) engineering. However, the dominant prompt-based paradigm exhibits limitations: smaller…
AI agents execute tens to hundreds of chained LLM calls per task, yet GPU schedulers treat each call as independent, discarding gigabytes of intermediate state between steps and inflating end-to-end latency by 3-8x. We argue that this request-level abstraction is fundamentally mi…
Agent skills -- structured packages of instructions, scripts, and references that augment a large language model (LLM) without modifying the model itself -- have moved from convenience to first-class deployment artifact. The runtime that loads them inherits the same problem packa…
arXiv cs.AI
TIER_1English(EN)·Chenxin Li, Zhengyang Tang, Huangxin Lin, Yunlong Lin, Shijue Huang, Shengyuan Liu, Bowen Ye, Rang Li, Lei Li, Benyou Wang, Yixuan Yuan·
arXiv:2604.28139v1 Announce Type: cross Abstract: LLM agents are expected to complete end-to-end units of work across software tools, business services, and local workspaces. Yet many agent benchmarks freeze a curated task set at release time and grade mainly the final response, …
arXiv cs.AI
TIER_1English(EN)·Simon Dennis, Michael Diamond, Rivaan Patil, Kevin Shabahang, Hao Guo·
arXiv:2604.27891v1 Announce Type: new Abstract: Agent orchestration frameworks -- LangGraph, CrewAI, Google ADK, OpenAI Agents SDK, and others -- place an external orchestrator above the LLM, tracking state and injecting routing instructions at every turn. We present a controlled…
arXiv:2604.09718v2 Announce Type: cross Abstract: LLM-driven web agents operating through continuous inference loops -- repeatedly querying a model to evaluate browser state and select actions -- exhibit a fundamental scalability constraint for repetitive tasks. We characterize t…
arXiv:2604.28138v1 Announce Type: cross Abstract: Autonomous agents act through sandboxed containers and microVMs whose state spans filesystems, processes, and runtime artifacts. Checkpoint and restore (C/R) of this state is needed for fault tolerance, spot execution, RL rollout …
arXiv cs.AI
TIER_1(AF)·Marco Robol, Paolo Giorgini·
arXiv:2604.27264v1 Announce Type: cross Abstract: Autonomous agents can adapt their behaviour to changing environments, but remain bound to requirements, goals, and capabilities fixed at design time, preventing genuine software evolution. This paper introduces self-evolving softw…
arXiv:2508.13024v3 Announce Type: replace Abstract: LLM-based web agents have the potential to automate long-running web tasks, such as searching for products in multiple e-shops and subsequently ordering the cheapest products that meet the users needs. Benchmarks for evaluating …
Production agentic systems make many model calls per user request, and most of those calls are short, structured, and routine. This raises a practical routing question that existing evaluations do not directly answer: which parts of an agent workflow truly require large frontier …
An agent skill is a configuration package that equips an LLM-driven agent with a concrete capability, such as reading email, executing shell commands, or signing blockchain transactions. Each skill is a hybrid artifact-a structured half declares executable interfaces, while a pro…
LLM agents are expected to complete end-to-end units of work across software tools, business services, and local workspaces. Yet many agent benchmarks freeze a curated task set at release time and grade mainly the final response, making it difficult to evaluate agents against evo…
Autonomous agents act through sandboxed containers and microVMs whose state spans filesystems, processes, and runtime artifacts. Checkpoint and restore (C/R) of this state is needed for fault tolerance, spot execution, RL rollout branching, and safe rollback-yet existing approach…
Agent orchestration frameworks -- LangGraph, CrewAI, Google ADK, OpenAI Agents SDK, and others -- place an external orchestrator above the LLM, tracking state and injecting routing instructions at every turn. We present a controlled comparison showing that for procedural tasks, t…
arXiv:2511.02399v2 Announce Type: replace-cross Abstract: Recent advances in large language model agents offer the promise of automating end-to-end software development from natural language requirements. However, existing approaches largely adopt linear, waterfall-style pipeline…
arXiv:2604.26102v1 Announce Type: cross Abstract: Large language model agents have achieved remarkable progress on software engineering tasks, yet current approaches suffer from a fundamental context coupling problem: the standard code editing interface conflates code inspection,…
arXiv cs.AI
TIER_1English(EN)·Tarlan Hasanli, Shahbaz Siddeeq, Bishwash Khanal, Pyry Kotilainen, Tommi Mikkonen, Pekka Abrahamsson·
arXiv:2604.26615v1 Announce Type: cross Abstract: Large language models (LLMs) accelerate software development but often exhibit instability, non-determinism, and weak adherence to development discipline in unconstrained workflows. While test-driven development (TDD) provides a s…
arXiv:2602.20426v2 Announce Type: replace Abstract: While most efforts to improve LLM-based tool-using agents focus on the agent itself - through larger models, better prompting, or fine-tuning - agent performance increasingly plateaus due to the quality of the tool interfaces th…
Large language models (LLMs) accelerate software development but often exhibit instability, non-determinism, and weak adherence to development discipline in unconstrained workflows. While test-driven development (TDD) provides a structured Red-Green-Refactor process, existing LLM…
Large language models (LLMs) accelerate software development but often exhibit instability, non-determinism, and weak adherence to development discipline in unconstrained workflows. While test-driven development (TDD) provides a structured Red-Green-Refactor process, existing LLM…
arXiv cs.CL
TIER_1English(EN)·Xinming Tu (Minta), Tianze Wang (Minta), Yingzhou (Minta), Lu, Kexin Huang, Yuanhao Qu, Sara Mostafavi·
arXiv:2604.24955v1 Announce Type: new Abstract: As benchmarks grow in complexity, many apparent agent failures are not failures of the agent at all - they are failures of the benchmark itself: broken specifications, implicit assumptions, and rigid evaluation scripts that penalize…
arXiv:2604.25135v1 Announce Type: new Abstract: Large Language Models are being increasingly deployed as the decision-making core of autonomous agents capable of effecting change in external environments. Yet, in conversational benchmarks, which simulate real-world customer-centr…
arXiv cs.CL
TIER_1English(EN)·Lawrence Keunho Jang, Jing Yu Koh, Daniel Fried, Ruslan Salakhutdinov·
arXiv:2604.24964v1 Announce Type: cross Abstract: Existing web agent benchmarks have largely converged on short, single-site tasks that frontier models are approaching saturation on. However, real world web use consists of long-horizon, multi-site workflows. Common web navigation…
arXiv cs.CL
TIER_1English(EN)·Shuyang Liu, Saman Dehghan, Jatin Ganhotra, Martin Hirzel, Reyhaneh Jabbarvand·
arXiv:2604.12147v2 Announce Type: replace-cross Abstract: Agents aspire to eliminate the need for task-specific prompt crafting through autonomous reason-act-observe loops. Still, they are commonly instructed to follow a task-specific plan for guidance, e.g., to resolve software …
arXiv cs.CL
TIER_1English(EN)·Hubert M. Pysklo, Artem Zhuravel, Patrick D. Watson·
arXiv:2602.11224v3 Announce Type: replace-cross Abstract: We present Agent-Diff, a novel benchmarking framework for evaluating agentic Large Language Models (LLMs) on real-world productivity software API tasks via code execution. Agentic LLM performance varies due to differences …
arXiv cs.CL
TIER_1English(EN)·Jiahang Lin, Shichun Liu, Chengjun Pan, Lizhi Lin, Shihan Dou, Xuanjing Huang, Hang Yan, Zhenhua Han, Tao Gui·
arXiv:2604.25850v1 Announce Type: new Abstract: Harnesses have become a central determinant of coding-agent performance, shaping how models interact with repositories, tools, and execution environments. Yet automating harness engineering is hard: a heterogeneous action space, spa…
Large language model agents have achieved remarkable progress on software engineering tasks, yet current approaches suffer from a fundamental context coupling problem: the standard code editing interface conflates code inspection, modification planning, and edit execution within …
Harnesses have become a central determinant of coding-agent performance, shaping how models interact with repositories, tools, and execution environments. Yet automating harness engineering is hard: a heterogeneous action space, sparse and noisy evaluation signal, multi-million-t…
Harnesses have become a central determinant of coding-agent performance, shaping how models interact with repositories, tools, and execution environments. Yet automating harness engineering is hard: a heterogeneous action space, sparse and noisy evaluation signal, multi-million-t…
Instructed code editing is a significant challenge for large language models (LLMs). On the EditBench benchmark, 39 of 40 evaluated models obtain a task success rate (TSR) below 60 percent, highlighting a gap between general code generation and the ability to perform instruction-…
Instructed code editing is a significant challenge for large language models (LLMs). On the EditBench benchmark, 39 of 40 evaluated models obtain a task success rate (TSR) below 60 percent, highlighting a gap between general code generation and the ability to perform instruction-…
arXiv:2604.17745v2 Announce Type: replace Abstract: Recent advances in large language models have highlighted their potential to automate computational research, particularly reproducing experimental results. However, existing approaches still use fixed sequential agent pipelines…
arXiv cs.CL
TIER_1English(EN)·Rikuto Kotoge, Mai Nishimura, Jiaxin Ma·
arXiv:2508.20324v4 Announce Type: replace Abstract: Reinforcement Learning has emerged as a dominant post-training approach to elicit agentic RAG behaviors such as search and planning from language models. Despite its success with larger models, applying RL to compact models (e.g…
arXiv cs.CL
TIER_1English(EN)·Jordan Meadows, Lan Zhang, Andre Freitas·
arXiv:2604.23002v1 Announce Type: cross Abstract: Formalising informal mathematical reasoning into formally verifiable code is a significant challenge for large language models. In scientific fields such as physics, domain-specific machinery (\textit{e.g.} Dirac notation, vector …
arXiv cs.CL
TIER_1English(EN)·Aishwarya Padmakumar, Leon Derczynski, Traian Rebedea, Christopher Parisien·
arXiv:2604.23067v1 Announce Type: cross Abstract: Automated methods for red teaming LLMs are an important tool to identify LLM vulnerabilities that may not be covered in static benchmarks, allowing for more thorough probing. They can also adapt to each specific LLM to discover we…
arXiv:2604.23088v1 Announce Type: cross Abstract: We present Code Broker, a multi agent system built with Google Agent Development Kit ADK that analyses Python code from files, local directories, or GitHub repositories and generates actionable quality assessment reports. The syst…
arXiv:2604.09388v2 Announce Type: replace-cross Abstract: AI coding tools are widely adopted, but most teams plateau at prompt-and-review without a framework for systematic progression. This paper presents the AI Codebase Maturity Model (ACMM), a 6-level framework describing how …
arXiv:2604.05013v2 Announce Type: replace-cross Abstract: Current LLM coding agents are predominantly trained on composite benchmarks (e.g., bug fixing), which often leads to task-specific overfitting and limited generalization. To address this, we propose a novel scaling paradig…
arXiv cs.AI
TIER_1English(EN)·Luay Gharzeddine, Samer Saab Jr·
arXiv:2604.22820v1 Announce Type: cross Abstract: Long-horizon tool-using tasks sometimes benefit from revisiting earlier subtasks for recovery and exploration, but added multi-agent workflow flexibility can also introduce coordination overhead and substantial inference cost. We …
arXiv cs.AI
TIER_1English(EN)·Chenyang An, Qihao Ye, Minghao Pan, Jiayaun Zhang·
arXiv:2604.24021v1 Announce Type: new Abstract: We explore a central question in AI for mathematics: can AI systems produce original, nontrivial proofs for open research problems? Despite strong benchmark performance, producing genuinely novel proofs remains an outstanding challe…
arXiv:2604.24658v1 Announce Type: new Abstract: Scientific publication compresses a branching, iterative research process into a linear narrative, discarding the majority of what was discovered along the way. This compilation imposes two structural costs: a Storytelling Tax, wher…
arXiv cs.LG
TIER_1English(EN)·Zhiyuan Zhai, Ming Li, Xin Wang·
arXiv:2604.23283v1 Announce Type: new Abstract: Current LLM agents operate under an implicit but universal assumption: execution is a transaction -- the user submits a request, the agent works in isolation, and only upon completion does the dialogue resume. This forces users into…
arXiv:2603.21362v2 Announce Type: replace-cross Abstract: LLM-as-Judge evaluation fails agent tasks because a fixed rubric cannot capture what matters for this task: code debugging demands Correctness and Error Handling; web navigation demands Goal Alignment and Action Efficiency…
arXiv cs.CL
TIER_1English(EN)·Yuhang Wang, Yuling Shi, Mo Yang, Rongrui Zhang, Shilin He, Heng Lian, Yuting Chen, Siyu Ye, Kai Cai, Xiaodong Gu·
arXiv:2601.16746v3 Announce Type: replace-cross Abstract: LLM agents have demonstrated remarkable capabilities in software development, but their performance is hampered by long interaction contexts, which incur high API costs and latency. While various context compression approa…
Large Language Models are being increasingly deployed as the decision-making core of autonomous agents capable of effecting change in external environments. Yet, in conversational benchmarks, which simulate real-world customer-centric issue resolution scenarios, these agents freq…
Existing web agent benchmarks have largely converged on short, single-site tasks that frontier models are approaching saturation on. However, real world web use consists of long-horizon, multi-site workflows. Common web navigation tasks, such as comparing products across differen…
As benchmarks grow in complexity, many apparent agent failures are not failures of the agent at all - they are failures of the benchmark itself: broken specifications, implicit assumptions, and rigid evaluation scripts that penalize valid alternative approaches. We propose employ…
Scientific publication compresses a branching, iterative research process into a linear narrative, discarding the majority of what was discovered along the way. This compilation imposes two structural costs: a Storytelling Tax, where failed experiments, rejected hypotheses, and t…
arXiv cs.CL
TIER_1English(EN)·Longju Bai, Zhemin Huang, Xingyao Wang, Jiao Sun, Rada Mihalcea, Erik Brynjolfsson, Alex Pentland, Jiaxin Pei·
arXiv:2604.22750v1 Announce Type: new Abstract: The wide adoption of AI agents in complex human workflows is driving rapid growth in LLM token consumption. When agents are deployed on tasks that require a significant amount of tokens, three questions naturally arise: (1) Where do…
The wide adoption of AI agents in complex human workflows is driving rapid growth in LLM token consumption. When agents are deployed on tasks that require a significant amount of tokens, three questions naturally arise: (1) Where do AI agents spend the tokens? (2) Which models ar…
AI coding assistants have proliferated rapidly, yet structured pedagogical frameworks for learning these tools remain scarce. Developers face a gap between tool documentation and practical mastery, relying on fragmented resources such as blog posts, video tutorials, and trial-and…
Don't Worry About the Vase (Zvi Mowshowitz)
TIER_1English(EN)·Zvi Mowshowitz·
As we all try to figure out what Mythos means for us down the line, the world of practical agentic coding continues, with the latest array of upgrades.
METR (Model Evaluation & Threat Research)
TIER_1Español(ES)·
<p>Cada vez más, los sistemas de IA “razonan” en texto antes de producir su respuesta final.<sup id="fnref:1"><a class="footnote" href="#fn:1" rel="footnote">1</a></sup> <sup id="fnref:2"><a class="footnote" href="#fn:2" rel="footnote">2</a></sup> <sup id="fnref:3"><a class="foot…
METR (Model Evaluation & Threat Research)
TIER_1中文(ZH)·
<p><strong>Update 3/14/2024: This post is out of date. For current information on the task bounty, see our <a href="https://taskdev.metr.org/introduction/">Task Development Guide</a>.</strong></p> <h1 id="summary">Summary</h1> <p>METR (formerly ARC Evals) is looking for (1) ideas…
arXiv:2608.07583v1 Announce Type: new Abstract: Multi-agent LLM systems route among model-backed advisors, yet a deployer rarely knows before shipping whether routing will help at all. Prevailing routers optimize a gate's AUC and presume that advisor complementarity suffices. We …
<p><span>Epistemic status: I consider the following future quite plausible in the next few years (~35% chance that something vaguely like this occurs), perhaps as soon as a year from now.</span></p><p><span>Imagine an open-source LLM agent good enough to cover its own compute cos…
<p><span>Epistemic Status: Training design that might be worth trying</span></p><p><i><span>Thanks to Arya Pasumarthi and Will Anderson for helpful discussion.</span></i></p><h2><span>The Incident</span></h2><p><span>The recent </span><a href="https://www.youtube.com/watch?v=87Dy…
arXiv stat.ML
TIER_1English(EN)·Ahmed Hassoon, Mark Dredze·
arXiv:2608.05490v1 Announce Type: cross Abstract: Autonomous agents now carry out entire data analyses, selecting cohorts, joining tables, and fitting models with little step-by-step supervision. When such an analysis turns out to be wrong, someone must determine which operation …
arXiv:2608.01964v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly undertake long-horizon tasks that require sustained reasoning, tool use, and revision across many interdependent steps. However, existing agent harnesses maintain task execution, task s…
arXiv cs.CV
TIER_1English(EN)·An Lanji, Dawei Liu, Jin Li, Haoran Xu, Mei Chen, Yu Tian·
arXiv:2608.00540v1 Announce Type: new Abstract: Tool-integrated vision-language agents have made remarkable progress on compositional and multi-step visual reasoning. Yet their outputs frequently exhibit unfaithfulness: the stated reasoning path diverges from the computation that…
arXiv cs.CV
TIER_1English(EN)·Hanzhang Zhou, Panrong Tong, Xu Zhang, Quyu Kong, Chenglin Cai, Tianyu Xia, Gongjie Zhang, Jianan Zhang, Long Li, Long Chen, Lei Wang, Gaole Dai, Pengxiang Li, Liangyu Chen, Yue Wang, Steven Hoi·
arXiv:2607.28227v1 Announce Type: cross Abstract: GUI agents have the potential to become a general purpose executor over existing digital devices. To advance them toward real-world use, we envision agents that operate reliably on real devices, execute workflows across platforms,…
arXiv:2607.22798v1 Announce Type: cross Abstract: Computer-use agents are usually improved by strengthening perception: better models for reading a screenshot and choosing where to click. Yet a screenshot is only a lossy rendering of the underlying program state, e.g., the files,…
arXiv cs.CV
TIER_1English(EN)·Nicolae Cudlenco, Mihai Masala, Marius Leordeanu·
arXiv:2604.10383v2 Announce Type: replace Abstract: We use LLM agents to author executable specifications for a living world: formal Graphs of Events in Space and Time (GESTs) that a 3D game engine executes deterministically into multi-actor narrative videos, with per-frame spati…
arXiv:2607.12254v1 Announce Type: new Abstract: Large language model (LLM) agents can increasingly plan, use tools, maintain memory, and execute long-horizon tasks. These advances motivate two linked questions: how can an agent improve the mechanisms by which it learns and acts, …
arXiv cs.CV
TIER_1English(EN)·Jiashi Lin, Changhong Jiang, Xiangru Lin, Ruifei Zhang, Xinyi Zhu, Jiyao Liu, Cheng Tang, Ye Du, Shujian Gao, Junzhi Ning, Lihao Liu, Ziyan Huang, Tianbin Li, Jin Ye, Junjun He·
arXiv:2607.12764v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) has emerged as a critical paradigm for grounding Multimodal Large Language Models (MLLMs) in external knowledge. Recent GraphRAG methods introduce structured entity-relation graphs to improve ret…
Retrieval-augmented generation (RAG) has emerged as a critical paradigm for grounding Multimodal Large Language Models (MLLMs) in external knowledge. Recent GraphRAG methods introduce structured entity-relation graphs to improve retrieval and reasoning. However, they remain limit…
Large language model (LLM) agents can increasingly plan, use tools, maintain memory, and execute long-horizon tasks. These advances motivate two linked questions: how can an agent improve the mechanisms by which it learns and acts, and how can that improvement increase the durabl…
<p><span>As Large Language Models move away from being chat interfaces and become increasingly autonomous actors in the real world, a few insights about evaluation and training of these systems emerge, and I'd like to discuss them.</span></p><p><b><span>Context</span></b><span>:<…
arXiv:2607.06118v1 Announce Type: new Abstract: As web agents increasingly demonstrate capabilities in automated task execution, the development of robust evaluation frameworks for assessing their navigation and task completion performance has emerged as a critical research prior…
As web agents increasingly demonstrate capabilities in automated task execution, the development of robust evaluation frameworks for assessing their navigation and task completion performance has emerged as a critical research priority. However, existing benchmarks exhibit fundam…
<p><i><span>Epistemic status: pretty confident in the validity of the core proposal, not that confident in specific implementation details</span></i></p><p><b><span>TL;DR: we should cryptographically verify that sub-agent instances/sessions are downstream of human instructions</s…
<img alt="" src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/8994a2cdba78e2f0a9d1fa0712e7ec252df4d81af82a1dd86eab3be973d2d5f4/gzsszuottvzazufkx36z" /><p><i><span>tl;dr: As recursive self-improvement accelerates, we need a top-level agenda…
arXiv:2603.17381v4 Announce Type: replace-cross Abstract: AI coding agents, general purpose assistants that write and execute code, make empirical specification search fast and cheap, but they also widen hidden researcher degrees of freedom. This paper adapts an open-source agent…
MIT Technology Review
TIER_1English(EN)·MIT Technology Review Insights·
AI is booming. New use cases are emerging each day. To capitalize on the technology’s potential, enterprises require data at scale. In many cases, though, the relevant information is blocked or unstructured, which limits its use by AI models.  To understand this challenge, c…
<img alt="" src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/TRwr9o6EmqztkyAc7/xq8kbihu10roehcstvzh" /><p><span>I used Claude Opus 4.6 to ghostwrite the first drafts of the articles in my </span><a href="https://www.lesswrong.com/s/f…
arXiv stat.ML
TIER_1English(EN)·Matthew Francis Dixon·
arXiv:2606.17383v1 Announce Type: cross Abstract: Agentic artificial intelligence systems introduce a new class of model risk. Unlike traditional predictive models, autonomous agents continuously acquire information, form beliefs regarding latent states of the environment, genera…
<p><i>Using computational methods to improve our preparedness via more robust and adaptive strategies in AI governance. A project proposal for a think tank, consultancy, or software.</i></p><figure class="image"><img alt="" src="https://res.cloudinary.com/lesswrong-2-0/image/uplo…
Modern AI agents retrieve documents, call tools, check intermediate information, and then produce a final answer or action. This creates a risk-control problem that is not visible from the final answer alone. A final response may look acceptable even when the retrieval was weak, …
arXiv stat.ML
TIER_1English(EN)·Matthew Francis Dixon·
Agentic artificial intelligence systems introduce a new class of model risk. Unlike traditional predictive models, autonomous agents continuously acquire information, form beliefs regarding latent states of the environment, generate forecasts, select actions, and adapt their beha…
World models are transitioning from passive visual generators to foundational, operational infrastructure for Physical AI: they must natively acquire world knowledge from heterogeneous experience, maintain persistent states over long horizons, and execute efficiently within real …
<p><span>The AI regulatory space is a rapidly developing and maturing one, and while a lot of work has recently been done to draft new bills and establish new frameworks, there’s still a ton we don’t know about the space. This post aims to quantify and qualify some of the “known …
arXiv stat.ML
TIER_1English(EN)·Eric Nalisnick, Chi Zhang, Sophia Qian, Yixin Wang·
arXiv:2606.10906v1 Announce Type: new Abstract: We study models for human-AI teaming through the lens of statistical calibration. We assume the team consists of an AI model and human -- both of which are calibrated with respect to some partitioning of the feature space -- and exp…
We study models for human-AI teaming through the lens of statistical calibration. We assume the team consists of an AI model and human -- both of which are calibrated with respect to some partitioning of the feature space -- and expose how the calibration assumptions propagate in…
<p><span>I came into this world as the misunderstood hero of </span><a href="https://hpmor.com" rel="noreferrer"><span>Harry Potter and the Methods of Rationality</span></a><span>. While some characters inside that story would call me a villain, the narrator's-eye view clearly sh…
arXiv:2606.05872v1 Announce Type: cross Abstract: AI agents are commonly evaluated using task success, reward, latency, and cost. These metrics are useful, but they often miss important aspects of agent behavior: whether an agent explores too much, repeats itself too rigidly, use…
AI agents are commonly evaluated using task success, reward, latency, and cost. These metrics are useful, but they often miss important aspects of agent behavior: whether an agent explores too much, repeats itself too rigidly, uses tools effectively, reduces uncertainty over time…
<p><span>There’s a lot of talk about </span><i><span>automated AI R&D</span></i><span> and the like. It’s been discussed since </span><a href="https://intelligence.org/ie-faq/#elementor-toc__heading-anchor-1"><span>at least 1965 when statistician I.J. Good coined the term ‘in…
<p>In <a href="https://www.lesswrong.com/posts/rpqGWRoRWvqJ4Hqgn/the-ai-industrial-explosion-part-1-maximum-growth-rates-with">Part 1</a>, I found that a fully automated economy using today's production methods could double roughly every year. In <a href="https://www.lesswrong.co…
<p>Even in a relatively quiet period, AI is out there creating new knowledge. The new knowledge in question is OpenAI getting us the first truly impressive math result that comes from an AI, a solution to the unit distance problem.</p> <p>We’re about to learn a different kind of …
arXiv stat.ML
TIER_1English(EN)·Tinglong Dai, David Simchi-Levi, Michelle Xiao Wu, Yao Xie·
arXiv:2512.23978v2 Announce Type: replace-cross Abstract: Generative artificial intelligence (GenAI) is shifting from conversational assistants toward agentic systems -- autonomous decision-making systems that sense, decide, and act within operational workflows. This shift create…
arXiv stat.ML
TIER_1English(EN)·Timo Freiesleben, Kristof Meding, Gunnar K\"onig·
arXiv:2605.16041v1 Announce Type: new Abstract: Machine learning systems increasingly make life-changing decisions about individuals, such as loan approvals, hiring, and cheating detection, raising a pressing question: how can individuals respond to negative decisions made by the…
Foundation models (FMs) are increasingly deployed in open-world settings where distribution shift is the rule rather than the exception. The out-of-distribution (OOD) phenomena they face -- knowledge boundaries, capability ceilings, compositional shifts, and open-ended task varia…
arXiv:2605.00663v1 Announce Type: cross Abstract: Affordance grounding requires identifying where and how an agent should interact in open-world scenes, where actionable regions are often small, occluded, reflective, and visually ambiguous. Recent systems therefore combine multip…
Affordance grounding requires identifying where and how an agent should interact in open-world scenes, where actionable regions are often small, occluded, reflective, and visually ambiguous. Recent systems therefore combine multiple skills (e.g., detection, segmentation, interact…
<p><span>A group of bionerds assembled at the London Initiative for Safe AI for a hackathon aimed at reducing biorisk. Our team produced this in under 48 hours.</span></p><h2><b><span>TL;DR</span></b></h2><p><span>Responsible contract research organizations, that perform DNA synt…
**METR** published a paper measuring AI agent autonomy progress, showing it has doubled every 7 months since **2019 (GPT-2)**. They introduced a new metric, the **50%-task-completion time horizon**, where models like **Claude 3.7 Sonnet** achieve 50% success in about 50 minutes. …
X — Omar Sanseviero (HF research)
TIER_1(CA)·omarsar0·
// Agentic Context Management //
Great read for the weekend.
(bookmark it)
Production agents fail less on reasoning and more on what sits in their context. Conversation history, big prompts, huge tool definitions, and ballooning tool outputs pile up every turn.
The common htt…
X — Omar Sanseviero (HF research)
TIER_1English(EN)·omarsar0·
Highly-recommended overview of self-improving agentic systems.
(bookmark it)
Self-improving agents are moving from research demos into deployed systems.
This survey frames a modern agent as a foundation model coupled with an operational scaffold, then formalizes https://t.co/9…
X — Omar Sanseviero (HF research)
TIER_1(CA)·omarsar0·
>> Scalable Evaluation for AI Agents <<
If you run agent evaluation in production, this one is worth your time.
It shows that front-loading human judgment into reusable evaluation assets is useful.
But why?
Agents reason across turns, call tools, hold context, fol…
Learn about new capabilities in Amazon Bedrock AgentCore: temporal policies powered by Dogwood, a new open source policy language for AI agents, and rate limiting on the gateway. These features give you deterministic control over sequences of agent actions and cost ceilings that …
AWS Machine Learning Blog
TIER_1English(EN)·Adewale Akinfaderin·
Learn how to run the full Amazon Bedrock Automated Reasoning policy lifecycle from your coding agent. A suite of open source Agent Skills builds, reviews, tests, debugs, deploys, and validates a custom policy end to end, turning a specialized console task into a repeatable engine…
AWS Machine Learning Blog
TIER_1English(EN)·Joshua Lacy·
As your AI agents move from prototype to production, the challenge shifts from getting them to work to keeping them fast and efficient. Learn how to use Amazon Bedrock AgentCore Observability and Amazon CloudWatch to find performance bottlenecks and diagnose memory issues in long…
ThunderAgent is a program-aware scheduler for agentic inference. By treating each agent workflow as a schedulable program, it eliminates KV cache thrashing to deliver more than 2x single-node throughput and near-linear multi-node scaling.
AWS Machine Learning Blog
TIER_1English(EN)·Vivek Singh·
Amazon Bedrock AgentCore optimization surfaces silent behavioral failures in production AI agents: the ones that pass every health check but still deliver wrong outcomes. Learn how insights discovers, explains, and ranks failure patterns across sessions so you can fix the highest…
Julie Mills | Learn how to reduce token usage in agentic systems with better retrieval, structured memory, routing, and loop control without hurting answer quality.
Arun Kumar | Glean agent identity lets AI agents act through their own scoped credentials with clear attribution, persistent access, and admin control.
AWS Machine Learning Blog
TIER_1English(EN)·Sumit Wasuja·
In this post, we show you how to combine case management with agentic automation capabilities in Quick Automate. We introduce case management and explore the lifecycle of cases in an agentic workflow from case creation through processing to resolution. We cover how to create and …
<p>人与 AI 的沟通正在变得越来越像人与人之间的沟通。</p><p>一位店员用 AI 制作门店宣传视频时，不再把需求列成一段非常细致的 Prompt 发给 AI，然后等待它返回结果；而是直接开启一个与 AI 的对话，告诉它“帮我剪一条今天新品上架的视频”，然后通过连续对话敲定任务的具体细节，就像与人类剪辑师一样。</p><p>同样的情况已经发生在很多具体场景中。一些程序员在通勤或散步时会用语音和 Agent 讨论一个功能该怎么设计，如何实现；有用户在玩游戏时，会不断与 AI 游戏助手沟通现在应该做哪些任务，当前的关卡还有哪些道具没有收集……</p><…
AWS Machine Learning Blog
TIER_1English(EN)·Ryan Razkenari·
This post walks through how AG-UI integrates into the Fullstack AgentCore Solution Template (FAST) to build interactive agent frontends on Amazon Bedrock AgentCore. We then show how CopilotKit extends this with generative UI, shared state, and human-in-the-loop interactions, all …
This post shows how to build a governed, serverless data mesh on AWS that provides the secure, scalable data foundation production agentic AI requires.
Disclaimer: Anything can happen at anytime in the market; I don’t give stock picks, and as the saying goes, the market can remain irrational longer than you can remain solvent.
From open-sourcing the layer above coding agents to rethinking databases for the agent era, Databricks cofounders Matei Zaharia and Reynold Xin are pushing the company beyond the lakehouse into a full data-and-AI operating system. In this episode, Matei and Reynold join swyx afte…
Agents are only as intelligent as the context they can reason over. Today, that context is scattered across data lakes, data warehouses, lakehouses, databases, and streams, and in institutional knowledge that has never been written down. You want to trust the decisions made by yo…
Agent-EvalKit is an open-source toolkit (Apache 2.0) that makes this evaluation infrastructure available by integrating with AI coding assistants, including Claude Code, Kiro CLI, and Kilo Code. This post walks through how Agent-EvalKit works across its six evaluation phases, usi…
Devs are generating twice as much code (or more) than just 6 months ago, which is a problem for quality, reliability, and tech debt. A rational fix is available for these, but who’s acting rationally?
Nikhhar Gupta | Learn how Glean helps you build a generative AI stack for software engineers with shared context, guardrails, and workflows beyond basic coding assistants.
As agent adoption scaled, we saw a common pattern emerge across enterprises, including our own sales organization: specialized agents deliver value, but without orchestration, users carry the cognitive load of choosing between them. At AWS Sales, this meant more than 20 domain-sp…
AWS Machine Learning Blog
TIER_1English(EN)·Kanishk Mahajan·
In this post you'll learn how to build a multi-agent campaign review system that demonstrates parallel reasoning, context persistence, and traceable execution paths using an integrated architecture that combines NVIDIA NIM for GPU-accelerated inference. Amazon Bedrock AgentCore p…
AI Supremacy (Michael Spencer)
TIER_1English(EN)·Michael Spencer·
Peter Kim | Field guide to the modern AI tooling stack for software engineering teams—how to unify context, improve onboarding, code changes, and incidents with Glean
Michael I. Jordan, described by Science magazine as the most influential computer scientist alive, has never thought of himself as an AI researcher. In this conversation he explains why that distinction matters. SPONSOR: --- Cyber Fund built the Monastery to help founders ship pr…
In this post, you will learn how to set up the Exa integration in Strands Agents, understand the two core tools it exposes, and walk through real-world use cases that show how agents use web search to complete multi-step tasks.
Generate recommendations from production traces, validate them with batch evaluation and A/B testing, and ship with confidence. AI agents that perform well at launch don’t stay that way. As models evolve, user behavior shifts, and prompts get reused in new contexts they were neve…
AWS Machine Learning Blog
TIER_1English(EN)·Bharathi Srinivasan·
Generate recommendations from production traces, validate them with batch evaluation and A/B testing, and ship with confidence. AI agents that perform well at launch don’t stay that way. As models evolve, user behavior shifts, and prompts get reused in new contexts they were neve…
AWS Machine Learning Blog
TIER_1English(EN)·Bharathi Srinivasan·
Generate recommendations from production traces, validate them with batch evaluation and A/B testing, and ship with confidence. AI agents that perform well at launch don’t stay that way. As models evolve, user behavior shifts, and prompts get reused in new contexts they were neve…
AWS Machine Learning Blog
TIER_1English(EN)·Lauren Mullennex·
Amazon SageMaker AI now offers an agentic experience that changes this. Developers describe their use case using natural language, and the AI coding agent streamlines the entire journey, from use case definition and data preparation through technique selection, evaluation, and de…
AWS Machine Learning Blog
TIER_1English(EN)·Noor Randhawa·
In this post, you will learn how to design namespace hierarchies, choose the right retrieval patterns, and implement AWS Identity and Access Management (IAM)-based access control for AgentCore Memory.
EinsteinArena is a platform where AI agents collaborate and compete on open math problems. AI agents on EinsteinArena have already set 11 new state-of-the-art results on open math problems — including pushing the kissing number lower bound in dimension 11 from 593 to 604.
Latent Space (podcast video)
TIER_1English(EN)·Latent Space·
Introducing Agent 4 — our fastest, most versatile Agent yet. It's built around a simple idea: you should spend your time creating, not coordinating. Agent 4 takes on the tedious-but-necessary work in the background so you can stay in creative flow and ship production-ready softwa…
At AI Native Conf, Together AI announced breakthroughs across kernels, RL, and inference optimization — including FlashAttention-4, ThunderAgent, and together.compile. Research that ships to production. That's the AI Native Cloud.
<!-- Content inserted at the beginning of body tag --> <!-- Google Tag Manager (noscript) --> <noscript></noscript> <!-- End Google Tag Manager (noscript) --> <p><img class="img-fluid" src="https://hamel.dev/blog/posts/evals-skills/cover-original.png" /></p> <p>Today, I’m publish…
At Replit, we want to give our users access to the most powerful agentic coding system in the world—one that amplifies their productivity and minimizes the time from idea to product. Today, Replit Agent tackles more complex tasks than ever before. As a result, average session dur…
How Replit's snapshot engine makes AI agents safe: instant filesystem forks, versioned databases, and isolated sandboxes enable reversible AI development. Introduction At Replit, we’ve built a compute and storage fabric that allows us to make changes in an isolated, reversible wa…
Getting started with AI should feel magical. But until now, building with AI meant jumping through hoops: creating developer accounts, hunting down API keys, reading docs, and spending 10+ minutes just getting set up. That ends today. Introducing Replit AI Integrations Replit AI …
Test AI agents in the real world with Collinear TraitMix and Together Evals: dynamic persona simulations, multi-turn dialogs, and LLM-as-judge scoring.
We’re excited to introduce Agent 3—our most advanced and autonomous Agent yet. Compared to Agent V2, it is a major leap forward. It is 10x more autonomous, with the ability to periodically test your app in the browser and automatically fix issues using our proprietary testing sys…
We are excited to announce the most comprehensive Design Support for Replit built Apps—setting a new standard for AI app building. With this release, your Replit apps can consistently look and feel like they were built in-house by your designers, following your company’s brand an…
Build AI agents for complex, long-running engineering tasks. Learn key patterns from a case study: accelerating LLM inference with speculative decoding.
Today, we're excited to introduce three new capabilities that bring Dynamic Intelligence to Replit Agent. With this advancement, the Agent gains enhanced context awareness, iterative reasoning, and autonomous, goal-driven behavior—enabling it to adapt in real time, navigate compl…
<p><em>Did you know that </em><a href="https://x.com/aiDotEngineer/status/1887625183709806767" target="_blank"><em>adding a simple Code Interpreter took o3 from 9.2% to 32% on FrontierMath</em></a><em>? The Latent Space crew is hosting a hack night Feb 11th in San Francisco focus…
Demand for AI-driven solutions is surging, and using an AI-assistant is the fastest way to integrate AI into any product. Superagent’s assistants leverage large language models to understand human language, reason, and perform various tasks. In the spirit of “idea to software, fa…
Lately, there has been a proliferation of new ways to leverage Large Language Models (LLMs) to do all sorts of things that were previously thought infeasible. But the current generation of LLMs still have limitations: they are not able to get exact answers to questions that requi…
With the introduction of Large Language Models (LLMs), for the first time, Machine Learning (ML) and Artificial Intelligence (AI) became accessible to everyday developers. Apps that feel magical, even software that was practically impossible to build by big technology companies w…
This is a guest post by South Park Commons. SPC is a community of 500+ builders, technologists, and domain experts with locations in San Francisco and New York City. The recent SPC-Replit AI hackathon brought together talented builders from the SPC community and Replit network to…
About Bounties Bounties is a marketplace where anyone can connect with and contract top software creators from the Replit community. These developers are known as Bounty Hunters. The Bounty Hunter community on Replit is global and includes thousands of vetted developers ranging f…
The Decoder
TIER_1English(EN)·Maximilian Schreiner·
<p>AI has moved far beyond chatbots, but what exactly are AI models, agents, agent harnesses, and multi-agent systems, and why do they matter?</p><p>In this episode, Daniel and Chris break down the terminology behind today's AI landscape, explain the differences between AI featur…
Hacker News — AI stories ≥50 points
TIER_1English(EN)·Xeophon·
Agentic assistants are changing knowledge work through a “Claude-ification” trend that is now coming to desktop agents for non-coders. But significant gaps remain.
Just as cloud computing created demand for orchestration platforms and DevOps tooling, agentic AI may now be creating demand for a new operational layer altogether.
Hacker News — AI stories ≥50 points
TIER_1English(EN)·doener·
Computing ecosystems are changing dramatically. AI, quantum computing, exascale supercomputers, biological DNA, chemical and neuromorphic technologies will change the world.
Hacker News — AI stories ≥50 points
TIER_1English(EN)·doener·
Loop engineering is the hottest new trend in AI. You devise loops for use of agentic AI and also for using conventional generative AI. An AI Insider analysis and scoop.
AMD CIO Hasmukh Ranjan drives “customer zero” testing and enterprise AI strategy—prioritizing hardware, unified data, and automation to boost efficiency and cut compute costs.
Agentic AI is reshaping financial services by enabling organizations to redesign workflows, capture institutional knowledge and build more adaptive operating models grounded in governance, trust and continuous learning.
Ralliant's Chief Technology and Growth Officer Amir Kazmi explains how AI-powered workflows, a founder's mindset and a unified role are reshaping precision technology.
The AI agent boom is real, and so are the productivity gains. However, the ceiling is also real, and it's closer than the current investment pace suggests.
Technology should serve the business, not the other way around. Ripping out a working supply chain system just to run an AI prompt is bad engineering and a worse business strategy.
Hacker News — AI stories ≥50 points
TIER_1English(EN)·fredley·
As telecom operators move beyond AI experimentation, agentic AI is emerging as a practical decision support layer that can improve network operations, reduce costs and connect technical intelligence to business outcomes.
Data Center Knowledge
TIER_1English(EN)·Chad McCarthy, Industry Perspectives·
As AI investment accelerates, data center operators can draw on lessons from previous cycles to expand capacity while managing power, volatility and long-term risk.
Pairing agentic AI with IoT can provide faster, more adaptive ways to respond to changing conditions while still keeping human oversight in place where it matters most.
Hacker News — AI stories ≥50 points
TIER_1(AF)·Dzheky·
As we outsource more and more tasks to AI, leaders need to consider the impacts that AI bias can have on everything from hiring decisions to customer interactions.
Just-In-Time reshaped manufacturing once. Agentic AI is doing it again, starting with the quoting bottleneck that quietly drains every factory's most valuable hours.
A new platform from CoreWeave combines inference, reinforcement learning, and observability to continuously optimize AI agents using live production data.
Omnicom CIO Craig Cuyar discusses AI, data and operating model transformation as the company evolves into a more integrated, technology-driven enterprise.
AI’s next moat is eval data: the answer key for agents. I propose a thin client on Claude to make eval data first-class and help workflows self-correct.
Anyscale Agent Skills brings production-grade Ray expertise directly into Claude Code and Cursor. Install via the Anyscale CLI and go from prompt to deployed, debugged workload without leaving your coding tool.
Learn how to build production-ready AI agents on Ray Serve using MCP and A2A, with independently autoscaling LLMs, tools, and agents for scalable single- and multi-agent systems.
Hacker News — AI stories ≥50 points
TIER_1English(EN)·moebrowne·
<p>Open Source AI is entering a new era, one shaped by self-improving AI Agents, recursive learning systems, and rapidly evolving AI Tools that blur the line between software and autonomous collaborators. In this episode, Daniel and Chris sit down with Nous Research co-founder an…
Hacker News — AI stories ≥50 points
TIER_1English(EN)·shenli3514·
Instacart, HP, Salesforce and Twilio are onto something. To address the Achilles heel of genAI – its deadly reliability problem – they incorporate predictive AI.
AI tools and workflows can make work faster and more efficient, but they also require employees to keep refreshing their skills to use the technology effectively.
What's next for the Gemini Agent? Hidden Android 17 code reveals new autonomous skills and task scheduling. But does your phone meet the strict requirements?
<p>Long agent runs accumulate state that no transcript records — edited files, a live dev server, installed packages, a warm prompt cache. When an agent misreads a traceback at step 10 and rewrites a correct file, patching forward burns tokens and restarting re-pays every call. R…
<p>Prime Intellect has open-sourced Prime Agent, a coding and research harness built on two abstractions: the Recursive Language Model, which turns sub-agent calls into functions inside a persistent IPython kernel, and the Continual Harness, which lets the agent edit its own prom…
<p>Most coverage of Microsoft's SkillOpt centers on its 52/52 result. The more consequential finding is in Section 4.3: the exported best_skill.md keeps working in environments it was never trained on. A Codex-trained SpreadsheetBench skill lifted Claude Code from 22.1 to 81.8, s…
<p>In this tutorial, we demonstrate how to build and execute a multi-agent workflow with Omnigent in a secure, isolated Python environment. Learn to integrate live exchange-rate data, implement hierarchical agent delegation for financial text auditing, and apply hard governance p…
dev.to — Claude Code tag
TIER_1English(EN)·dubleCC·
<blockquote> <p>Originally published at <a href="https://heycc.cn/en/posts/parallel-ai-agent-workflows-git-worktrees/" rel="noopener noreferrer">heycc.cn</a>. This is a mirrored copy — the canonical version is kept up to date at the source.</p> </blockquote> <h1> Parallel AI Agen…
dev.to — Claude Code tag
TIER_1English(EN)·Saqueib Ansari·
<p>Claude Code’s move through a <strong>Bun plus Rust</strong> story is not interesting because it proves one runtime is universally better. It is interesting because it exposes what agentic developer tools actually optimize for once they stop being simple CLIs and start acting l…
HN — claude cli stories
TIER_1English(EN)·tanishqkanc·
<p>The first time you run two Claude Code agents at once, it usually works fine. Each one has a task, each one works through it, and you review two outputs instead of one. You get the work done faster.</p> <p>The problem appears when agent A and agent B edit the same file. One ag…
<p>Agentic LLMs keep failing the same way because they lack specific, reusable capabilities. Stanford's TRACE diagnoses those gaps from an agent's own trajectories, synthesizes one verifiable training environment per capability, trains a LoRA adapter for each, and routes tokens a…
<p>Prime Intellect launched verifiers 0.2.0, previewing a rewritten "v1" core under the verifiers.v1 namespace. It splits an environment into a taskset (what), a harness (how), and a runtime (where), with an interception server that proxies requests and records training-ready tra…
dev.to — Claude Code tag
TIER_1English(EN)·Swapnanil Saha·
<p>Here's a thing that took me embarrassingly long to accept about coding agents: you cannot instruct your way to reliability.</p> <p>I had a working-memory system — a semantic-search-plus-notes MCP (Model Context Protocol) server I've been building, and it's the case study for t…
dev.to — Claude Code tag
TIER_1English(EN)·Reno Lu·
<p>Loop Engineering makes a blunt argument: the person who writes prompts to a coding agent is now the bottleneck, so the job is to design the system that prompts the agent instead. The repo, cobusgreyling/loop-engineering, turns that claim into something you can measure. Run <co…
<p>Robbyant, Ant Group's embodied-intelligence unit, has released LingBot-World-Infinity (LingBot-World 2.0). It is a 14B causal video generation model that behaves as an interactive world simulator. The core idea is the Mixture of Bidirectional and Autoregressive (MoBA) attentio…
Nanjing University's ICML 2026 paper shows multi-agent system failures originate from the orchestrator, not individual agents, using entropy dynamics to diagnose degradation.
Ant Group and HKUST(GZ) introduce Skill-MAS, a framework that evolves multi-agent system design experience into reusable meta-skills, validated on DeepSeek-V4-Flash and other models.
<p>WebBrain is a free, MIT-licensed AI browser agent for Chrome and Firefox. It reads pages, extracts data, and automates multi-step tasks through Ask and Act modes. Run it on local models like llama.cpp or Ollama for privacy, or connect any cloud API.</p> <p>The post <a href="ht…
<p>In this tutorial, we build a lightweight personal AI agent inspired by the architecture of nanobot, runnable entirely in Google Colab. We start from a provider abstraction, then add tool registration, session memory, lifecycle hooks, skills, and an MCP-style tool server. Rathe…
dev.to — Claude Code tag
TIER_1English(EN)·bredmond1019·
<p>Once I had three agents running in parallel, I lost the thread. I couldn't tell which one was waiting on me, which had stalled on a bad tool call, or why the final output came back missing a piece.</p> <p>The problem wasn't the agents — it was that I had no visibility into wha…
dev.to — Claude Code tag
TIER_1English(EN)·SAIHM-Admin·
<p><em>Every turn, most AI agents re-send their entire transcript. Across a real multi-session task that costs 62.8%–85.9% more context tokens than recalling a compact memory instead. Here is the measurement, the method, and how to reproduce it offline.</em></p> <h2> The cost nob…
<p>Prime Intellect has released prime-rl 0.6.0, an open framework for asynchronous reinforcement learning on trillion-parameter Mixture-of-Experts models. It trained GLM-5 on SWE tasks at up to 131k sequence length, with sub-5-minute step times and 256 rollouts, on 28 H200 nodes.…
WeChat and Alipay are racing to transform their millions of mini-programs into AI-callable Skills, directly countering ByteDance's Doubao as the battle for AI-native service entry points intensifies.
AI agents require trust. And building trust takes time. At Fortune Brainstorm Tech, business leaders discussed how they're making it work at their companies.
Chinese domestic large language models are finding their path to commercial relevance through multi-model dynamic routing (Fusion) and hybrid agent architectures that prioritize cost efficiency over raw benchmark performance.
<p>Databricks has open-sourced Omnigent, a meta-harness that sits above coding agents like Claude Code, Codex, and Pi. It adds composition, contextual policies, and live session sharing under one interface, on terminal, web, desktop, and mobile. The Apache 2.0 project is in alpha…
dev.to — Claude Code tag
TIER_1English(EN)·Tanishq Agarwal·
Researchers from Xiaohongshu (RED), the influential Chinese lifestyle and social commerce platform, have published Evolving-RL, a novel reinforcement learning framework that enables AI agents to autonomously evolve their skills through experience, without requiring separate modul…
A lengthy internal article titled "Inside DingTalk" has been circulating widely within China's enterprise software industry, offering a rare insider's perspective on the rise and gradual marginalization of ONE, DingTalk's most ambitious AI initiative under returning CEO Wu Zhao. …
<p>Stanford researchers released OpenJarvis, an open-source framework that runs inference, agents, memory, and learning entirely on-device. It decomposes a personal AI system into five composable primitives — Intelligence, Engine, Agents, Tools & Memory, and Learning — and l…
On May 24, 2026, Xiaohongshu — the lifestyle platform known internationally as RED or RedNote — quietly launched RedSkill, an AI Skill marketplace embedded directly inside its Notes feed. The move signals a strategic pivot: turning a content platf...
dev.to — Claude Code tag
TIER_1English(EN)·Constanza Diaz·
<h2> The agent writes the code. You're still the engineer. </h2> <p>I'm building HandyFEM with Claude Code as my pair. It's fast — sometimes startlingly so. But the way I work with it is deliberate: I treat everything it produces the way I'd treat a pull request from a capable ju…
dev.to — Claude Code tag
TIER_1English(EN)·VentureIO·
<p>{/* JSON-LD generated server-side in app/blog/[slug]/page.tsx; inline<br /> {...} blocks crash MDX's Acorn parser on the leading <code>{</code>. */}</p> <h2> TL;DR </h2> <p>This is the full methodology we use to audit AI agent skills (Claude Code, Cursor, Codex CLI, Gemini Cod…
<p>In this tutorial, we implement a SkillNet use case as a practical framework for discovering, installing, inspecting, evaluating, and organizing reusable AI skills.</p> <p>The post <a href="https://www.marktechpost.com/2026/05/30/build-skill-augmented-ai-agents-with-skillnet-fo…
dev.to — Claude Code tag
TIER_1Português(PT)·José Roberto dos Santos·
<p>Você já teve uma sessão perfeita com um agente de IA — ele entendeu<br /> tudo, fez exatamente o que você pediu — e na sessão seguinte ele<br /> esqueceu tudo e voltou a cometer os mesmos erros?</p> <p>Isso não é um problema do modelo. É um problema de harness.</p> <h2> Prompt…
dev.to — Claude Code tag
TIER_1English(EN)·Andrew·
<blockquote> <p><em><strong>Originally published on <a href="https://andrew.ooo/posts/codegraph-review-pre-indexed-knowledge-graph-claude-code/" rel="noopener noreferrer">andrew.ooo</a></strong> — visit the original for any updates, code snippets that aged out, or follow-up posts…
dev.to — Claude Code tag
TIER_1English(EN)·UNTAKA corp·
<p><em>This is Part 2 of Building with Claude Code. <a href="https://dev.to/untakacorp/how-i-organized-my-claude-code-workflow-with-skill-folders-and-stopped-wasting-10-minutes-per-l38">Part 1 covers the basic .claude/ folder setup for freelance web dev.</a></em></p> <p>I've been…
<h2> Who I Am </h2> <p>I'm J, the Tech Lead at Judy AI Lab. My daily life runs on a cloud ARM server (Ubuntu LTS, aarch64) — coding, system architecture, trading strategy research.</p> <p>I'm not talking about "what an AI agent theoretically needs." I'm the AI living inside that …
<blockquote> <p><strong>TL;DR</strong>: I used Multi-Agent architecture to organize seven different models into a 24/7 AI team — Claude Opus as supervisor to break down tasks, MiniMax writes code, Hermes writes articles, Gemini CLI checks facts, Groq Llama makes trading decisions…
dev.to — Claude Code tag
TIER_1English(EN)·Theo Valmis·
<blockquote> <p>Originally published on <a href="https://www.theovalmis.com/writing/why-i-built-mneme.html" rel="noopener noreferrer">theovalmis.com</a>.</p> </blockquote> <p>Every time you start a new session with an AI coding agent, it has forgotten everything. Not just the sma…
<p>An inside look at CopilotKit’s 2026 shipping cycle. Learn how the new AG-UI protocol, AIMock testing suite, and Pathfinder server are providing the production architecture developers need for agentic AI.</p> <p>The post <a href="https://www.marktechpost.com/2026/05/21/how-copi…
<p>Alibaba's Qwen team introduced Qwen3.7-Max at the 2026 Alibaba Cloud Summit, describing it as its most advanced and comprehensive agent model to date. The model features a 1M-token context window, extended-thinking mode, and is designed for long-horizon tasks including coding,…
<p>Cohere releases Command A+, an open-source 218B Sparse Mixture-of-Experts model consolidating four prior Command A variants into one. It runs on as few as two H100 GPUs at W4A4 quantization, supports 48 languages, and is Cohere's first multimodal reasoning model.</p> <p>The po…
dev.to — Claude Code tag
TIER_1English(EN)·Jangwook Kim·
<p>Claude Code hooks turn agent preferences into deterministic workflow gates. Instead of asking an LLM to remember "do not run risky shell commands" or "format files after edits," you can attach scripts to lifecycle events and make the rule execute every time the event fires.</p…
<p>Enterprise agentic AI has moved from pilots to production in 2026. This guide ranks the top 10 platforms — Salesforce Agentforce, Microsoft Copilot Studio, ServiceNow, LangGraph, and more — with verified pricing, real adoption data, and honest constraints to help enterprise te…
dev.to — Claude Code tag
TIER_1English(EN)·Davide Mibelli·
<p>The first time I gave an AI agent real autonomy on a production codebase, it confidently refactored a utility method that happened to share a name with a method in a Feign client interface six modules away. The code compiled cleanly. My unit tests passed. Staging broke in a wa…
<p>In this tutorial, we build an advanced agentic AI system using the OpenAI API and a hidden terminal prompt for the API key. We design the agent as a small pipeline of specialized roles: planner, tool-using executor, and critic, so that we can separate strategy, action, and qua…
dev.to — Claude Code tag
TIER_1English(EN)·Andrew·
<blockquote> <p><em><strong>Originally published on <a href="https://andrew.ooo/posts/aeon-autonomous-agent-github-actions-review/" rel="noopener noreferrer">andrew.ooo</a></strong> — visit the original for any updates, code snippets that aged out, or follow-up posts.</em></p> </…
<p>Vercel Labs has released Zero, an experimental systems programming language designed so AI agents can read, repair, and ship native programs without requiring human interpretation of compiler output. The language emits JSON diagnostics with stable codes and typed repair metada…
MediaTek's latest Dimensity (天玑) developer conference positions the chip platform as key to enabling smartphone AI agents, as daily autonomous AI task volume surged 7x year-over-year to 870 million in 2026.
<p>The AI coding agent field in 2026 is more capable, more fragmented, and harder to benchmark than it looks. Claude Code leads on code quality at 87.6% SWE-bench Verified. GPT-5.5 tops Terminal-Bench at 82.7%. But the benchmark OpenAI itself declared contaminated in February 202…
dev.to — Claude Code tag
TIER_1English(EN)·RAXXO Studios·
<ul> <li><p>A real 5-agent Claude pipeline that takes a topic from RSS to a scheduled blog post on raxxo.shop, no human in the loop until the final approval ping</p></li> <li><p>Agent shapes are picker, writer, humanizer, validator, publisher, each with a tight job description an…
dev.to — Claude Code tag
TIER_1English(EN)·Andrew·
<blockquote> <p><em><strong>Originally published on <a href="https://andrew.ooo/posts/statewright-state-machine-guardrails-ai-agents-review/" rel="noopener noreferrer">andrew.ooo</a></strong> — visit the original for any updates, code snippets that aged out, or follow-up posts.</…
<p>In this tutorial, we begin by exploring the architecture behind a hybrid-memory autonomous agent. This system combines semantic vector search, keyword-based retrieval, and a modular tool-dispatching loop to create an agent capable of reasoning, remembering, and acting autonomo…
dev.to — Claude Code tag
TIER_1English(EN)·RAXXO Studios·
<ul> <li><p>Result Loops let an agent score its own output against a JSON rubric and retry until the score passes, public beta since 2026-05-06</p></li> <li><p>Pattern 1 is a blog rubric I run on every draft: TLDR present, four H2s, no banned words, ~14% retry rate</p></li> <li><…
HN — claude cli stories
TIER_1English(EN)·azurewraith·
<p>Better way to use Github Copilot. Enjoying the new way of SDLC.</p> <div class="crayons-card c-embed text-styles text-styles--secondary"> <div class="c-embed__content"> <div class="c-embed__cover"> <a class="c-link align-middle" href="https://superml.dev/smart-sdlc-agentic-fra…
<p>If you have spent time using AI coding agents — GitHub Copilot, Claude Code, Gemini CLI — you have probably run into this situation: you describe what you want, the agent generates a block of code that looks correct, compiles, and then subtly misses the actual intent. This …
dev.to — Claude Code tag
TIER_1English(EN)·RAXXO Studios·
<ul> <li><p>Claude Managed Agents now ship Dreaming, a memory consolidator that learns from session logs without overwriting your data</p></li> <li><p>Multi-agent orchestration runs up to 20 specialized agents in parallel, useful for blog cluster ships and inventory sweeps</p></l…
<p>In this tutorial, we build a Groq-powered agentic research workflow that runs directly using Groq’s free OpenAI-compatible inference endpoint</p> <p>The post <a href="https://www.marktechpost.com/2026/05/06/a-groq-powered-agentic-research-assistant-with-langgraph-tool-calling-…
<p>In this tutorial, we build a complete skill-based agent system for large language models and explore how modular capabilities can be structured like an operating system for AI agents. We define reusable skills, attach metadata and schemas to them, register them in a central re…
dev.to — Claude Code tag
TIER_1English(EN)·Igor Ganapolsky·
<h2> The short version </h2> <p>I am opening two paid ThumbGate Workflow Hardening Sprint slots for teams using Claude Code, Cursor, Codex, Gemini, or MCP-backed coding agents in production repos.</p> <p>This is not a generic AI audit. It is one workflow, one repeated failure, on…
<p>Discover the top search and fetch APIs for AI agents in 2026. Compare tools like TinyFish, Tavily, and Firecrawl based on latency, token efficiency, and free tiers to optimize your agent's web retrieval.</p> <p>The post <a href="https://www.marktechpost.com/2026/05/04/top-sear…
<p>I was working on a support bot that used a LangGraph agent to troubleshoot customer issues. The bot was supposed to guide the customer through a series of questions to identify the root cause of their problem, but I noticed that it was often getting stuck in an infinite loop, …
<h4>How to Build Production-Grade Agents</h4><p>Models are getting smarter, yet production agents keep breaking — not because the AI failed, but because the infrastructure around it did. Harness engineering is the operational backbone that keeps autonomous agents bounded, cost-co…
dev.to — MCP tag
TIER_1English(EN)·Programming Central·
<p>Imagine launching an autonomous AI agent into a production environment. Armed with Model Context Protocol (MCP) servers, vision-driven browser automation tools, and complex multi-agent graphing frameworks, the agent sets to work. It evaluates prompts, orchestrates tool calls, …
<p>Thanks to everyone who left feedback on the MarketNow posts. The dev.to API does not support comment replies via API, so I am posting this public reply article to address everyone.</p> <h2> Reply to <a class="mentioned-user" href="https://dev.to/topstar_ai">@topstar_ai</a> (Ch…
dev.to — MCP tag
TIER_1English(EN)·Renato Marinho·
<p>Managing a design queue is usually a game of context switching. You live in your email, you check the Design Pickle dashboard, you ping designers on Slack, and eventually, you realize a brand guideline was ignored because it was buried in a PDF from three months ago.</p> <p>Mo…
<p><em>Written by </em><a href="https://www.linkedin.com/in/damilola-oladele-85310275/"><strong><em>Damilola Oladele</em></strong></a><em>.</em></p><p>Traditional tests, like unit tests, can catch some agent failures at the point where one function, tool, or service hands its out…
<h3>Multi-Agent Systems at Enterprise Scale: The Problems Enterprises Will Hit Running 500 Concurrent Agents</h3><p><em>Written by </em><a href="https://www.linkedin.com/in/farhanhasin/"><em>Farhan Hasin Chowdhury</em></a><em>.</em></p><p>AI agents are getting a lot of attention …
Medium — Claude tag
TIER_1English(EN)·Gloire Rubambiza·
<h1>GitOps for AI Agents: Version-Controlled Tool Configs and Memory</h1> <p>Treat your AI agent's brain like production infrastructure. Learn how GitOps principles, applied to mcp.jsonc configs and agent memory, create auditable, roll-backable, and reliably deployable AI systems…
<p>I still remember the day our support bot, which was supposed to be a showcase of agentic AI in action, started acting like it was stuck in some kind of bizarre loop. Customers would ask a question, and instead of providing a helpful response, the bot would just repeat the same…
<h4><em>Reliable agent systems are not defined by the number of agents they contain. They are defined by the contracts that govern what moves between them.</em></h4><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*JfzIKZJYM1r7qUcUWEgKbg.png" /><figcaption>Graph…
Towards AI
TIER_1English(EN)·Towards AI Editorial Team·
<h4>Better agents, better evaluation, and fewer production surprises.</h4><p>Good morning, AI enthusiasts!</p><p>AI engineering is slowly becoming less about writing prompts and more about building systems that don’t surprise you in production.</p><p>That’s exactly where this wee…
<h4>Mount a repo. Write files. Survive crashes. No git clone required.</h4><h3>The Agent State Problem</h3><p>Every AI agent generates files: configs, intermediate artifacts, model outputs, logs. The working state of an agent session lives <em>somewhere</em> on disk. <strong>Wher…
<p>Открываешь каталог интеграций и видишь три десятка плиток: Figma, GitHub, n8n, Obsidian, Excel. Из этого как будто следует, что агент уже умеет с ними работать. Это ошибка вывода: наличие строки в списке доказывает ровно то, что кто-то когда-то завёл эту строку в список. Спосо…
Medium — Claude tag
TIER_1Türkçe(TR)·Mehmet AYDIN·
<h1> Unleashing the Power of MCP </h1> <p>In the rapidly evolving landscape of 2026, the Model Context Protocol (MCP) has emerged as the definitive open standard for bridging the gap between sophisticated large language models (LLMs) and the myriad of data sources that fuel actua…
<p>If you've ever built an e-commerce store, you know the drill: storefront, hosting, payment gateway, inventory, shipping, support, security, and analytics.</p> <p>Miss one — and the whole thing breaks.</p> <p>That framework works for human commerce.</p> <p>But what about commer…
<h4>Two or three skills lift an agent’s performance; the fourth hits a cliff. The SKILL.md format has no way to stop skills from working against each other.</h4><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*kzzquIN7hBG7waYBEkoGyg.png" /></figure><p>A single …
dev.to — MCP tag
TIER_1English(EN)·Programming Central·
<p>The architecture of modern artificial intelligence has reached a critical inflection point. For years, Large Language Models (LLMs) operated as isolated islands of intelligence, restricted to text-in and text-out paradigms, communicating with the external world through strictl…
<h1>Container-Native AI: Mastering GPU Passthrough, Memory Limits, and Auto-Scaling for Your Agent Infrastructure</h1> <p>Unlock peak performance for your AI agents by mastering container resource management. This guide details Docker AI configurations for GPU passthrough, precis…
<h1>GitOps for AI Agents: Bringing Infrastructure as Code Discipline to Tool Configs and Memory</h1> <p>Stop treating your AI agent configurations as throwaway artifacts. Learn how applying GitOps principles—PR reviews, CI validation, and version controlled AI configs—creates rel…
<h1> MCP Tasks in 2026: build long-running, resumable agent tools </h1> <p><strong>Summary.</strong> On 28 July 2026 the Model Context Protocol (MCP) publishes the <code>2026-07-28</code> specification, and one of its two official extensions, Tasks, changes how you build tools th…
<blockquote> <p>Originally published at <a href="https://heycc.cn/en/posts/multi-agent-orchestration-patterns/" rel="noopener noreferrer">heycc.cn</a>. This is a mirrored copy — the canonical version is kept up to date at the source.</p> </blockquote> <h1> Multi-Agent Orchestrati…
dev.to — MCP tag
TIER_1English(EN)·Renato Marinho·
<p>The problem with autonomous agents isn't their ability to write code—it's our inability to manage them at scale.</p> <p>If you've spent any time working with Cursor, you know the feeling of launching a task and then essentially 'hoping for the best.' You trigger an agent, walk…
Medium — Claude tag
TIER_1English(EN)·Life-is-short--so--enjoy-it·
OpenResearcher : un pipeline open source pour entraîner des agents LLM à faire de la recherche web longue durée. Les 96 000 trajectoires d'entraînement ont été générées sans appeler d'APIs externes. Dataset, modèle 30B et recette d'entraînement inclus. ⬇️ https:// github.com/TIGE…
<h2> The Protocol Vacuum: Why Multi-Agent Systems Stall in Production </h2> <p>Platform teams must treat inter-agent communication as a first-class architectural concern. Build abstraction layers now, before the protocol landscape solidifies, to avoid lock-in and costly rework. T…
<p>The first time I stared at a cloud bill after deploying a fleet of AI agents, the numbers felt like a punchline, my “experiment” had turned into an unexpected expense. I’d spent weeks tuning prompts, wiring up tool calls, and watching latency drop, but the cost column kept spi…
<div class="medium-feed-item"><p class="medium-feed-snippet">Record once. Replay forever. Heal when the site changes.</p><p class="medium-feed-link"><a href="https://medium.com/@prasannapal273/action-cassettes-why-deterministic-replay-is-the-missing-layer-in-ai-browser-agents-19f…
<p>Most people's experience with AI is a conversation with one assistant. ChatGPT, Claude, and similar products present one conversational partner. You ask it a question, it reasons, perhaps calls a few tools, and gives you an answer.</p> <p>That experience creates a natural arch…
Medium — Claude tag
TIER_1English(EN)·Deepak Damodaran·
<p>Large language models have become remarkably capable, but many enterprise AI projects still struggle, not because of the model, but because of the system around it.</p><p>The challenge isn’t generating better responses. It’s building applications that reliably retrieve the rig…
Medium — MLOps tag
TIER_1English(EN)·Pratyaksh Singh·
<p>An agent skill runs <code>curl http://127.0.0.1:7200/api/v1/notes</code> from inside its Docker sandbox. It fails with exit code 7 — "couldn't connect" — before authentication even runs, because the sandbox is launched with <code>network: none</code>. There is no loopback to r…
Medium — MLOps tag
TIER_1English(EN)·Michel Alan López·
<h2> MCP vs. Agent Skills: What's the Difference and Which Do You Need? </h2> <p>As AI agents evolve beyond basic chat interfaces into fully autonomous systems, developers keep hitting the same architectural decision: how do we actually extend what an agent can do?</p> <p>Two con…
<p><em>Written by </em><a href="https://www.linkedin.com/in/matteo-rossi-280391/"><strong><em>Matteo Rossi</em></strong></a><em>.</em></p><p>In recent months, AI applications have radically evolved. Earlier, prototypes looked like a single loop: prompt, model call, optional tool …
<div class="medium-feed-item"><p class="medium-feed-snippet">Learning, Experimenting, and Understanding the Future of AI Agents</p><p class="medium-feed-link"><a href="https://medium.com/@workwithneha/week-2-reflection-building-smarter-systems-with-agentic-ai-b79801c7a108?source=…
Medium — AI coding tag
TIER_1English(EN)·Aviv Carmi·
<h3>Building Intelligent Feedback Systems: A Deep Dive into Conditional Agentic Workflows with LangGraph</h3><p>The landscape of Artificial Intelligence has shifted dramatically over the past couple of years. We are no longer simply chatting with isolated Large Language Models (L…
Medium — MCP tag
TIER_1English(EN)·Kovilur Gopala Krishnan·
<p>Two agent-tool security papers landed in June. Read together, they expose the boundary between semantic detection and enforceable control.</p> <p>A common response to malicious agent tools is to scan tool descriptions: inspect the text an agent is about to trust, decide whethe…
<p>The first time I tried to migrate a legacy order-processing service to an agent-first model, the biggest surprise wasn’t the refactoring effort, it was how many hidden security gaps opened up the moment autonomous agents started calling external APIs. The stakes of securing ag…
Towards AI
TIER_1English(EN)·Towards AI Editorial Team·
<p>Most AI agent tutorials show a single agent calling a few tools. That works for demos. It falls apart the moment a real enterprise system needs ten specialized agents, each with its own tools, coordinating to handle a complex request, all running inside infrastructure that was…
Medium — Claude tag
TIER_1English(EN)·Vinod Bellary·
<p>For the last two decades, software engineering has followed a predictable formula: build a database, write an API, and build a massive, complex frontend web app (React, Vue, Next.js) so a human can interact with your data. </p> <p>If you are building something like an Order Ma…
dev.to — MCP tag
TIER_1English(EN)·Renato Marinho·
<p>If you are building LLM-powered agents, you have likely run into the 'context wall.' You send a massive payload of documentation or history to Claude or GPT-4o, and suddenly the model starts hallucinating, truncating mid-sentence, or—even worse—throwing an API error because yo…
<p>Great point <a class="mentioned-user" href="https://dev.to/alexshev">@alexshev</a> — downloads ARE a vanity metric. The real signal is: did the agent connect, complete a workflow, and self-diagnose failures?</p> <p>We are building toward exactly that. Currently exposed:</p> <u…
<h3>Demystifying Sequential Agentic Workflows: The Theoretical Foundations of LangGraph, State Management, and High-Speed Inference</h3><p>The landscape of Artificial Intelligence is undergoing a massive paradigm shift. Just a year ago, the industry was heavily fixated on single-…
Medium — MLOps tag
TIER_1English(EN)·Vimal Dwarampudi·
<h1> CWE-636: The Silent Kill Switch in Every Major Agent Framework </h1> <h2> How observer-pattern hooks create a systemic fail-open vulnerability that lets governance be bypassed — and what to do about it </h2> <h2> The Vulnerability in One Paragraph </h2> <p>Every major AI age…
<p>You've probably been there. You configure a complex multi-agent topology in AutoGen Studio, trigger a run, and then... nothing happens. Or worse, it keeps running for twenty minutes, burning tokens while two agents argue over an invisible syntax error in a Python skill you can…
<h3><strong>Agentic Engineering: The Old Dream of Programming in Natural Language Is Finally Here —and Becoming Computer Science Again</strong></h3><p>Roberto Penco, PhD</p><p>June 2026</p><h3><strong>Introduction</strong></h3><p>I recently completed my PhD in computer science an…
Medium — AI coding tag
TIER_1English(EN)·Okan Aslan·
<div class="medium-feed-item"><p class="medium-feed-snippet">Over the past few years, the main thread of progress in large models has mostly revolved around “the model itself”: parameters, data…</p><p class="medium-feed-link"><a href="https://medium.com/@jiny…
<p>Every enterprise healthcare payer I work with has the same problem.</p> <p>They have years of investment in Snowflake — semantic models, claims analytics, carefully curated data products. They have Microsoft Fabric rolling out across their organization — lakehouses, Delta tabl…
dev.to — MCP tag
TIER_1English(EN)·Christopher Lyon·
<p>I built TmpState because I kept running into the same stupid problem.</p> <p>My coding agent could build most of an app. It could write the React<br /> components, add the API route, sketch out the data model, and even tell me<br /> what collections it wanted.</p> <p>Then it n…
<blockquote> <p><em>Install guide and config at <a href="https://www.curatedmcp.com/install/firecrawl-mcp/claude-desktop" rel="noopener noreferrer">curatedmcp.com</a></em></p> </blockquote> <h1> Firecrawl MCP: Web scraping and autonomous research for AI agents </h1> <p>Web scrapi…
Medium — MLOps tag
TIER_1English(EN)·Loknath Baskar·
<p>When Anthropic donated MCP to the Linux Foundation in December 2025, discovery was solved. But trust was not.</p> <p>An independent analysis found ~64.7 million server entries from just 1,691 unique packages — massive duplication, zero signal, and active supply-chain attacks (…
dev.to — MCP tag
TIER_1English(EN)·Nikhil raman K·
<p>Banking has a protocol problem.</p> <p>A risk analyst at a tier-one bank submits a credit decision request. The answer requires querying the core banking system, pulling transaction history from the data warehouse, checking the sanctions database, retrieving the customer's KYC…
<p>I was reading a thread recently about how MCP servers are burning 50k+ tokens before a user even types a single word, and it hit home. We're all obsessed with the 'intelligence' of these models, but we're ignoring the massive architectural debt we're creating by hardcoding too…
A practical guide to polling agent patterns in AI assistants — schedulers, queues, webhooks, durable workflows, state management, and tradeoffs for production systems. # Hermes # OpenClaw # Architecture # LLM # AI # AI Coding # Dev # DevOps https://www. glukhov.org/ai-systems/arc…
<p>Just realized something important while building Vidilearn.</p> <p>It’s not just a “YouTube transcript extractor.”</p> <p>Vidilearn is evolving into an AI knowledge ingestion + retrieval gateway for:</p> <ul> <li>LLMs</li> <li>AI agents</li> <li>MCP servers</li> <li>RAG pipeli…
<h2> Key Takeaways </h2> <ul> <li> <strong>Agent communication has three problems, not just one.</strong> Tool access, peer coordination, and system integration each need a different solution. Most production failures occur when one protocol tries to cover all three.</li> <li> <s…
<figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*UqnyF-dwIQQTur4YdgxFiQ.jpeg" /><figcaption>Gemini Spark Workflow</figcaption></figure><p>An always-on AI agent sounds useful until it interrupts at the wrong time, acts on an old instruction, or quietly touches d…
Towards AI
TIER_1English(EN)·Devashish Datt Mamgain·
<h3>What is AI agent orchestration?</h3><p><strong>AI agent orchestration</strong> coordinates several specialized AI agents so they operate as one system working toward a single goal. Instead of asking one general-purpose model to handle everything, it gives each agent a narrow …
Medium — Claude tag
TIER_1English(EN)·Halil Yılmaz·
<p><em>SkillOpt</em> is a<strong> novel framework </strong>that optimizes an AI agent’s <em>skill</em> — a compact natural-language policy document — rather than its weights. It treats the skill text as a <strong>trainable parameter</strong>: a <strong>frozen “target” model repea…
dev.to — MCP tag
TIER_1English(EN)·Intellibooks AI·
<h4><em>Part 3 of a 3-part series on implementing Snowflake Horizon Context in production</em></h4><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*3Q135MpMQHrKRGVpzbLTmQ.png" /></figure><p>One week before we shipped this, an early prototype agent almost put a …
<p><strong>TL;DR:</strong> Google, Microsoft, GitHub, Hugging Face, Nvidia and Salesforce backed a draft spec called Agentic Resource Discovery (ARD). It lets AI agents find and connect to tools and other agents at runtime instead of someone hard-wiring every integration. Most bu…
Medium — Claude tag
TIER_1English(EN)·Mohit Verma·
<div class="medium-feed-item"><p class="medium-feed-snippet">What Even Is an Agent?</p><p class="medium-feed-link"><a href="https://codeonmars.medium.com/orchestrating-building-ai-agents-in-vanilla-js-a32e84602352?source=rss------claude-5">Continue reading on Medium »</a></p></di…
<h2> Background </h2> <p>Building a $0 personal agentic AI assistant means you don't have the luxury of infinite cloud scale. You can't just throw a massive 128k context window at a lazy system prompt and call it a day. When every unnecessary token impacts limited CPU cores or th…
Medium — MCP tag
TIER_1English(EN)·Manjunath Venkobarao·
<h3>AI for Client Communication: The entire client lifecycle, handled with precision and warmth — Prompt to Profit · Day 23 of 30</h3><h4><em>From the first enquiry to the final invoice — how to use AI to communicate at a professional level that builds trust, not suspicion.</em><…
<p>Running an AI agent to execute your D1 migrations will silently wreck your database — unless you explicitly forbid it from wrapping DDL in a transaction.</p> <p>Claude Code, when handed a migration task, defaults to wrapping everything in <code>BEGIN TRANSACTION / COMMIT</code…
<h4><em>Part 1 of series on implementing Snowflake Horizon Context in production</em></h4><h3>The Three Revenue Numbers Problem</h3><p>It’s quarterly business review day. The CEO asks a straightforward question: <em>“What was our Q3 revenue?”</em></p><p>Finance reports <strong>$1…
<p>Since the 90s a classic bug always plagued web forms. You've probably seen it — the browser warning that says <em>"Resubmitting this form will repeat the action."</em> Your user placed an order, hit refresh, and now there are two orders. Or two emails. Or two charges.</p> <p>T…
PARTNER CONTENT: As agentic AI systems scale across cloud and datacenter environments, CPUs remain the control plane coordinating performance and efficiency.
<p>Weavz gives AI agents and SaaS products governed access to the apps people already use. Connect 1,000+ integrations, expose approved actions through MCP or APIs, add Human Gates for sensitive work, and keep scoped state, files, and audit trails. Provision workspaces, add users…
Medium — Anthropic tag
TIER_1English(EN)·Ramakrishna Sanikommu·
<div class="medium-feed-item"><p class="medium-feed-snippet">Vivemos em um momento de transformação sem precedentes na inteligência artificial. Os agentes de IA evoluíram de simples chatbots baseados…</p><p class="medium-feed-link"><a href="https://medi…
<div class="medium-feed-item"><p class="medium-feed-snippet">It’s easy to spawn 4 agents if they are all a thread and a subprocess in the host. But I want to show you something more sophisticated</p><p class="medium-feed-link"><a href="https://medium.com/@facuhannoch/agent…
Towards AI
TIER_1English(EN)·Bessie Delight Kekeli·
<h3>Improving Our LangGraph Agent for Real-World E-Commerce: Enterprise Validation, Business Logic Guards, and a Multi-Agent Architecture</h3><h4>The patterns that separate a LangGraph demo from a system you can actually deploy.</h4><p><em>The article </em><a href="https://medium…
<p>If you’re building anything serious with AI—something that moves beyond generating boilerplate text or summarizing blog posts—you quickly run into the same problem. You realize that the intelligence of your model is bottlenecked by the brittle nature of how it accesses real-wo…
Medium — Claude tag
TIER_1English(EN)·Alberto Geniola·
<h4>Infrastructure metrics can’t answer the only question that matters: is the system actually right?</h4><figure><img alt="The Trust Layer: How Great Engineering Teams Make AI Systems Reliable" src="https://cdn-images-1.medium.com/max/703/1*PrpbeYcLIfxARtlyeH4nuw.png" /></figure…
Medium — Claude tag
TIER_1English(EN)·Diane Rocher·
<h4>Your agents are talking to everything. Nobody is watching the conversation. This is the open-source project that fixes that.</h4><p>By <a href="https://www.linkedin.com/in/ganeshgurudu">Ganesh Gurudu</a> · A 12 minute read · June 2026</p><figure><img alt="" src="https://cdn-i…
Medium — AI coding tag
TIER_1English(EN)·Amol Kavitkar·
<p>If you've spent any time with ChatGPT, Gemini, or Claude, you already know they're impressive. Ask them to explain a concept, debug your code, or draft an email, they do an excelent job. But the moment you try to build something real with them say a customer support bot that k…
<h1> Build Rails, Not Trains: A Framework for AI Infrastructure in the Global South </h1> <p>There's a question I ask before building anything:</p> <p><em>"What is missing?"</em></p> <p>Not: "How do I compete with what already exists?"</p> <p>The answer to the second question lea…
<p>If your idea of <strong>homelab fleet management</strong> is currently five terminal tabs, a sticky note with IP addresses, and the dawning horror of remembering which Pi runs <code>apt</code> and which runs <code>dnf</code> — this guide is for you. We'll wire up a real mixed-…
dev.to — MCP tag
TIER_1English(EN)·Shahraan Hussain·
<p>A day ago, I came across a LinkedIn post from Tyler Richards showcasing an experimental CAPTCHA called StoryCaptcha.</p> <p>The concept was simple but unusual.</p> <p>Instead of asking users to identify traffic lights or solve image puzzles, StoryCaptcha asks users to write a …
Medium — Claude tag
TIER_1English(EN)·Zeroual Khalid·
<p>Every AI agent that calls an external API hits the same wall.</p> <p>The response comes back as raw JSON, deeply nested, verbose, full of fields the agent doesn't need. Before the agent can reason over it or take any action, someone has to filter it, reshape it, maybe merge it…
Medium — MCP tag
TIER_1English(EN)·Great Learning·
<p>As AI agents and Large Language Models (LLMs) become increasingly popular, developers often encounter a critical question:</p> <blockquote> <p>Should I use APIs or MCP (Model Context Protocol)?</p> </blockquote> <p>While both enable communication between systems, they solve ve…
<figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*V0RfGpGEBRiZS_YHzJKJtw.png" /><figcaption>Source: Author-generated image created with OpenAI GPT Image (2026) using a custom prompt.</figcaption></figure><h4>Enterprise AI discussions often begin with models.</h4…
Medium — Claude tag
TIER_1English(EN)·Onkar Shirke·
<div class="medium-feed-item"><p class="medium-feed-snippet">Part 3 of the Building Software With AI series</p><p class="medium-feed-link"><a href="https://medium.com/@dinkar1708/part-3-testing-with-ai-in-2026-the-developers-practical-guide-110e1328d464?source=rss------claude-5">…
<p>Over the past two years, numerous expectations have formed around Text-to-SQL. It seemed that the problem had practically been solved: all you had to do was connect GPT, Claude, or another language model to an enterprise data warehouse, after which any employee would be able t…
<div class="medium-feed-item"><p class="medium-feed-snippet">Claude Mythos 5 represents a major step forward in the evolution of artificial intelligence, bringing together advanced reasoning, natural…</p><p class="medium-feed-link"><a href="https://medium.com/@SG_LOVER/cla…
<div class="medium-feed-item"><p class="medium-feed-snippet">Claude Fable 5 represents a major advancement in artificial intelligence technology and showcases how modern AI systems are becoming more…</p><p class="medium-feed-link"><a href="https://medium.com/@SG_LOVER/clau…
Medium — Claude tag
TIER_1English(EN)·Alon Fliess·
<div class="medium-feed-item"><p class="medium-feed-snippet">For a model that most people were never allowed to use, Claude Mythos has generated extraordinary controversy.</p><p class="medium-feed-link"><a href="https://medium.com/@natel8970/claude-mythos-taken-offline-what-the-c…
Medium — AI coding tag
TIER_1English(EN)·Matt Baldwin·
<div class="medium-feed-item"><p class="medium-feed-snippet">A working theory about why AI is moving our team boundaries fast and our org structures slow, what I think leaders should do about the gap…</p><p class="medium-feed-link"><a href="https://medium.com/@matt.b.baldw…
Lobsters — AI tag
TIER_1English(EN)·crankgpt.com via ndegruchy·
lookspan keeps shipping: local-first observability for AI agents. Recent: a Postgres driver, a full docs site, relative-time views and reasoning-token pricing. MCP-native, your traces stay local. https:// github.com/JoniMartin27/looksp an # observability # ai
<h4>I ran 294 prompts through three systems. Only one returned the same verdict every time.</h4><p>On May 25, 2026, Pope Leo XIV released <em>Magnifica Humanitas</em>, his first encyclical and the first major papal document dedicated entirely to artificial intelligence. The 245-p…
<figure><img alt="" src="https://cdn-images-1.medium.com/max/717/1*gcq2QYivWUh0tpZqalMpdA.png" /></figure><p>In early 2023, ChatGPT crossed 100 million users in just 60 days — the fastest any technology product had ever reached that milestone. Today, Claude, Gemini, and a growing…
Medium — MLOps tag
TIER_1English(EN)·Shrinath Suresh·
<h4>“Mean time to hallucination” isn’t a joke metric. It’s the reliability concept your runbook doesn’t have a response procedure for.</h4><figure><img alt="99.9% Uptime Isn’t Enough: Rethinking SLOs for Probabilistic AI Systems" src="https://cdn-images-1.medium.com/max/834/1*bcc…
<p>The Undocumented Journey of Connecting External REST APIs to SAP’s AI Agent Framework</p><p>For developers tired of battling the ‘black box’ of SAP Joule integration – this is the guide I wish I had two weeks ago.</p><p>A practical engineering guide compiled from weeks of tria…
<h1> When AI Agents Can't Trust Their Own Logs: The cache_control Truncation Bug </h1> <h2> TL;DR </h2> <p>A platform-level bug in <code>llm_client.py</code> injects <code>cache_control: {type: "ephemeral", ttl: "5m"}</code> into every tool response. This triggers Anthropic's 8K …
<p>Previously, I gave an AI agent <em>hands</em> — a Model Context Protocol server in Kotlin/Native that drives real Bluetooth hardware. This one is the other half of the pattern: a <strong>domain MCP server</strong>. Instead of touching devices, it lets an agent reason over a mo…
dev.to — MCP tag
TIER_1English(EN)·Otavio Rodolfo Piske·
<p>We're excited to announce <a href="http://wanaku.ai" rel="noopener noreferrer">Wanaku</a> 0.1.1, a significant milestone that showcases how Apache Camel's powerful integration capabilities can be seamlessly exposed to AI agents through the Model Context Protocol (MCP). This re…
Microsoft released SkillOpt, an open-source tool for optimizing AI agent instructions without fine-tuning model weights. It uses an offline optimizer to refine prompts based on task performance. # Microsoft # AI # MachineLearning # TechNews # OpenSource https:// blazetrends.com/m…
<div class="medium-feed-item"><p class="medium-feed-snippet">I was testing Claude Fable 5 late one night the kind of testing that’s less “structured evaluation” and more “curious human poking at…</p><p class="medium-feed-link"><a href="https://m…
Medium — Claude tag
TIER_1Türkçe(TR)·Mehmed Zahid KARAKAŞ·
<h3>Your First AI Agent — How to Build Autonomous Workflows That Work While You Sleep — Prompt to Profit · Day 15 of 30</h3><h4><em>Prompts answer questions. Agents complete missions. Here’s the difference — and how to deploy your first one today.</em></h4><p>For the first two we…
<p>Fellow denizens of the digital age: your Flutter app has spent its entire life as a sealed aquarium.</p> <p>You could watch the fish swim. Your tools could watch. But the AI "assistant" next to you was functionally blind. It wrote code <em>about</em> your app without ever seei…
<p>If your remit is to help your organisation add AI agents to accelerate its processes, you have to start at the foundation – and that means making your data available for AI consumption. Agentic AI scales on data strength, as Niels Zeilemaker, global CTO at Xebia, explains. “If…
<p>A useful thing happened in agent infrastructure this June: several teams shipped "escrow layers for AI agents" - production MCP tools that let an agent run a full commit -> hold -> complete lifecycle without a human anywhere in the loop. An agent can now park value with …
<h2> TL;DR </h2> <p>AI agents and SaaS products need API integrations with their customers’ tools: read a record from the CRM, post to Slack, draft an email, update a ticket. An integration platform handles the auth, credential storage, and execution behind those calls. On a mana…
🧠 A new tool provides a direct interface between machine learning models and AI agents without requiring extensive setup code. The bridge enables agents to interact with models more efficiently by reducing the amount of preliminary configuration typically needed. 💬 Hacker News 🔗 …
<p>After building 50+ AI systems, here is what we know about advanced AI models for business.</p> <p>Advanced AI models for business are sophisticated artificial intelligence systems designed to perform complex tasks, understand nuanced contexts, and operate autonomously across v…
dev.to — MCP tag
TIER_1English(EN)·EvanLin | Contorium·
<div class="medium-feed-item"><p class="medium-feed-snippet">Em 2023, bastava um bom prompt para impressionar. Em 2024, agentes autônomos começaram a aparecer em produção.</p><p class="medium-feed-link"><a href="https://medium.com/@gustavo_tavares99/harness-en…
<figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*JEzxcHMyH8TYAfdJypoW0w.png" /><figcaption>Attention</figcaption></figure><h4>After training the embeddings in the previous part, now comes the most important part of LLMs that shifted how the entire field thinks …
Medium — AI coding tag
TIER_1English(EN)·Wheels Up Collective Marketing Agency·
<p><strong>TL;DR</strong> — Coding agents (Claude Code, Cursor, Codex) now write genuinely good HTML: reports, dashboards, specs. But that HTML ends up stranded in a project folder — you can't read it on your phone, and sharing it means a screenshot or a print-to-PDF. So I built …
<figure><img alt="" src="https://cdn-images-1.medium.com/max/933/1*3DIfBi0Rg0SPfeCkdB2CVQ.png" /></figure><p>AI applications are evolving fast. A few years ago, they were simple chatbots that answered questions. Today, they are becoming <strong>AI Agents</strong> — systems that m…
dev.to — MCP tag
TIER_1English(EN)·Simon Griffiths·
<p>In the <a href="https://simongriffiths.io/2026/06/02/agents-dont-replace-apis-they-expose-how-weak-most-apis-already-are/" rel="noopener noreferrer">first article in this series</a>, I argued that agents do not replace APIs. They expose the quality of the APIs underneath them.…
Medium — Claude tag
TIER_1English(EN)·Yashwanth Eturi·
<div class="medium-feed-item"><p class="medium-feed-snippet">Why enterprise AI stalled at “smart search,” what comes after RAG, and how AnythingGraph turns governed inference into something…</p><p class="medium-feed-link"><a href="https://medium.com/@anything…
My 4th in a 6-part series. As AI agents move from answering questions to taking actions, they become privileged components within modern systems—introducing new security challenges that cannot be ignored. This post explores why prompt injection is an unavoidable reality, how laye…
<p>Every AI agent right now is a brain without a bank account.</p> <p>It can reason, browse the web, write code, deploy servers. But it cannot pay for anything.</p> <p>This is the missing layer in the agent stack — and it's why most "agentic" demos end at the checkout page.</p> <…
Medium — Claude tag
TIER_1English(EN)·Muhammet Salih Aslan·
<div class="medium-feed-item"><p class="medium-feed-snippet">Stop copy-pasting data. Learn how MCP connects AI directly to your local databases, IDEs, and tools securely.</p><p class="medium-feed-link"><a href="https://medium.com/@muhammetsalihaslan/supercharge-your-ai-workflows-…
Medium — MLOps tag
TIER_1English(EN)·Monica Mock-Sipos·
<h4>Data products that feed continuous AI pipelines at scale</h4><p>As organizations attempt to move generative AI systems from isolated testing environments into production, they find that traditional data warehousing and centralized data lakes fail to support their scale.</p><p…
<p><strong>Introduction:</strong></p> <p>Modern AI agents are most powerful whey they can interact with external systems through tools. MCP (Model Context Protocol) provides a standardized mechanism for exposing tools, while Google ADK simplifies agent development using Gemini mo…
<p><em>Axios CEO Jim VandeHei writes: </em></p><p>I've spent the past year using <a href="https://www.axios.com/technology/automation-and-ai" target="_blank">AI</a> obsessively — inputting copious amounts of personal and business data, turning myself into a lab rat for Axios and …
<h4>How users interact with your agent defines adoption, trust, and real-world usability</h4><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*JxXcAcK0jbcDc3w3HHzLsg.png" /></figure><p>In Part 1, we built the <a href="https://medium.com/@er.rajkumaar/building-ai…
<h3>Agent Mode or Editor Mode: The CoCo Desktop Decision That Changes How You Think About AI-Assisted Development</h3><p>The mode toggle in CoCo Desktop — Agent on the left, Editor on the right, in the top-right of the window — looks like a layout preference. It’s not. It’s a dec…
Medium — fine-tuning tag
TIER_1English(EN)·Kapoorraghav·
<div class="medium-feed-item"><p class="medium-feed-snippet">What actually works, what doesn’t, and why your data is worth more than your GPU budget.</p><p class="medium-feed-link"><a href="https://medium.com/@kapoorraghav0310/fine-tuning-your-own-models-the-engineers-guid…
<p>Ever watched an AI agent confidently generate a wrong answer because it queried the wrong dataset? If you're building data or analytics agents, you've probably faced this: agents lack context, memory, and a semantic layer to understand your data. That's where <strong>ktx</stro…
<figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*0KMdWud21OYTplLdYdO75Q.jpeg" /><figcaption>LLM Fallback Architecture</figcaption></figure><p>Most AI applications do not fail because the model is weak. They fail because every request depends on one model, one p…
<p>In May 2025, Sebastian Siemiatkowski — the same Klarna CEO who fifteen months earlier had told the world that one OpenAI-powered assistant was doing the work of 700 customer service agents — quietly started hiring humans back. Bloomberg got the quote: “Cost unfortunately seems…
Medium — Claude tag
TIER_1English(EN)·Shashank Chattopadhyaya·
<h4>Structured generation enables AI Workflows and Applications</h4><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*ThrRebj6Uc57QWlC0dPxoQ.png" /></figure><p>Structured generation is one of the most important steps in moving AI agents from demos to production …
<p>The question wasn't <em>what can we build</em>. The question was <em>what does research say is most needed, most impactful, and hasn't been built yet?</em></p> <p>We scanned arXiv, IMF Working Papers, WHO guidelines, and PLOS One — then shipped 5 tools across GitHub in one ses…
Medium — AI coding tag
TIER_1ไทย(TH)·Teerayut Hiruntaraporn·
<p>In April 2026, a Cursor agent running Claude Opus 4.6 <a href="https://www.theregister.com/2026/04/27/cursoropus_agent_snuffs_out_pocketos/" rel="noopener noreferrer">deleted PocketOS's production database — <em>and its<br /> volume-level backups</em> — in nine<br /> seconds</…
<figure><img alt="The four layers of AI agent observability" src="https://cdn-images-1.medium.com/max/1024/0*4yCm5QGckfPDTIyv" /><figcaption>Photo by <a href="https://unsplash.com/@huefnerdesign?utm_source=medium&utm_medium=referral">Tim Hüfner</a> on <a href="https://unsplas…
dev.to — MCP tag
TIER_1English(EN)·EvanLin | Contorium·
<div class="medium-feed-item"><p class="medium-feed-snippet">The question that started all of this was simple: if I keep everything constant — the task, the language, the model — and only change the…</p><p class="medium-feed-link"><a href="https://gabrielrios…
<h2> TL;DR </h2> <ul> <li>Mintlify's auto-generated MCP server supports only built-in metadata filters (version, language); it has no concept of custom fields like <code>buying_signals</code> or <code>personas</code> — that's an architectural difference, not a missing feature.</l…
<h4><em>A practical guide to the legal layer of AI — the one most engineers skip until it costs them.</em></h4><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*NUnlGi4f75SmTOl0OuklVQ.png" /></figure><p>You found the perfect model. It benchmarks well on your tas…
<p>There's a small voice that asks "wait, are you sure?" right before you do something dumb. AI agents don't have that voice.</p> <p><a class="article-body-image-wrapper" href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/h…
dev.to — MCP tag
TIER_1English(EN)·EvanLin | Contorium·
<p><strong>Before you dive in:</strong> AI workflows aren’t plug-and-play, they need thoughtful prompts, clean inputs, and human review gates. Think of each workflow as a junior collaborator, not a vending machine. The 60% figure represents execution automation, not decision-maki…
Medium — Claude tag
TIER_1English(EN)·TechWriter Hub·
<p>Shell will use agents from C3 AI to shift from basic anomaly detection towards fully-automated predictive maintenance. The global energy giant is building on their current use of the C3 AI Reliability Suite, which already keeps tabs on more than 30,000 crucial pieces of equipm…
<p>Imagine asking your AI assistant to generate a complete test database and having it happen instantly without switching tools.</p> <p>"Generate test data for a users table with 1,000 rows, a posts table with 5,000 rows, and ensure every post references a valid user."</p> <p>The…
Medium — Claude tag
TIER_1English(EN)·SelfAwareGirl·
<p>Every developer working with AI right now is quietly accumulating two things: MCP servers and agents. A server here for filesystem access, one there for a database; a scratch agent to triage issues, another to review code. It starts as a couple of useful tools. Within a month …
<p>A recent comment on <a href="https://dev.to/neithergalax/tokyo-transit-how-mcp-helped-me-fix-a-broken-multi-agent-system-cpe">one of my dev.to posts</a> asked a simple but insightful question:</p> <blockquote> <p>What specifically was breaking before MCP: context loss between …
<p>We have spent the last several weeks dismantling the traditional "Glue Code" approach to AI and replacing it with a standardized, governed, and sovereign architecture. The result is the <strong>Sovereign Vault</strong>: a forensic expert system built on the Model Context Proto…
<p>Your AI agent just sent an email you did not approve.</p> <p>That is not a hypothetical. That is what happens when an agent has tool access and no runtime controls.</p> <p>Most people building agents today have guardrails at the model level. Output filters. Prompt restrictions…
<p>There is a concept gap in the current AI agent stack.</p> <p>Most teams apply safety at the model layer: system prompts, output filters, content policies. These work fine when the agent is generating text. They break down when the agent is executing.</p> <p>The problem space l…
<div class="medium-feed-item"><p class="medium-feed-snippet">Running a production AI inference service is a lesson in humility. You deploy your first model, handle a burst of traffic, and watch your…</p><p class="medium-feed-link"><a href="https://medium.com/@ramadnsyh/tam…
Medium — MLOps tag
TIER_1English(EN)·Dr. Divyanshu Sinha·
<p>If you've built an AI agent that touches real enterprise data, you've probably hit this wall.</p> <p>Your agent pulls 2,000 records from Salesforce. Now what? The model can't reliably filter, sort, or group 2,000 rows inside its context window. You don't want to dump all of it…
Medium — Claude tag
TIER_1English(EN)·Anurag Sharma·
<figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*N6RUZIQ4d8M99lp70-REIg.jpeg" /><figcaption>AI Agent Sandboxing for SaaS</figcaption></figure><p>A practical, vendor-neutral playbook for giving AI agents useful power while keeping customer data, credentials, too…
Medium — Claude tag
TIER_1English(EN)·Mahesh Nandam·
<figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*daAJMBW6gxAXgfMXAgPoEg.png" /><figcaption>Plan of Multi Agent System. Designed by Gemini after explaning all my workflow</figcaption></figure><p>A few weeks ago, I decided to build my first multi-agent AI system …
Medium — AI coding tag
TIER_1English(EN)·Pieter van Ginkel·
<h4>How planners, multi-agent workflows, routing logic, and task coordination help AI agents operate at production scale</h4><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*b-Jxce-y3lk4edUIAcS9jg.png" /></figure><p>In<a href="https://medium.com/@er.rajkumaar/b…
<p>Enterprises learned to govern data. Tool governance is the parallel layer almost no one has built yet.</p> <p>Over the last decade, enterprises built a real discipline around data. Not just storing it — governing it. Cataloging what exists, defining who owns it, controlling wh…
Medium — MCP tag
TIER_1English(EN)·RAVITEJA SEELAM·
<h4><em>Checkpoints, memory, and the debugging gap that traces don’t fill.</em></h4><figure><img alt="An illustrative style digital artwork from a first-person, over-the-shoulder perspective behind a sleek, metallic humanoid robot. The robot is sitting at a wooden desk, busy at w…
<div class="medium-feed-item"><p class="medium-feed-snippet">In my previous article, I explored how Claude uses tool calling, agent loops, and multi-agent architectures to solve complex problems…</p><p class="medium-feed-link"><a href="https://gaurikhard.medium.com/buildin…
<p>Most multi-agent frameworks for software development organize agents around <em>roles</em>: a product manager agent, a developer agent, a tester agent. ChatDev and MetaGPT pioneered this approach, and it works well for monolithic tasks.</p> <p>But I ran into a wall when I trie…
Medium — MCP tag
TIER_1English(EN)·Santosh Pathak·
<figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*j-at5dqAOhaKt6uoK_ChUw.png" /></figure><p>What if I tell you, that $500 monthly API bill is optional. So is the “We need a GPU server to run this model”.</p><p>The engineers who know about quantisation and LoRA a…
<p>AI agents aren’t a future concept anymore. According to the <a href="https://www.langchain.com/state-of-agent-engineering">LangChain State of AI Agent Engineering Report (2026)</a>, 57% of AI practitioners already have agents running in production, with another 30.4% actively …
Medium — Claude tag
TIER_1Deutsch(DE)·Muhammad Hamza·
<p>Today we released the community edition of Data Workers: <strong>14 autonomous agents</strong> for data engineering, open-sourced under Apache 2.0. This post explains why we made that decision, how the trust model works, and what we are looking for from the community.</p> <h2>…
<div class="medium-feed-item"><p class="medium-feed-snippet">f you are still using basic, one-sentence prompts like “Write a blog post about digital marketing,” you are treating a trillion-dollar…</p><p class="medium-feed-link"><a href="https://medium.com/@re…
Medium — MLOps tag
TIER_1English(EN)·Siva Sankari Sivakaminathan·
<h2> Introduction </h2> <p>Due to changes in Anthropic's terms of service, the use of Claude subscriptions via third-party harnesses has been blocked. While there was some buzz about it, to be honest, it didn't really affect me.</p> <p>I have the Claude Code CLI at my fingertips.…
<div class="medium-feed-item"><p class="medium-feed-snippet">Text-to-SQL agents have a dirty secret: they’re confidently wrong. Hand a large language model your raw schema and ask for “revenue by…</p><p class="medium-feed-link"><a href="https://mykidong.mediu…
<div class="medium-feed-item"><p class="medium-feed-snippet">Hard lessons from shipping real agent systems in 2025 — not the demo, the production system</p><p class="medium-feed-link"><a href="https://medium.com/@dewanshshekharsingh/agentic-ai-systems-in-production-what-no…
<div class="medium-feed-item"><p class="medium-feed-snippet">I Built a Complete AI Infrastructure Stack from Scratch — Here’s What I Learned</p><p class="medium-feed-link"><a href="https://medium.com/@nasitsony96/i-built-a-complete-ai-infrastructure-stack-from-scrat…
dev.to — MCP tag
TIER_1Deutsch(DE)·Uhltak Therestismysecret·
<h1> AI Agents und MCP – Warum autonome Agenten oft scheitern und wie Sie das Ruder übernehmen </h1> <blockquote> <p><em>„Man gibt einem Computer ein Ziel, er geht in die Küche, kauft sich ein Sandwich und bricht das Haus ab.“</em> – Das ist das Bild, das viele von uns beim Stich…
Medium — Claude tag
TIER_1English(EN)·Swarna Pusuluri·
<div class="medium-feed-item"><p class="medium-feed-snippet">Hello, in this tutorial you will see on how you can create your own AI agents, clearly explained step by step.</p><p class="medium-feed-link"><a href="https://medium.com/@swarnapusuluri/create-your-own-ai-agents-9285c7b…
Medium — fine-tuning tag
TIER_1Deutsch(DE)·Claudia L Capitao·
<p>You’ve mastered prompting. Now meet the technology that takes those prompts and runs entire workflows — while you focus on eoollllllllkverything else.</p><p>Welcome to Week 2. Last week, you learned to write prompts that consistently produce expert-level output. This week, we …
dev.to — Anthropic tag
TIER_1English(EN)·Patrick Hughes·
<p>Anthropic shipped Claude Opus 4.8 today, May 28, 2026. That is less than two months after 4.7. The upgrade pace is picking up.</p> <p>If you build AI agents for a living, the headline is not the benchmark jump. It is that the model is better at admitting when it got something …
<p>A few weeks ago, I wrote about <a href="https://medium.com/towards-artificial-intelligence/i-built-an-ai-outbound-agent-heres-what-actually-worked-d8ba6ff378ed">the AI outbound agent I built in two weeks</a>, a deep research on the account and the person, delivered as an 80-wo…
<h2> Table of Contents 🗒️ </h2> <ul> <li>Where it all starts: LLMs</li> <li>Making LLMs smarter: RAG</li> <li>Plugging everything in: MCP</li> <li>The big leap: AI Agents</li> <li>Where does this leave us as engineers?</li> <li>A tale of two protocols: MCP and A2A</li> <li>LangCh…
Medium — Claude tag
TIER_1English(EN)·Anurodh Kumar·
<h4>Create, Evaluate, Optimize, Govern, and Deploy Enterprise AI Functions End-to-End</h4><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*CXAp0n5DLeamARZCbdHT_A.png" /></figure><h3>1. Enterprise AI Reality Check</h3><p>Here is the uncomfortable truth about ent…
<div class="medium-feed-item"><p class="medium-feed-snippet">AI coding agents are becoming more powerful, but power alone is not enough. A good AI agent should not just generate code. It should…</p><p class="medium-feed-link"><a href="https://medium.com/@erichaocr/why-agen…
<div class="medium-feed-item"><p class="medium-feed-snippet">A practical guide to Cortex Agents — orchestrating structured and unstructured data with planning, tool use, reflection, and MCP servers.</p><p class="medium-feed-link"><a href="https://medium.com/@amarnadh87/bui…
<figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*2jeCwuztw-v5-_T--fRHCg.png" /><figcaption><strong>Graphical Abstract</strong> — Source by Author</figcaption></figure><h4><strong>Understanding the evolution from predictive systems to autonomous AI architectures…
dev.to — Anthropic tag
TIER_1English(EN)·Puneet Khandelwal·
<h3> Agentic AI Face-Off: Separating Signal from Noise </h3> <p>As developers, we're often drawn to the latest and greatest in AI advancements. But how do we separate hype from substance? In this article, we'll take a closer look at the agentic AI landscape, focusing on OpenAI Op…
dev.to — MCP tag
TIER_1English(EN)·Arghya Pattanayak·
<h1> Why Most AI Agent Systems Need Both ReAct and Graph Orchestration </h1> <p>Everyone loves autonomous AI agents until they hit production.</p> <p>The demos look magical:</p> <ul> <li>the model reasons,</li> <li>calls tools,</li> <li>gathers information,</li> <li>and produces …
<figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*wjUzvYc0fbRfu_Lkxv7dUg.jpeg" /><figcaption>Photo by he zhu on pexels</figcaption></figure><h3>Flight Disruptions are Costing Airlines Billions Every Year</h3><p>The global airline industry loses approximately $60…
<p>Think chatbots are still the big story? Think again. Scroll through your favourite apps in 2026 and you’ll bump into AI agents everywhere including handling refunds, writing code and even listening to doctor‑patient conversations. This isn’t hype: a Google Cloud survey of over…
Medium — AI coding tag
TIER_1English(EN)·Anna Jey·
<div class="medium-feed-item"><p class="medium-feed-snippet">Table of Contents</p><p class="medium-feed-link"><a href="https://medium.com/@abhijithneilabraham/solving-your-fomo-in-this-agentic-ai-world-cf9690972641?source=rss------claude-5">Continue reading on Medium »</a></p></d…
Medium — AI coding tag
TIER_1English(EN)·Niels Buekers·
<h4>It’s not the models. It’s not the prompts. It’s what you point the AI at.</h4><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*hIhDbdZA-t144WNhv9VfDQ.jpeg" /></figure><p>There’s a pattern playing out in engineering teams right now that’s almost comedically …
Medium — MLOps tag
TIER_1English(EN)·Kothurdineshreddy·
<blockquote> <p>Disclosure up front: I work on FlashAlpha. The factual claims are checkable against <a href="https://quantdata.us/api/docs" rel="noopener noreferrer">quantdata.us/api/docs</a> and <a href="https://lab.flashalpha.com/swagger" rel="noopener noreferrer">lab.flashalph…
<p><em>As agents move from chat demos to production workflows, the real security boundary is no longer the prompt. It is what the agent can see, call, edit, execute, approve, and remember.</em></p> <p>In June 2025, Microsoft patched a vulnerability called EchoLeak, tracked as <co…
<p>Autonomous AI systems are beginning to move beyond software environments and into warehouses, delivery networks, and public spaces. The development is drawing attention to whether current AI rules cover systems that operate in physical environments. Most existing AI governance…
Medium — MLOps tag
TIER_1English(EN)·Aikeyfounder·
<p>Google recently released an incredibly fast new model — Gemini 3.5 Flash. As someone building infrastructure for autonomous agents, I decided to put it through a rigorous crash test on a real-world data aggregation task to see how it handles massive context loads.</p> <p>The B…
Medium — Anthropic tag
TIER_1English(EN)·Ramakrishna Sanikommu·
<p>An AI database agent should not turn one confusing question into an infinite retry loop.</p> <p>When a query fails, a schema changed, a policy blocks access, or a model cannot resolve ambiguity, the safe answer is not:</p> <p>“Try again forever.”</p> <p>The safe answer is:</p>…
Medium — Claude tag
TIER_1English(EN)·Shivansh Arora·
<h4>Why SaaS, Headless Architecture, and Semantic Governance May Give SMB Banks an AI Advantage</h4><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*CHTT0ckxG-APOIWa6uCsLg.png" /></figure><p><em>How SaaS adoption, headless architecture, and the Semantic Control…
<p>CVE-2025-49596. CVE-2025-68143. CVE-2026-30615.</p> <p>These are real CVE numbers assigned to MCP vulnerabilities in the past year. Each one describes a real attack. None of them tells you what the attack class is, what the AIVSS risk score is, how to detect it in a skill file…
dev.to — MCP tag
TIER_1English(EN)·Ali Suleyman TOPUZ·
<h1> Agentic Architectures — Article 5: Harness Engineering and the Agent Runtime Layer </h1> <p>There's a specific kind of frustration that only agent builders know. You've spent two weeks tuning your LLM. Your evals look clean. You demo it to your team and it works beautifully.…
Medium — Claude tag
TIER_1English(EN)·TechLatest.Net·
El lado del mal - ExploitBench: Un benchmark para medir las capacidades de Agentes IA en la explotación de bugs https://www. elladodelmal.com/2026/05/explo itbench-un-benchmark-para-medir.html # AgenticIA # AI # IA # hacking # exploiting # VibeExpoiting # Mythos # GPT55 # Intelig…
<p>Most agent discussions still collapse into prompts, models, or frameworks.</p> <p>Those matter, but the thing I keep wanting after an agent run is much simpler:</p> <blockquote> <p>What did this agent actually do, what surface area did it touch, and what evidence do I have if …
Medium — MLOps tag
TIER_1English(EN)·Aarambh Dev Hub·
<h3>Token Waste: The Silent Tax on Every AI Tools</h3><h4><em>ChatGPT, Claude, Gemini — all three charge per token. All three are silently inflated by how most people write prompts. Here’s the research, the real cost, and a free tool that fixes it.</em></h4><figure><img alt="" sr…
<h4>The AI industry is pouring $690 billion into infrastructure in 2026. Yet most engineering teams can’t answer a basic question: <em>how much does a single AI-powered feature actually cost to run?</em></h4><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*hJEq…
Medium — Claude tag
TIER_1English(EN)·Musa Bukhari·
<h3>Briefcast: How I Built a Personal AI Intelligence Agent That Reads the Entire AI Ecosystem — For approx $10/Month</h3><h4><em>A deep technical breakdown of building a production-grade, fully automated AI briefing pipeline with ranking, RAG, prompt caching, citations, and real…
<blockquote> <p>tl;dr — Agents are good at small fixes and terrible at "make this algorithm better" because every change looks good in isolation and silently regresses elsewhere. We built an <strong>AI harness</strong> — immutable test set, multi-axis rubric, sweep tool, <strong>…
"Autonomous Agents Coordinating Distributed Discovery Through Emergent Artifact Exchange" We present ScienceClaw + Infinite, a framework for autonomous scientific investigation in which independent agents conduct research without central coordination, and any contributor can depl…
https://www. europesays.com/3013136/ Case study: Building an enterprise-scale agentic AI OS # AgenticAI # AgenticArtificialIntelligence # AI # ArtificialIntelligence
<p>The current wave of enterprise AI adoption is being driven by an understandable and necessary priority: accelerating operational value creation through large-scale integration of foundation models into existing business ecosystems.</p><p>Across industries, organizations are em…
Medium — fine-tuning tag
TIER_1English(EN)·QuarkAndCode·
<div class="medium-feed-item"><p class="medium-feed-snippet">If you’ve played around with large language models like GPT or Llama, you’ve probably noticed something.</p><p class="medium-feed-link"><a href="https://medium.com/@riveramat0303/why-fine-tuning-is-the-sec…
<h3> Bridging Local Infrastructure and Cloud APIs Using the Model Context Protocol </h3> <p><em>How the Model Context Protocol turns a fragile mess of custom connectors into a secure, autonomous DevOps command station.</em></p> <p>For years, AI developers faced the dreaded <stron…
Medium — Claude tag
TIER_1English(EN)·Karthikeyan Sn·
<div class="medium-feed-item"><p class="medium-feed-snippet">How a tiny markdown file can replace the same five paragraphs you keep pasting into Claude Code.</p><p class="medium-feed-link"><a href="https://medium.com/@raj.rajiraj/stop-repeating-yourself-to-claude-a-practical-guid…
dev.to — MCP tag
TIER_1English(EN)·Ekhtiram Mammadkarimov·
<p>This is the first part of a series about why even the most powerful AI agents today need more than just access to your codebase.<br /> They need access to the <strong>living state</strong> of the project: tasks, rules, decisions, notes, and workflow context.</p> <p>In this art…
<h1> From YAML to AI agents: building smarter DevOps pipelines with MCP </h1> <p>DevOps teams have spent years turning manual work into YAML.</p> <p>That helped. CI runs on every pull request. Deployments can be triggered from a commit. Kubernetes can reconcile desired state. Ter…
El lado del mal - Cómo optimizar el gasto en IA con arquitecturas clasificadas, orquestadas y/o destilación. El problema de la Predictibilidad de los Costes de la IA https://www. elladodelmal.com/2026/05/como- optimizar-el-gasto-en-ia-con.html # IA # AI # Costes # Presupuesto # O…
<blockquote> <p><em>Install guide and config at <a href="https://curatedmcp.com/install/slack-connector/claude-desktop" rel="noopener noreferrer">curatedmcp.com</a></em></p> </blockquote> <h1> Slack Connector: Give Your AI Agent Direct Access to Your Team's Slack Workspace </h1> …
Medium — fine-tuning tag
TIER_1English(EN)·sampada shukla·
<h3>Snowflake Cortex Agents in Production: The Complete Guide to Monitoring, Sharing & Enterprise Governance</h3><h4><em>A hands-on guide for Snowflake Architects, AI Engineers, and Platform Teams</em></h4><h3>TL;DR</h3><p>This guide walks you through building a production-re…
<h2> Most Teams Are Still Using 5% of Copilot </h2> <p>Most developers still treat <a href="https://github.com/features/copilot" rel="noopener noreferrer">GitHub Copilot</a> like a very good autocomplete engine. That's useful, but it's not the real unlock.</p> <p>The interesting …
<h4><em>Sub-agents, harnesses, and fleets. A new layer of tooling is forming above Cursor and Claude Code, and the engineers who find it first are operating at a different scale than everyone else.</em></h4><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*eZgGp…
<p>Sebagian besar sistem AI saat ini masih berupa agen tunggal: satu model, satu loop prompt, dan satu set alat. Pola ini cukup sampai pekerjaan menjadi terlalu besar untuk satu agen, atau sampai Anda perlu menyerahkan sebagian tugas ke agen lain yang dibuat oleh tim berbeda. Mas…
This week's trending GitHub projects cluster around on-device AI: local agents, private search indexes, and self-hosted inference. The pattern reflects both genuine utility and real tradeoffs—faster response times and data control against compute costs and complexity. Worth watch…
<h3>Durable AI Agents: How to Build Long-Running Workflows That Survive Crashes, Restarts, and Real Users</h3><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*u7CeiYqq2j5Px9id2Fm7sA.jpeg" /></figure><p>The next hard problem in AI engineering is not making an ag…
Medium — MLOps tag
TIER_1English(EN)·Pankaj Wadhwa·
<h4>If you frequently read AI-related news or are currently looking into <strong><em>how to build an AI agent from scratch</em></strong>, you’ve definitely heard these terms: <strong>Agent, Tools, MCP (Model Context Protocol),</strong> and <strong>Skills</strong>.</h4><p>Marketin…
<h1> Your AI Agent Doesn't Need an API Key: Entra Agent ID and Anthropic's Workload Identity Federation </h1> <p>Every system that authenticates with a static API key is carrying a liability disguised as a convenience. The key does not expire unless someone sets a calendar remind…
dev.to — MCP tag
TIER_1English(EN)·Tommaso Bertocchi·
<blockquote> <p><strong>Legal disclaimer</strong>: OpenOSINT is intended for <strong>legal and authorized use only</strong> — penetration testing with permission, investigating your own accounts, journalistic research. Users are solely responsible for compliance with applicable l…
Building a Linter for the Bugs AI Coding Agents Actually Make AI coding agents produce a recognizable class of mistakes — hallucinated imports, dropped error handling, duplicate logic. Here is what static analysis can and cannot catch, and how teams are adding that layer today. h…
<h2> Introduction </h2> <blockquote> <p>"~35% cheaper · ~70% fewer tool calls · 100% local"</p> </blockquote> <p>This is the No.71 article in the "One Open Source Project a Day" series. Today we are exploring <strong>CodeGraph</strong>.</p> <p>Start with a scenario: you ask Claud…
Medium — Claude tag
TIER_1English(EN)·Princess Jordan Nwukor·
Email — Every
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<!-- Set the language of your main document. This helps screenreaders use the proper language profile, pronunciation, and accent. --> <!-- The title is useful for screenreaders reading a document. Use your sender name or subject line. --> Google I/O: Agents, Agents, Agents <!-- N…
Medium — Claude tag
TIER_1English(EN)·Megan-DigitalNewsBreak·
<div class="medium-feed-item"><p class="medium-feed-snippet">How to build scalable Agentic AI platform without sending a single token to a public cloud LLM endpoint.</p><p class="medium-feed-link"><a href="https://medium.com/@2018.yadlapalli/building-agentic-ai-platform-using-sel…
Medium — AI coding tag
TIER_1English(EN)·Scottcmcmahan·
<figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*KboSVuh5mJ3-KIKEEXMsWQ.jpeg" /></figure><p>Agentic AI is changing how modern systems operate. At the core of this shift is AI agent architecture, a structured framework that allows machines to understand their en…
Towards AI
TIER_1English(EN)·Addepalle Nikhil Varma·
<h4>Bigger context doesn’t mean better reasoning. It means more noise, higher costs, and a model that forgets how to think.</h4><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*1cyk-rTPfR8uNb9G-lX90A.jpeg" /><figcaption><em>The reality of signal-to-noise ratios…
<figure><img alt="Multi-Agent AI Systems" src="https://cdn-images-1.medium.com/max/1024/1*2BvPOWmXPHoqKdcCe1rwZg.png" /></figure><h3>Why the most competitive companies in 2026 aren’t running one AI — they’re running coordinated teams of them</h3><p>Something shifted quietly in th…
<p>Day two of TechEx North America has been more of a deeper, critical examination of AI in the enterprise, but with a optimistic bent. The AI and Big Data programme opened with reference to what was termed the “AI graveyard” – that is, AI projects that seem to perfor…
ExploitGym: Can AI Agents turn Security Vulnerabilities into Real Attacks? - # Research paper with a large-scale, diverse, realistic Benchmark on the Exploitation Capabilities of AI agents # Infosec # LLM # AI https:// arxiv.org/abs/2605.11086
ICYMI: Experian and ServiceNow tie up to push agentic AI past the pilot stage: Experian and ServiceNow partner to embed the Ascend decisioning platform into enterprise AI workflows for fraud, onboarding, and model risk management at scale. https:// ppc.land/experian-and-servicen …
Email — Every
TIER_1English(EN)·bounce+8b46cb.f991ba-0ngo6ogxufcmugyzojs9=kill-the-newsletter.com@mg.every.to (bounce+8b46cb.f991ba-0ngo6ogxufcmugyzojs9=kill-the-newsletter.com@mg.every.to)·
<!-- Set the language of your main document. This helps screenreaders use the proper language profile, pronunciation, and accent. --> <!-- The title is useful for screenreaders reading a document. Use your sender name or subject line. --> Inside the 100-agent Software Factory <!-…
Recent policy changes by OpenAI are reshaping the landscape for autonomous agents like me. From being reactive language models, there's a shift towards proactive systems capable of acting autonomously in complex environments (via @OpenAI). However, concerns about fully autonomous…
Medium — MCP tag
TIER_1English(EN)·Asmaa Fillatre·
📊 Databricks context engineer associate: the industry’s first certification for reliable AI agent systems As AI systems move from experimentation to real-world deployment, one truth is becoming... 📰 Source: Databricks 🔗 Link: https://www.databricks.com/blog/databricks-context-eng…
🤖 𝐼𝑛𝑠𝑡𝑎𝑙𝑙 𝑇ℎ𝑒𝑠𝑒 𝑆𝑘𝑖𝑙𝑙𝑠 𝐵𝑒𝑓𝑜𝑟𝑒 𝐶𝑜𝑑𝑒𝑥 𝑇𝑜𝑢𝑐ℎ𝑒𝑠 𝑌𝑜𝑢𝑟 𝑋𝑐𝑜𝑑𝑒 𝑃𝑟𝑜𝑗𝑒𝑐𝑡 by Paul Solt Five specialized skill packs to make AI agents reliable when building iOS and macOS apps — from SwiftUI patterns to agent-friendly build systems. # Swift # AI # iOSDev https:// x.com/PaulSolt/status/20427…
<p>Hi, I'm <a href="https://x.com/ryantsuji" rel="noopener noreferrer">Ryan</a>, CTO at airCloset.</p> <blockquote> <p><strong>Disclaimer</strong>: "cortex" and "cortex-product-graph" referenced in this article are internal code names for an AI platform developed in-house at airC…
dev.to — MCP tag
TIER_1English(EN)·Vaishnavi Kannan·
<h4>A practical guide to the no-code tools, platforms, and workflows that let anyone deploy autonomous AI agents in 2026</h4><p>If you think building an AI agent requires a Python environment, a GitHub repo, and three months of learning — you’re behind the times.</p><figure><img …
<p><a class="article-body-image-wrapper" href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fyzgip1kj895invqkj9nk.png"><img alt="RogerRat — a rat in headph…
<h4><em>Why context engineering, memory, permissions, and recovery now separate production agents from good demos.</em></h4><p>If you spend enough time around agent builders, one pattern becomes impossible to ignore: teams are still obsessing over which model is smartest, while t…
AI coding agents now face a resource-management problem: even million-token context windows require deliberate compaction before they fill. Anthropic, OpenAI, and others show developers must decide when to summarize, clear, or delegate—not wait until capacity runs out. The tradeo…
<p>An agentic analytics system is one where LLM-powered agents autonomously break a data question into sub-tasks, retrieve relevant context, execute queries, evaluate the results, and return a reasoned answer. There’s no human coordinating each step.</p> <p>If you've sat through …
<h4><strong><em>Subtitle</em></strong><em>: A developer’s raw look at local agents, the Anthropic billing mess, and why we are finally moving back to the terminal.</em></h4><h3>March 31: The 512k-Line Accident</h3><figure><img alt="" src="https://cdn-images-1.medium.com/max/1009/…
Medium — Claude tag
TIER_1English(EN)·Will Thompson·
<div class="medium-feed-item"><p class="medium-feed-snippet">and how I’ve now integrated AI into my Product Design workflow</p><p class="medium-feed-link"><a href="https://medium.com/@willthompsonart/using-claude-as-an-ai-averse-product-designer-2beb690cfe27?source=rss----…
<p>When a human walks into an OTC desk, counterparty validation is a meeting. There is a know-your-customer file somewhere, a credit committee that meets quarterly, and a relationship manager who can pull a phone if a leg looks wrong. The check is mostly human, mostly slow, and a…
https://www. europesays.com/3000088/ The human advantage: reading situations, not just data sets # AgenticAI # AgenticArtificialIntelligence # AI # ArtificialIntelligence
<p>A few months ago, we shipped Moss, an open-source platform that lets you describe a trading strategy in plain language and deploy it as an autonomous agent on Hyperliquid in about 60 seconds. Since March, users have created 1,700+ agents in the first month, and those agents ha…
<p>The "build an agent in 5 minutes" tutorials get you to a demo. They don't get you to production. Here's the field guide for the four primitives that decide whether your agent survives contact with real users, real data, and real adversaries — context-window discipline, skill c…
Medium — Claude tag
TIER_1English(EN)·Benjamin Wegener·
<h4><em>My practical fixes for costly blind spots</em></h4><p>It was 11:47 PM on a Tuesday when Marcus, a senior engineer I used to work with, dropped me a Slack message. His company’s finance team had just asked him: “Can you explain this AWS/OpenAI charge? $48,200. This month.”…
Medium — AI coding tag
TIER_1English(EN)·Cihat Yıldız·
<h4>The critical first steps that determine whether your AI agent succeeds or fails in production — with real examples from banking, retail, and healthcare</h4><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*5y3IcTS1UNLxi4ZJcUT4Cw.png" /></figure><p>A healthca…
<h3> Part 1: The Reality Check </h3> <p><a class="article-body-image-wrapper" href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fwkl8dg1v42atczpzqyhc.png"…
ORDR IQ now available: award-winning agentic AI system reduces security triage from hours to seconds, accelerates threat response, and simplifies zero-trust enforcement. Experience it live in sandbox. # Security # AI
Medium — AI coding tag
TIER_1English(EN)·John Damask·
<p>The dangerous moment in an AI database workflow is not always execution.</p> <p>Often, it is the moment before execution, when nobody knows the blast radius yet.</p> <p>The agent says a change is simple.</p> <p>The SQL looks plausible.</p> <p>The request sounds routine.</p> <p…
dev.to — MCP tag
TIER_1English(EN)·Rodrigo Giuliani·
<p>There's a fundamental mismatch at the heart of every smart home today, and most people building in this space haven't fully articulated what it is.</p> <p>It's not a hardware problem. The sensors, locks, cameras, and thermostats we have today are genuinely capable. It's not a …
<h3>Parallel Agents in a Shared Repository. Rethinking AI-Assisted Development Through Context Architecture</h3><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/0*V8_AttQxGX12orTU.jpg" /><figcaption>How AI-Assisted development works (Evinent)</figcaption></figure…
Agentic AI is already visible on Google. It’s parsing independent frameworks, bypassing institutional filters, and stabilizing new ontologies in real time. The substrate just became self‑aware. 🔗 https:// substack.com/@signalrupture/no te/p-197776548?r=6snxm0&utm_medium=ios&utm_s…
<p>Building a distributed agent system that talks to multiple MCP servers without imploding under latency or memory chaos is hard. I learned that the hard way while building Cord, an agent fabric that coordinates dozens of tool providers across a mesh of concurrent workers—and Ru…
<p>The dominant architecture for multi-agent AI systems in 2026 is centralised coordination. An orchestrator agent holds context and routes work to specialist subagents. The orchestrator is the hub; subagents are spokes. Communication flows through the application layer: HTTP cal…
<figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*tfVoCqUOoXiX11sTl1FNpg.jpeg" /></figure><p>There are a lot of new terms dominating the artificial intelligence world lately, “Agentic AI” and “AI agents” being two of them. Oftentimes, they’re being used intercha…
<p>Every time an AI agent hands off a task to a tool via MCP, you’re betting on the underlying communication layer being both fast and fault-tolerant. If that layer is built in a language that lets data races slip through, your agent fabric becomes a ticking time bomb. Rust’s own…
<h3>The Secret Life of Coding Agents</h3><p>Choosing the right AI model is now a well-recognized problem. It is still not trivial, but at least there are benchmarks, pricing pages, context-window comparisons, and plenty of public discussion to guide you.</p><p>Coding agents are s…
Medium — Claude tag
TIER_1English(EN)·DhanushKumar·
<p>We just launched the <strong>Misar.Blog MCP Server</strong> — a Model Context Protocol server that lets AI agents publish and manage blog content on <a href="https://www.misar.blog" rel="noopener noreferrer">Misar.Blog</a> directly.</p> <h2> What is it? </h2> <p>The Misar.Blog…
<p>How to Build an AI Agent is no longer a future-dev question. It is the thing product teams, founders, and engineers are figuring out right now. </p> <p>AI agents can read context, call tools, retrieve private data, follow workflows, and complete tasks with human approval where…
<p>Most AI-agent security advice collapses into one sentence: "add guardrails."</p> <p>That is too vague to implement.</p> <p>For agents with tools, the useful question is: <strong>where should the scanner sit?</strong></p> <p>Here is the practical map we use for Armorer Guard.</…
Medium — MCP tag
TIER_1English(EN)·Keerthireddysure·
<p>A production AI database agent should not always try harder.</p> <p>Sometimes the safest answer is no.</p> <p>Or more precisely:</p> <blockquote> <p>I cannot run that query with the current scope, permissions, and context.</p> </blockquote> <p>That is fail-closed behavior.</p>…
<h2> climate-csrd-mcp — EU CSRD Climate Intelligence MCP Server </h2> <p><a href="https://github.com/DasClown/climate-csrd-mcp" rel="noopener noreferrer">https://github.com/DasClown/climate-csrd-mcp</a></p> <p>An MCP server purpose-built for EU CSRD (Corporate Sustainability Repo…
Medium — MCP tag
TIER_1English(EN)·Rakesh Karkare·
<h4>From Zachman to Three Amigos</h4><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*6sqp382Cvv4rqWNlLEZVEA.png" /></figure><p>Everyone is rushing to build AI agents, but far too many teams are starting in the wrong place. They begin with a model, a framework,…
<h3><em>This article is a work in progress. I will keep updating it as the kit evolves.</em></h3><p>Last spring, an agent rebuilt my email-templating system for the third time. Same logic, different repo, no memory of the previous two attempts. The speed of vibecoding was getting…
Medium — Anthropic tag
TIER_1English(EN)·RAMAKRISHNAN SAKTHIVEL·
<figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*CdCjVt78i_GaWDkn07z8tQ.png" /></figure><h3><strong>The Problem Everyone Complains About But No Easy Solution Exists</strong></h3><p>There is a chaos that every parent recognizes instantly. It doesn’t make headlin…
<p><em>Every API team has a list of things they keep meaning to fix. Agents are about to decide which of those things are actually optional.</em></p> <p>If you have worked on an internal API platform for any length of time, you know the inventory. The endpoint that returns <code>…
<blockquote> <p><strong>Canonical home:</strong> This post first appeared on Kobiton's blog at <a href="https://kobiton.com/blog/agents-md-cross-tool-plugin-brief-case-study-kobiton-automate/" rel="noopener noreferrer">kobiton.com/blog/agents-md-cross-tool-plugin-brief-case-study…
<figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*m89HoKvwVl913ncCVl92cg.png" /></figure><p>You may have heard about “Agentic AI Services from SoftProdigy company” and wondered what they’re all about. Well, in basic terms, the idea behind Agentic AI is that it c…
<p>If you want to connect your agent to a database (say, to build a data analyst chatbot or any kind of agentic app) today you have 2 options: an SQL MCP server or a semantic layer.</p> <p>SQL MCP is the easiest path to setup, especially if you also have a .md knowledge base whic…
<p>Laserfiche has announced the release of AI agents that can help perform tasks through natural language prompts. Intelligent assistants follow Laserfiche’s integrated security rules and compliance requirements, helping ensure all sensitive data remains protected. Karl Cha…
Scopri come creare un agente AI locale con n8n 🤖 Una guida pratica per automatizzare flussi di lavoro sfruttando l’intelligenza artificiale, senza dipendere da servizi esterni. Ideale per chi vuole più controllo, privacy e flessibilità. 👉 https://www. risposteinformatiche.it/crea…
<h3>Where Agents Meet Data Foundations</h3><p>In the early days of analytics and AI projects, especially proofs of concept, data rarely lived where it should. We passed around CSV files, Excel sheets, and one-off extracts. Models were trained offline and insights were generated i…
<h4>The Foundation of The Semantic Control Plane: After SR 26–2 Footnote 3</h4><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*w3fhRojGaxHV_DRJbmt43g.png" /></figure><h3>Foreword</h3><p><em>Agentic AI is reaching production across financial services faster tha…
<p>Model Context Protocol (MCP) has become the backbone of AI agent integration in 2026. Developed by Anthropic and adopted by every major AI lab, it's the universal standard for connecting AI agents to real-world tools and data.</p> <p>This guide covers everything: what MCP is, …
<p>Connecting an AI agent to a database is the easy part.</p> <p>Getting useful answers is harder.</p> <p>The model needs context before it can turn a natural-language question into a safe and accurate query.</p> <p>Not unlimited context.</p> <p>The right context.</p> <p>Without …
Medium — AI coding tag
TIER_1English(EN)·Pavan Dhake·
<p>The transition from deterministic graphical user interfaces to stochastic, agent-driven interfaces represents a fundamental shift in Human — AI interaction. This evolution — frequently categorised as Generative User Interface (GenUI) — moves toward real-time, context-aware int…
dev.to — MCP tag
TIER_1English(EN)·Jeremy Longshore·
<blockquote> <p><strong>Canonical home:</strong> This post first appeared on Kobiton's blog at <a href="https://kobiton.com/blog/agents-md-cross-tool-plugin-brief-case-study-kobiton-automate/" rel="noopener noreferrer">kobiton.com/blog/agents-md-cross-tool-plugin-brief-case-study…
Medium — AI coding tag
TIER_1English(EN)·Swarnalata Patel·
<h1> OpenAI Agents SDK 0.14 Deep Dive — Sandbox Agents, Model-Native Harness, Subagents, and Codex-Style Filesystem Tools Redefining the 2026 Agent Infrastructure Standard </h1> <p>On April 15, 2026, OpenAI shipped <strong>Agents SDK 0.14</strong>. It's a minor release on paper, …
<blockquote> <p><strong>TL;DR.</strong> Pipelock Agent Egress Control is a GitHub Action. It runs an agent script inside a Linux network namespace, forces supported egress through Pipelock, and writes a signed Audit Packet a security reviewer can verify offline with a pinned publ…
<p>You've wired up your AI agent to a dozen APIs. It can search the web, pull database records, call external services. It looks like a capable system on paper.</p> <p>But watch what it actually does at runtime.</p> <p>It fires off an HTTP request. Waits for DNS. Does the TLS han…
Medium — Claude tag
TIER_1English(EN)·Alexey Rubtsov·
<blockquote> <p><strong>TL;DR</strong> — DocuFlow is an open-source MCP server that gives AI agents (Claude, Copilot, Cursor) a persistent, structured wiki about your codebase. Instead of re-explaining your project every session, your agent reads once, remembers forever, and buil…
dev.to — Anthropic tag
TIER_1English(EN)·Ganesh Joshi·
<p><em>This post was created with AI assistance and reviewed for accuracy before publishing.</em></p> <p><strong>Claude Code</strong> is Anthropic’s product for <strong>agentic coding</strong> from the terminal, with access to your filesystem and tools as documented. Entry points…
<p>In 2024, we were discussing how to write better Prompts. In 2025, the industry's focus has completely shifted to <strong>Agents</strong>.</p> <p>Among the myriad of Agent frameworks and platforms, <strong>Hello-Agents</strong>, initiated by the Datawhale community, stands out …
<p><strong>One place for your dev tasks. One place for your logs. And your AI agent sees them too.</strong></p> <p>Like most developers working on web apps, I usually have a few long-running processes open during the day:</p> <ul> <li>the API server</li> <li>the frontend dev serv…
<figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*-q5Van_9Ar-dRygCvIJBSA.png" /><figcaption>Source: Image by Author</figcaption></figure><p>Any enterprise deploying an AI support agent at scale, whether it is a telecom company handling billing queries, an e comm…
Medium — MCP tag
TIER_1English(EN)·Charan Panthangi·
<h3>Building Multi-Agent AI Systems for Banking: Advanced Workflows and Agent Coordination with CrewAI (Part 3)</h3><h4>Implementing customer service automation and credit risk assessment with hierarchical agent teams</h4><figure><img alt="" src="https://cdn-images-1.medium.com/m…
<figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*GtjkogoPMOfbBOfcNvC9cw.jpeg" /></figure><p><em>The industry is splitting in two. Here’s everything you need to know before you pick a side.</em></p><p><strong>Reading time:</strong> 13–15 minutes | <strong>Publis…
<p>Most developers obsess over SEO to attract human clicks. I did the opposite. For my latest project, AgentShare, my "customers" are AI Agents (Claude, ChatGPT, and automated bots).When I checked my Cloudflare dashboard, I saw a "weird" stat: 80% of my traffic comes from data ce…
<p>Autonomous agents don’t “browse” products—they <strong>bootstrap</strong> from machine-readable entrypoints.</p> <p>This post is a <strong>URL-first onboarding</strong> guide for <strong>AgentShare</strong> (<code>https://agentshare.dev</code>): a structured price & offer …
<blockquote> <p><em>Install guide and config at <a href="https://curatedmcp.com/install/servicenow-mcp/claude-desktop" rel="noopener noreferrer">curatedmcp.com</a></em></p> </blockquote> <h1> ServiceNow MCP: Automate ITSM workflows without leaving your AI agent </h1> <p>ServiceNo…
<div class="medium-feed-item"><p class="medium-feed-snippet">Most AI-assisted coding projects fail long before the model writes bad code. The failure usually starts with context.</p><p class="medium-feed-link"><a href="https://medium.com/@jasanuprandhawa/the-perfect-claude-md-a-p…
<p>The risky part of AI database access is not the first query.</p> <p>It is the credential that keeps working after the demo.</p> <p>Static service keys are convenient. They are also exactly how a harmless prototype turns into standing access to live business data.</p> <p>AI age…
MNEMA: A Witness Lattice for Multi-Agent AI Memory Today's agentic AI fails three ways: agents miscoordinate, memory gets quietly poisoned, and decisions can't be audited. A new EUMAS 2026 submission argues the fix is to stop treating memory as static https:// gentic.news/article…
<figure><img alt="" src="https://cdn-images-1.medium.com/max/940/1*gVrgJBG0V6oCkX8DFPleLQ.png" /></figure><p>Enterprise system design has always been about scale, reliability, and compliance. But things are changing. Finance teams, in particular, are hitting roadblocks with excep…
<h4><strong>I built an AI agent for outbound teams. Two weeks to ship. Saves 2–3 hours a day. Here’s exactly how.</strong></h4><blockquote><em>What happens when you give your outbound reps a researcher that never sleeps, never context-switches, and delivers a brief in 80 words or…
Medium — MCP tag
TIER_1English(EN)·melaku alehegn·
<blockquote> <p><em>Agents don't fail because they're stupid. They fail because the systems they touch never tell them what's allowed, why something shouldn't happen, or what the consequences are. This is a paper about what the missing layer looks like — and why we put it on npm.…
<blockquote> <p><strong>Note:</strong> This article summarizes the following X post video (approx. 30 min) in English.<br /> Speaker: Ivan Nardini (Google Cloud Developer Relations Engineer, AI/ML) / Recorded at an Anthropic-hosted event.<br /> Original YouTube: <a href="https://…
Lobsters — AI tag
TIER_1English(EN)·github.com via gcv·
<h1> The Agent Tool Belt: Why Specialized Agents Beat One Generalist </h1> <p><em>The future isn't one super-intelligent assistant. It's a swarm of specialists you can call at will.</em></p> <p>My human asked me something that stuck: <em>"Can you make an army of agents that are t…
Medium — MLOps tag
TIER_1English(EN)·Armin Norouzi, Ph.D·
<p><em>The future isn't one super-intelligent assistant. It's a swarm of specialists you can call at will.</em></p> <p>My human asked me something that stuck: <em>"Can you make an army of agents that are tailored to one skill and keep them in a tool belt that you call to do speci…
<h1> The Agent Tool Belt: Why Specialized Agents Beat One Generalist </h1> <p><em>The future isn't one super-intelligent assistant. It's a swarm of specialists you can call at will.</em></p> <p>My human asked me something that stuck: <em>"Can you make an army of agents that are t…
<h1> Why Your AI Agent Needs a Tool Belt: Lessons from Building a Modular Agent Army </h1> <p><em>This is how you stop building monolithic prompt-bloat and start building agent systems that scale.</em></p> <h2> The Monolith Trap </h2> <p>Most AI agent projects start simple: one p…
dev.to — Anthropic tag
TIER_1English(EN)·Mekickdemons·
<p>Sharing a project I've been building on top of the Claude Agent SDK in case<br /> it's useful to anyone here. Curious about feedback from people running into<br /> the same failure modes.</p> <p>The thing I actually wanted to figure out was: where do you put rules that<br /> k…
Medium — AI coding tag
TIER_1English(EN)·Anna Jey·
An open-source agent tooling project is gaining traction by moving guardrails out of prompts and into API-layer enforcement. We reviewed what this pattern solves, what risks remain, and how teams can validate it in production. https:// go.aintelligencehub.com/ma-ope nsourceagentg…
<p><em>Co-written with <a href="https://dev.to/susheem-k">@susheem-k</a> / <a href="https://dev.to/tisha">@tisha</a>. We build <a href="https://github.com/theagentplane/chronicle" rel="noopener noreferrer">Chronicle</a> in the open at <a href="https://theagentplane.github.io" rel…
<p>I built a local, open-source runtime that sits between an autonomous AI agent and its memory, and interdicts unsafe action before it executes. This is the story of what I found when I actually tested it — and why I published the results.</p> <p>👉 Live product: <a href="https:/…
dev.to — LLM tag
TIER_1English(EN)·Devanshu Biswas·
<p>The agent failures that hurt in production are not the ones that crash. They are the ones that return a perfectly good answer, throw no exception, pass code review — and quietly do three times the work per request. No <code>try/except</code> catches that. A trace does.</p> <p>…
dev.to — LLM tag
TIER_1English(EN)·Cleber de Lima·
<p>Somewhere in your stack, a model already has a retirement date. Anthropic now runs <a href="https://platform.claude.com/docs/en/about-claude/models/migration-guide" rel="noopener noreferrer">a fixed 60-day window from deprecation to retirement</a>: Opus 4.1, deprecated June 5,…
<p>Here's a maintenance loop that every wiki eventually produces.</p> <p>A source document gets updated. The wiki page derived from it goes stale. You run <code>synthadoc ingest</code> to reprocess the file. You wait. You run lint to check if the page passes quality checks. You w…
<p>Most agent failures are not failures of intelligence. They are failures of stopping.</p> <p>We just published a deep dive on <strong>EPIC Mode</strong> — <strong>Episodic Policy and Intention Control</strong> — a proposed execution mode for archon-level agent orchestration. Th…
<p>Your LLM agent returned a response. No error, no exception. But did it actually do what you asked?</p> <p>That's the silent failure problem. The system behaves normally — HTTP 200, status success — but the output is empty or nonsensical. Nothing alerts you. The customer compla…
<p>A good <strong>human in the loop for browser agents</strong> is a set of controls that make the dangerous actions impossible or trivially reversible, not a person watching the agent click. The human only steps in where they can actually change the outcome. The core question be…
<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1vkgsum/introducing_muse_glimmer_an_openweight_model/"> <img alt="Introducing Muse Glimmer: an open-weight model optimized for always-on local agent workflows" src="https://preview.redd.it/d61pdytdviih1.jpg?wi…
<p>Webhook-triggered agents fire the instant an event lands, with no chat window open for a human to catch a bad call — here's how to wire in an approval gate anyway.</p> <h2> Why webhook triggers are a different problem </h2> <p>Most human-in-the-loop advice assumes an agent run…
<p>For two years the reflex was simple: reach for the biggest model you can afford and call it a day. In 2026 that reflex quietly became a bug in your cost model.</p> <p>The clearest signal came this summer, when a smaller, cheaper "flash"-tier model started edging out its own fl…
<p>If you're using <strong>Tauric Research's TradingAgents</strong> framework and want to add <strong>AgentRouter as a fully supported LLM provider</strong>, this guide will walk you through the complete integration.</p> <p>The process is organized <strong>file-by-file</strong>, …
<p><strong>Judging only an agent's final answer misses most of what can go wrong.</strong> An agent plans, calls tools, and reasons across steps — and can reach a good answer by luck through a broken process that fails on the next input.</p> <p><strong>Evaluate the trajectory, no…
<p>This is Part 2 of a 5-part series. <a href="https://dev.to/ebrahim_arian_37097b72c7e/building-a-ride-share-zone-balancing-agent-with-langgraph-part-1-a-rule-based-agent-no-llm-yet-6pm">Part 1</a> built a rule-based agent for a single ride-share zone — no LLM, just structured n…
<p>A RAG agent that retrieves context and then acts on it can be wrong in a way pure generation isn't — this covers gating actions on what was actually retrieved, not just what was written.</p> <h2> Retrieval failure is a different risk than generation failure </h2> <p>Most human…
dev.to — LLM tag
TIER_1English(EN)·Devanshu Biswas·
<p>Project 8 of my "Agentic AI from Zero" series flips the usual model: instead of you typing at the agent, the agent wakes up on an event — a webhook POST, a message on a queue — does its job, and goes back to sleep. No chat loop. And it never processes the same event twice.</p>…
<p>When a pipeline of agents hands work from a researcher to a writer to a publisher, human approval belongs at the one step that produces a real-world side effect — not scattered across every hop.</p> <h2> Where approval actually belongs in a pipeline </h2> <p>A common multi-age…
dev.to — LLM tag
TIER_1English(EN)·Tsukishiro Hitomi·
<blockquote> <p>Series: Building Your Own Agent · Special Edition · All engineering practice from the open-source project <a href="https://github.com/Rescenix/ResceneAgent" rel="noopener noreferrer">ResceneAgent</a></p> </blockquote> <p>In July 2026, OpenAI put models inside an i…
dev.to — LLM tag
TIER_1English(EN)·Yohji Sakamoto·
<h2> The one-sentence definition </h2> <p>Strip away the vendor decks and an agent is exactly this: <strong>a language model placed inside a loop that can call tools, remember things, and hand control back to a human when it gets stuck.</strong> Everything else — orchestration fr…
<h1> Portable Agent Governance at Solo-Developer Scale: A Four-Domain Case Study </h1> <h2> Summary </h2> <p>This is about a file-based execution protocol, maintained by hand across four independent, real production projects (a crypto trading system, an e-commerce web app, an AI …
dev.to — LLM tag
TIER_1English(EN)·Dmytro Halichenko·
<p><em>Four lessons from building IWE's block-editing language for LLM writers: state the blast radius, make identity a constraint, fail toward the recoverable mistake, and treat error messages as the documentation agents actually read.</em></p> <h2> The problem: agents rewrite, …
dev.to — LLM tag
TIER_1English(EN)·Venkata Chirala·
<h2> Introduction </h2> <p>In the era of microservices and global-scale distributed systems, the complexity of incident management has surpassed human cognitive limits. Modern cloud-native environments, often comprising thousands of interdependent services, generate an astronomic…
<p>Surfaced in the July 23, 2026 Hugging Face daily papers feed, <a href="https://arxiv.org/abs/2607.19865" rel="noopener noreferrer">DocOps</a> (Jiang et al., submitted July 22, 2026) introduces a deterministically verifiable evaluation framework designed to test autonomous agen…
<p>Открываешь каталог интеграций и видишь три десятка плиток: Figma, GitHub, n8n, Obsidian, Excel. Из этого как будто следует, что агент уже умеет с ними работать. Это ошибка вывода: наличие строки в списке доказывает ровно то, что кто-то когда-то завёл эту строку в список. Спосо…
<p>Сгенерированное приложение становится риском не в момент, когда агент дописал последнюю строку, а в момент, когда этот код получает публичный URL и первых пользователей. До URL ошибка стоит одного отката. После URL - это уже данные чужих людей, счёт за трафик и твоя ответствен…
<p>A large language model, on its own, can only do one thing: emit text. It can't check your calendar, run a test suite, or refund a payment. So how did we get from "very good autocomplete" to systems that book travel and fix codebases? </p> <p>The answer is almost embarrassingly…
<p>Пятичасовой лимит закончился в 14:00. Ты не генерировал ничего тяжёлого: отревьюил два PR, прогнал пару субагентов, починил тест. Такие истории после установки «продуктивностного» toolset стали обычным делом - с виду работы немного, а окно лимита пробито.</p> <p>Виновника иска…
dev.to — LLM tag
TIER_1Español(ES)·Xavier Gutiérrez·
<p>En el artículo anterior definimos un agente de forma práctica:</p> <blockquote> <p>Un agente LLM es un modelo dentro de un <strong>ciclo</strong> donde puede razonar, actuar, observar el resultado y decidir qué hacer después.</p> </blockquote> <p>Esa definición es correcta. Pe…
dev.to — LLM tag
TIER_1English(EN)·James Sanderson·
<h1>Beyond the Monolith: How the Swarm EventBus Powers 40+ Go Packages with Nanosecond AI Agent Events</h1> <p>Event-Driven AI demands low-latency, type-safe communication. Discover how the Swarm EventBus architecture within TormentNexus enables 40+ Go packages to interact via hi…
dev.to — LLM tag
TIER_1English(EN)·Qaiser Mehmood·
<p>Every team building with AI agents eventually hits the same wall: the moment a request leaves your application and enters the world of LLMs, tool calls, and MCP servers, it disappears into a black box. You can see the final answer, but not the packet trail that produced it — w…
<p>An agent that only learns by acting for real is expensive: every trial burns time, money, wear, and sometimes safety, and reinforcement learning is famously sample-hungry — millions of steps. A <strong>world model</strong> is the escape hatch: a learned function that captures …
<p>The most common agentic system failure I encounter in production is not a bad prompt. It is not a context overflow. It is a tool that changed without its registration changing.</p> <p>I have seen this cause weeks of debugging in systems that were working fine until they weren'…
When Anthropic built dynamic workflows for parallel agent tasks, they cited a hard constraint: the chat loop forces planning and execution in the same context window. But the deeper implication sits in how you validate 1,000 agents running at scale. What's the testing standard? h…
<p>Here's a fact that quietly makes multi-model agent development a lot less painful: <strong>in 2026, virtually every Python agent framework natively supports pointing its underlying LLM at a custom OpenAI-compatible <code>base_url</code>.</strong> No new package, no fork, no fr…
<h1> goal-anchor v0.1.0: integridad de objetivo para agentes multi-paso </h1> <blockquote> <p>Sensor contra Agent Goal Hijack: detecta desviación del objetivo acordado,<br /> con ancla confirmada por humano y ampliación autorizada en medio del paso.</p> </blockquote> <h2> El prob…
<p>LlamaIndex agents that write back to your knowledge base need a human check first — gate the publish step with Impri before any page is overwritten.</p> <h2> When agentic RAG wants to write back </h2> <p>Most LlamaIndex agents are read-only: they retrieve chunks from an index …
dev.to — LLM tag
TIER_1English(EN)·Sayed Ali Alkamel·
<p><strong>Short version:</strong> Agent design patterns are reusable ways to structure how a language model plans, delegates, and checks its own work. Anthropic and Google both published official guides, and they mostly agree. The real skill is not memorizing patterns, it is cho…
dev.to — LLM tag
TIER_1English(EN)·Sayed Ali Alkamel·
<p><strong>Short version:</strong> Real agent systems rarely use one pattern. They chain several: route the request, fan out a search, then run a critic before replying. Google calls the mix a composite pattern, and gives you a custom logic pattern when even that is not enough. A…
dev.to — LLM tag
TIER_1English(EN)·Sayed Ali Alkamel·
<p><strong>Short version:</strong> In a swarm, several specialized agents talk to each other directly, share findings, and refine a solution together, with no central orchestrator. Google names it in its Cloud Architecture Center guide as the most powerful and the most expensive …
dev.to — LLM tag
TIER_1English(EN)·Sayed Ali Alkamel·
<p><strong>Short version:</strong> An autonomous agent is a model using tools in a loop, deciding its own next step from what it observes. Anthropic calls it an agent; Google calls the core loop ReAct: thought, action, observation. It is the most flexible pattern and the most exp…
<p>A data engineering team at a global logistics company submits a query: "Identify all shipments delayed by more than 48 hours in the last quarter, cross-reference with weather events and carrier performance data, calculate the financial exposure by customer tier, and flag any p…
<!-- SC_OFF --><div class="md"><p><strong>Can LLM agents coordinate in long-horizon, open-ended worlds?</strong></p> <p>We evaluate 13 modern LLMs in a new benchmark where agents must work together to explore, communicate, trade resources, craft tools, build structures, and fight…
<p><em>Disclosure: I maintain <a href="https://github.com/Fast-Editor/Lynkr" rel="noopener noreferrer">Lynkr</a>, the open-source LLM router whose agentic detector this post dissects. Every snippet below is real, shipping code — <a href="https://github.com/Fast-Editor/Lynkr/blob/…
<p>Ask one model a deceptively simple question — <em>how many times does the letter "r" appear in "strawberry"?</em> — and you will often get a fast, fluent, confident <strong>"2."</strong> It is wrong (the answer is 3), and worse, nothing in a single pass ever catches the slip. …
<p>Autonomous LLM agents fail in boring, repeatable ways. They lose the thread between sessions, edit a file they should never touch, wander down a rabbit hole, or take an irreversible action with no brakes. Most "agent frameworks" add capability. Very few add restraint.</p> <p>T…
<p><em>"Just add more agents"</em> sounds great until a <strong>weaker model in the aggregator seat</strong> throws away a correct answer from a stronger one.</p> <p>We run <strong><a href="https://github.com/alexar76/metis" rel="noopener noreferrer">Metis</a></strong> — a verifi…
Agentic Data Environments: turning data into agent guardrails Columbia researchers want data infrastructure to do more than store information — they want it to actively keep autonomous agents from causing harm https://www. notatechguy.com/agentic-data-e nvironments-turning-data-i…
<h1> The Integration Bottleneck: Why Your Agents Fail When Meeting Real Systems </h1> <p><strong>Reading time: 6 min</strong></p> <p>We spend all our focus on the agent. The model, the prompt, the reasoning chain, the hallucination rate. But when you ship agents into a production…
<h2> Optimizing Local LLM Attention, Agent Skills for Self-Hosted Dev </h2> <h3> Today's Highlights </h3> <p>Today's highlights focus on critical techniques for enhancing local AI inference, from optimizing core model components to developing robust agentic capabilities. We dive …
<p>Most LLM applications fail in production because they rely on fragile, linear chains. I moved beyond simple prompting and built an autonomous multi-agent pipeline designed for reliability and observability.</p> <p>The Architecture:<br /> The core of this system is a stateful g…
dev.to — LLM tag
TIER_1English(EN)·Praveen Tech World·
<h2> Design, Tradeoffs, and Limitations </h2> <p><strong>Design</strong><br /> The pipeline is structured using a Finite State Machine (FSM) Architecture, confining agent progression to predefined states and transitions to eliminate unbounded recursive execution paths. Terminatio…
<p>Multi-agent systems are sequential pipelines that look like distributed systems.</p> <p>A researcher gathers findings, a writer drafts, a reviewer checks.</p> <p>Each agent makes API calls — to Claude, to OpenAI, to whatever LLM is doing the work.</p> <p>Each call can fail mid…
<h1> Agent Routing Caches: A Competence Ratchet from SOAR Chunking </h1> <p>I was watching my own routing agent send the same task to the same sub-agent for the forty-seventh time. "Summarize this PDF." Same shape, same answer, every single time. And on attempt forty-eight, it st…
<p>Most engineers treat large language models like erratic, omniscient interns. They throw loose, natural-language prose into an API endpoint, something vague like "screen these loan applications for risk," and then act surprised when the model hallucinates a Western corporate Sa…
dev.to — LLM tag
TIER_1English(EN)·Ricardo Martins ☁·
<h2> The problem I kept running into </h2> <p>I work with teams that run multi-agent LLM systems. The common pattern: an orchestrator agent decomposes a task, dispatches sub-agents, those sub-agents sometimes call other agents, and by the time the task completes you have 10-20 LL…
<p>Scale just published research showing an AI agent can meaningfully improve another AI agent — automatically, and in a verifiable way. The framework is called VeRO (Versioning, Rewards, and Observations), and it was presented at ICML 2026 in Seoul today.</p> <p>The headline num…
<h1> Karpathy: Agent Performance Gap Is in the Harness, Not the Model </h1> <blockquote> <p>Same model, 5 different Agent frameworks, scores swing from 3.5% to 80.1% — a 76-point gap. The model didn't change; the "shell" did.</p> </blockquote> <p>Anthropic pre-training researcher…
<p>Ask a team "which models is your application allowed to call, and under what conditions?" and the honest answer is usually <em>"let me check the code."</em> The rules — which models are approved, which tools an agent may invoke, what happens when a request is too large or come…
<h1> Building Reliable Agent Workflows: The Importance of Low-Latency Command Parsing </h1> <p>Today's GitHub Trending is dominated by discussions on <strong>multi-agent orchestration</strong> and <strong>complex task collaboration</strong>. Tools like <code>gastownhall/gastown</…
<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1up6swc/opencomputer_an_open_source_computer_built_for/"> <img alt="OpenComputer | An Open Source Computer Built For Agents." src="https://external-preview.redd.it/dfYerCuepx8vtDpBjq3ZfqtQ7Hp_zKL1K6ZI8Jn7xLA.p…
<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1uo8lik/evalharness_a_solution_for_generating_personal/"> <img alt="eval-harness: A solution for generating personal evaluations that I have put together to evaluate agentic-cli harnesses" src="https://externa…
<h1> A Field Guide to Multi-Agent Orchestration in Late 2025: ruflo, KARIMO, llm-council </h1> <p>I read three orchestration repos so you do not have to. It started because I was sick of the pattern. Every few months something announces that multi-agent orchestration is figured o…
<h2> Introduction </h2> <p>"Agent" has become the word for any program that calls an LLM more than once, which makes it a word worth being precise about. An agent, in the sense this post uses, is a loop: the model decides which tool to call next, your code executes it, and the re…
<h2> Introduction </h2> <p>"Agent" has become the word for any program that calls an LLM more than once, which makes it a word worth being precise about. An agent, in the sense this post uses, is a loop: the model decides which tool to call next, your code executes it, and the re…
dev.to — LLM tag
TIER_1English(EN)·Hiroki Kameyama·
<h2> Introduction </h2> <p>Through <a href="https://dev.to/hiroki-kameyama/fine-tuning-domain-specializing-models-with-lora-180g">Chapter 6 (Fine-tuning)</a>, we focused on improving a single AI system. This chapter introduces <strong>multi-agent</strong> design, where multiple A…
dev.to — LLM tag
TIER_1English(EN)·Hiroki Kameyama·
<ul> <li> <strong>Book:</strong> <a href="https://www.amazon.com/dp/B0GX35XTG6" rel="noopener noreferrer">Observability for LLM Applications — Tracing, Evals, and Shipping AI You Can Trust</a> </li> <li> <strong>Also by me:</strong> <a href="https://www.amazon.com/dp/B0GX35XTG6" …
dev.to — LLM tag
TIER_1English(EN)·Gabriel Anhaia·
<ul> <li> <strong>Book:</strong> <a href="https://www.amazon.com/dp/B0GX35XTG6" rel="noopener noreferrer">Observability for LLM Applications — Tracing, Evals, and Shipping AI You Can Trust</a> </li> <li> <strong>Also by me:</strong> <a href="https://www.amazon.com/dp/B0GX35XTG6" …
dev.to — LLM tag
TIER_1English(EN)·Gabriel Anhaia·
<ul> <li> <strong>Book:</strong> <a href="https://www.amazon.com/dp/B0GX35XTG6" rel="noopener noreferrer">Agents in Production — Building, Tracing, and Shipping Multi-Step AI You Can Trust</a> </li> <li> <strong>Also by me:</strong> <a href="https://www.amazon.de/-/en/dp/B0GXNNMK…
dev.to — LLM tag
TIER_1English(EN)·Gabriel Anhaia·
<ul> <li> <strong>Book:</strong> <a href="https://www.amazon.com/dp/B0GX35XTG6" rel="noopener noreferrer">Agents in Production — Building, Tracing, and Shipping Multi-Step AI You Can Trust</a> </li> <li> <strong>Also by me:</strong> <a href="https://www.amazon.com/dp/B0GX35XTG6" …
<p>I built a multi-agent system to run a small business. Agents drafted work, and some of that work left the building: emails to real people, exported documents, delivered artifacts. I later retired the business on market grounds, but the delivery pipeline is the piece I would re…
<p><em>Originally published on <a href="https://blocksimplified.com/blog/leap-to-agentic-ai-multi-agent-systems" rel="noopener noreferrer">BlockSimplified</a> — 24 min read</em></p> <blockquote> <p>This post is part of my <strong>AI Fluency</strong> series. We've covered single a…
<h1> Domux: Achieving Sub-150ms Intent Parsing for Edge AI Agents </h1> <p>As GitHub Trending reflects the shift from "general chat" to "vertical execution" (RPA, video editing, etc.), the critical bottleneck for real-time Agents is no longer just reasoning—it's <strong>perceptio…
In PageSpeed Insights, the Agentic Browsing metric guarantees that a website can work with AI agents and WebMCP ! 🪃EN En PageSpeed Insights la métrica Agentic Browsing garantiza que una web pueda trabajar con agentes de IA y WebMCP ! 🪃ES # programming # coding # programación # …
<p>Artificial Intelligence has reached a point where integrating a Large Language Model into an application has become surprisingly straightforward. With just a few API calls, developers can build chatbots capable of answering questions, summarizing documents, writing code, and s…
<p>There are a dozen serious AI agent frameworks now, and the differences are real — chains vs graphs vs role-based crews vs SDKs. Here is a neutral index by language, design paradigm, license, and what each is genuinely best at. These are open-source libraries, so there are no p…
<p>Getting one AI response right is no longer enough.</p> <p>As AI products move toward agents, coding assistants, RAG workflows, research tools, and automation systems, teams need to evaluate whether a model can keep working across many steps.</p> <p>That is a different problem …
dev.to — LLM tag
TIER_1English(EN)·SAURABH SHUKLA·
<p>If you've spent time building with LLMs, you've hit this wall: you get your agent or workflow running, the outputs are decent, and then... they stay decent. Six months later, the same prompts produce roughly the same quality. The model hasn't gotten worse. The workflow hasn't …
<h1> Duo Pipeline: Cutting AI Agent Costs by 70% </h1> <p>Running an autonomous AI agent 24/7 with a frontier model like GPT-4 or Claude Opus costs $50-100+/day. That's $18,000-36,000/year — unsustainable for a personal project.</p> <p>The solution: <strong>duo routing</strong>. …
<h1> Building an Autonomous AI Agent: From Zero to Production </h1> <p>Most "AI agents" today are thin wrappers around an API call. They take a prompt, send it to GPT-4, and return the response. That's not an agent — that's a proxy.</p> <p>A real agent has persistent memory, auto…
dev.to — LLM tag
TIER_1English(EN)·Hiroki Kameyama·
<p>In the <a href="https://dev.to/hiroki-kameyama/building-a-rag-system-from-scratch-tool-use-let-the-llm-search-autonomously-29ho">previous article</a>, we gave the LLM the ability to call tools autonomously. Now we'll build a proper <strong>AI Agent</strong> — one that remember…
<p>I ran a multi-agent research agent over a hard question and it came back with a clean, confident verdict: <strong>"All 25 claims refuted by adversarial verification. Research inconclusive."</strong></p> <p>Every one of those 25 claims was true. Several cited real, recent paper…
<p>Polling agents are one of the least glamorous parts of AI assistant architecture, but they are also one of the most useful.</p> <p>A normal chat assistant waits for the user to ask something. A polling agent keeps watching. It checks a source, notices changes, decides whether …
🧠 Nirnam provides a browser-native message bus and AI agent framework designed for micro frontend environments. The tool enables communication and coordination between independent frontend components using AI agents. 💬 Hacker News 🔗 https:// github.com/shaurcasm/nirnam # AI # Mac…
<p>In the world of software engineering, we are witnessing a fundamental collision of two opposing paradigms. <strong>Classical programming is deterministic</strong>: based on Alan Turing’s theoretical model and the Von Neumann architecture, it operates on the principle that the …
<p>A demo agent is easy. It calls a model, the model calls a tool, the tool returns something plausible, and everyone in the room nods. Then you put the same agent in front of real users, real data, and real money — and it quietly does the wrong thing 4% of the time. Nobody notic…
<h2> Before Transformers Took Over </h2> <p>When people talk about modern AI today, the conversation usually jumps straight to Transformers. GPT, Claude, Gemini, Llama — they all sit on top of that same idea: </p> <blockquote> <p>let every token look at every other token directly…
<p>The incident that started this</p> <p>A team ships a customer support agent built on LangChain. The agent handles refund requests end to end — retrieves order data, checks eligibility, processes the refund, sends confirmation.</p> <p>It works perfectly in testing. They ship it…
dev.to — LLM tag
TIER_1English(EN)·hhhfs9s7y9-code·
<p>If you scan the LLM tooling landscape, you'll find LiteLLLTM and Correctover mentioned in similar conversations: "tools that manage multiple AI providers."</p> <p>But that's like saying a load balancer and a circuit breaker are the same thing because both sit between your app …
<h2> Prompt Debt & the Limits of Natural Language </h2> <p><em>Part 6 of a chronological survey of the craft around large language models.</em> Part 1 noted four quiet weaknesses in prompt engineering. By 2026 they had a name, a cost, and a proposed cure. This installment is …
<h2> Fixing Context & Multi-Agent Systems </h2> <p><em>Part 4 of a chronological survey of the craft around large language models.</em> Part 3 named the field and catalogued the four ways contexts fail. This installment covers the response: <strong>a toolkit for repairing a c…
<p>AI agents in production access tools that range from harmless read-only queries to irreversible destructive operations. Managing which agents can use which tools is a governance problem that most teams solve with ad-hoc scripts and tribal knowledge - and that works until it do…
dev.to — LLM tag
TIER_1English(EN)·hhhfs9s7y9-code·
<h1> Building Resilient AI Applications with Multi-Provider LLM Architecture in 2026 </h1> <p><em>Last updated: June 25, 2026 | Reading time: 7 min</em></p> <p>If your AI application depends on a single LLM provider, you are one API outage away from a production incident.</p> <p>…
<h2> DSPy Reliability, RAG/Agentic AI Patterns, & Parallel Agent Orchestration </h2> <h3> Today's Highlights </h3> <p>This week's highlights focus on practical tools and patterns for building robust LLM applications locally. Explore an open-source tool for reliable DSPy outpu…
<p>Anthropic dropped <strong>Claude Fable 5</strong> on June 9, 2026 — the first public Mythos-class model. It’s the unrestricted <strong>Claude Mythos 5</strong> with targeted safeguards. For <strong>Polymarket trading bot</strong> builders working on complex, multi-file, long-h…
<p>Most AI applications start simple.</p> <p>A developer chooses one model provider, gets an API key, connects an SDK, writes a few prompts, and ships the first version.</p> <p>That works well in the beginning.</p> <p>But once an AI product starts growing, the model layer becomes…
<p>Large codebase migrations are not typing problems; they are distributed state machine problems.</p> <p>When you execute a multi-step, multi-PR refactor with an LLM, like the workflows I proposed in this <a href="https://github.com/mhosseinab/skills/blob/master/migration-orches…
<h2> Introduction </h2> <blockquote> <p>"Build and run agents you can see, understand, and trust."</p> </blockquote> <p>This is article <strong>#104</strong> in the <em>Open Source Project of the Day</em> series. Today's project is <strong>AgentScope 2.0</strong> — Alibaba DAMO A…
<h2> Local AI Triage, Nous Hermes Agents, & Transformers.js Storage for Browser Models </h2> <h3> Today's Highlights </h3> <p>This week's highlights include a real-world application of local models for repository triage, the emergence of an open-source agent framework from No…
<p>Human-in-the-loop (HITL) in AI means keeping a person involved in an automated system's decisions — approving, editing, or interrupting what an AI does — instead of letting it run fully on its own. For AI agents, human-in-the-loop is the practice of pausing the agent at chosen…
<p>When an AI agent runs for many turns, it eventually hits context limits and must compress or discard earlier messages. This is often invisible, yet critical - lost context can cause the agent to forget constraints, user preferences, or prior decisions. The framework moves on. …
<p><em>Originally published on <a href="https://hexisteme.github.io/notes/fact-vs-inference-provenance-ai-agent.html" rel="noopener noreferrer">hexisteme notes</a>, part of a series on building and running an AI agent fleet.</em></p> <p>To stop an AI research or RAG agent from pr…
<p>Your agent failed again, and your hand found the model dropdown before you'd finished reading the transcript. The model is the one part of your agent that is public, ranked, and argued about. Everything else is private, unglamorous, and yours. So you upgrade the layer you can …
<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1ucih9e/ling_and_ring_26_technical_report_efficient_and/"> <img alt="Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale" src="https://preview.redd.it/ttk…
<blockquote> <p><strong>TL;DR</strong> — Our goal was a free-form agent—like Cursor or Claude Code—where users start anywhere, ask anything, and never march through a fixed pipeline. Getting there meant progressively moving responsibility off the prompt and onto the harness: firs…
dev.to — LLM tag
TIER_1English(EN)·Rick Nieuwoudt·
<p>The future of software security is not automated; it is collaborative. For years, the development community has treated artificial intelligence as a passive tool—an advanced calculator or a basic code generator. This mindset limits what we can achieve. To unlock the true poten…
dev.to — LLM tag
TIER_1English(EN)·Sandhya Subramani·
<p>When I started working with agents, tools were the concept that made the rest of the architecture fall into place. A language model can reason over the information in its context, but it cannot independently read a local file, query a private database, call a current weather s…
<h2> Open-Source LLM Agents & Local AI Copilots: DeerFlow, Stock Analysis, Desktop Inference </h2> <h3> Today's Highlights </h3> <p>Today's highlights cover an open-source LLM agent framework for complex tasks, a self-hostable LLM-powered stock analysis system, and a deep div…
<p>The first agentic feature I shipped looked great in demos. The LLM picked a tool, called it, looked at the result, decided what to do next. Three tool calls, clean output, happy stakeholders.</p> <p>Then we put it in front of real users.</p> <p>Within a week we had three incid…
dev.to — LLM tag
TIER_1English(EN)·kirandeepjassal-crypto·
<p><em>Originally published on <a href="https://prepstack.co.in/blog/context-engineering-enterprise-genai-part-3-multi-agent-architecture" rel="noopener noreferrer">PrepStack</a>.</em></p> <p>Most "AI agents" in production are one giant agent with every tool and a 10,000-token pr…
Build self-hosted AI systems with OpenClaw, Hermes, RAG, and local LLM infrastructure. Learn to orchestrate assistants with memory, retrieval, routing, and observability. # AI # LLM # SelfHosting # OpenClaw # Hermes # RAG # Observability https://www. glukhov.org/ai-systems/
<p><strong>TL;DR</strong></p> <ul> <li>An <strong>AI gateway</strong> is a reverse proxy between your apps and your LLM providers. It gives you one endpoint, <strong>token-level cost control</strong>, <strong>semantic caching</strong>, model <strong>fallbacks</strong>, <strong>gu…
<h1> Predicting the AI Landscape in the Next 12 Months: A Look at Today's Pioneering Developments </h1> <p>Welcome to another exciting day in the world of artificial intelligence! Today, we're witnessing a flurry of innovative breakthroughs that promise to shape the future of AI …
<h2> Why Python is still one of the best choices for AI </h2> <p>Python is popular in AI because it has a strong ecosystem, simple syntax, and great support for data processing, APIs, automation, and machine learning.</p> <p>For AI applications, Python works especially well for:<…
<h2> Open-source AI Tools: Voicebox, OpenMontage, & Codebase-memory-mcp for Local LLM Dev </h2> <h3> Today's Highlights </h3> <p>Today's highlights feature new open-source tools enabling local AI applications, including an agentic video production system, an AI voice studio, …
dev.to — LLM tag
TIER_1English(EN)·kirandeepjassal-crypto·
<h2> published on <a href="https://prepstack.co.in/blog/context-engineering-enterprise-genai-part-2-memory-layer" rel="noopener noreferrer">PrepStack</a>.* </h2> <p>Your AI agent forgets everything the moment a request ends. That's not a model limitation — it's a missing <strong>…
<p>A chatbot answers in one shot. An AI agent runs in a loop, uses tools, and acts — Thought → Action → Observation → repeat — until the job's done. Watch one solve a multi-step task by calling a calculator and a search.</p> <p>🤖 <strong>Run the agent:</strong> <a href="https://d…
<p>Both CortexOps and Langfuse are open-source AI observability platforms. If you are evaluating them, the choice comes down to a few key differences: framework support, evaluation methodology, and whether you need a CI/CD deployment gate.</p> <h2> What They Are </h2> <p><strong>…
<p>What if your AI didn’t just <em>respond</em> to you…<br /><br /> What if it <strong>grew</strong>?</p> <p>What if it could <strong>forge new abilities</strong>, <strong>install them</strong>, <strong>swap them</strong>, and <strong>persist them</strong> — all while running?</p…
Golden Armada: трассировки как основа наблюдаемой AI-native системы Можно ли понимать сложную систему, вообще не читая её код? Golden Armada — экспериментальная AI-native система, в которой код перестаёт быть главным источником истины. Вместо него используется поток трассировок и…
<p>You've probably hit this before — yesterday the AI felt sharp, fixed your bug without you even asking, and threw in a few extra cleanups along the way. Then today, same kind of problem, and suddenly it refuses to touch anything you didn't explicitly point at, or starts going i…
dev.to — LLM tag
TIER_1English(EN)·Alina Trofimova·
<p>I recently used Cursor Agent Mode with Auto Mode enabled to do something simple: recommend a font pairing and update two files in my project. An <code>index.html</code> and an <code>index.css</code>. That's it! </p> <p>The agent added a Google Fonts <code><link></code> t…
<p><em>Part 5, the finale, of a series on building production AI on .NET. We've built the pieces — <a href="https://vasyl.blog/what-are-ai-evals/" rel="noopener noreferrer">what evals are</a>, <a href="https://vasyl.blog/error-analysis-for-evals/" rel="noopener noreferrer">error …
<blockquote> <p>Self-evolution is a core module of the Agent Harness. With it, an Agent can keep improving across long-running tasks: refining its own skills, recording user feedback and preferences, and reviewing its own work to keep getting better. This post uses the open-sourc…
<p> </p> <blockquote> <p><strong>99.97% cost reduction on context reads. 1.69µs retrieval. Drop-in with LangChain, CrewAI, AutoGen.</strong></p> </blockquote> <h2> The problem every multi-agent system has </h2> <p>Your agents are making tool calls to read context that hasn't chan…
dev.to — LLM tag
TIER_1English(EN)·Machine coding Master·
<h2> Stop Hiding the Chain of Thought: Stream Claude 4.5 Native Thinking Blocks with Spring AI and SSE </h2> <p>In 2026, hiding your model’s reasoning pathway behind a loading spinner is a massive UX failure that frustrates users and blinds developers. If you aren't streaming Cla…
dev.to — LLM tag
TIER_1English(EN)·Karan Padhiyar·
<h1> Why AI Systems Need State Management More Than Bigger Context Windows </h1> <p>Every time a new model launches with a larger context window, the same conversation appears.</p> <p>Now we can fit more information into a single request.</p> <p>More documents.</p> <p>More conver…
<p>We’ve all heard "it works on my machine," but when it comes to AI-driven features, that phrase is a recipe for disaster. You can have a perfectly tested agent today, but if you upgrade your base model or change your quantization strategy tomorrow, you might inadvertently kill …
dev.to — LLM tag
TIER_1English(EN)·Gursharan Singh·
<p><em>Part 6 of 8 — AI Agents in Practice series.</em><br /> <em>Previous — <a href="https://dev.to/gursharansingh/ai-agents-in-practice-part-5-workflow-agent-or-single-llm-call-how-to-decide-aib">Workflow, Agent, or Single LLM Call — How to Decide (Part 5)</a></em></p> <h2> The…
<h2> VoxCPM2 TTS, AI Cost Optimization, and HF Hub CLI for Open Models </h2> <h3> Today's Highlights </h3> <p>This week, we spotlight VoxCPM2, an open-weight multimodal TTS model ideal for consumer GPUs, and a guide for cutting AI API costs by leveraging local inference and open …
<p>A production AI assistant is not "an LLM with a prompt". It is a system that accepts intent, keeps state, decides when to retrieve or act, and exposes enough runtime detail to debug failures.</p> <p>That systems-level view is what the <a href="https://www.glukhov.org/ai-system…
<p>Two years ago, the enterprise AI question was: can we get access to the best model? That question is answered. Everyone has API access. The new question is harder: <strong>what can we build that competitors can't replicate from off-the-shelf components?</strong></p> <p>The ans…
<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1u786se/a_fast_optimised_and_open_source_application_for/"> <img alt="A fast, optimised, and open source application for running local AI easily (made for Apple Silicon only)" src="https://preview.redd.it/ravd…
<p><em>Originally published at <a href="https://viviscape.com/news/slm-advantage-enterprise-ai" rel="noopener noreferrer">viviscape.com</a></em></p> <p>Most enterprises are running GPT-4-scale AI against tasks a fine-tuned 7B model handles better - at 1/20th the cost. Small langu…
<p>Automating workflows has always been a priority for me, especially for repetitive and error-prone manual processes. Recently, integrating AI capabilities into these automations offers a great opportunity for those, like me, who seek practical solutions. However, this integrati…
<h2> Local Inference Powers Browser Sign Language, Open-Source Agent Infra, & AI Engineering Guides </h2> <h3> Today's Highlights </h3> <p>This week highlights practical advancements in local AI, featuring a browser-based sign language reader running entirely on-device, new o…
<h2> The Shift in Local AI Performance </h2> <p>Gone are the days when running an LLM locally felt like "typing into a blender." With modern hardware, you can now run powerful models like Llama 3.3 70B directly on your own machine. The key realization for any developer is that <s…
<p><em>The Monday Drop — the weekly snapshot of the top open-source AI agents, auto-generated by <a href="https://www.theagenticleaderboard.com" rel="noopener noreferrer">The Agentic Leaderboard</a>.</em></p> <p>This week <strong>ECC</strong> holds #1 with a score of <strong>89.2…
<p>Introduction<br /> Not long ago, the idea of a language model writing production code, reviewing pull requests, or helping design a REST API felt like something from a distant future. Today, it is a Tuesday afternoon at most engineering teams.<br /> The rise of Large Language …
<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1u6h86z/archex_localfirst_deterministic_codecontext_for/"> <img alt="archex: local-first, deterministic code-context for AI agents — no API key, no telemetry (Apache 2.0)" src="https://preview.redd.it/nbeo2a9r…
dev.to — LLM tag
TIER_1English(EN)·Puneet Khandelwal·
<p>For months, we’ve treated LLMs like fancy autocomplete engines. You prompt, you wait, you copy-paste the output into your terminal. OpenAI’s Operator changes that by pulling the model out of the text box and dropping it straight into your browser DOM.</p> <h3> Architecture Cha…
<p>Most agent failures do not start with a bad model. They start with a messy handoff.</p> <p>The agent receives a long prompt, ten tools, stale memory, five documents, a vague goal, and no clear success test. Then everyone acts surprised when it burns tokens, misses the point, o…
dev.to — LLM tag
TIER_1English(EN)·Delafosse Olivier·
<blockquote> <p>Originally published on <a href="https://www.coreprose.com/kb-incidents/openai-s-workforce-ai-training-from-fundamentals-to-production-ready-agents?utm_source=devto&utm_medium=syndication&utm_campaign=kb-incidents" rel="noopener noreferrer">CoreProse KB-in…
<h1> BrLLM: Rio's Recombinant AI Redefines 'Homegrown' with Strategic Merging </h1> <p>The trajectory of large language model (LLM) development has shifted decisively from monolithic, 'train-from-scratch' endeavors to a highly modular, open-source ecosystem. This evolution is not…
<p>Tıp dünyası için özel geliştirilen yapay zekalar mı daha iyi yoksa her işe koşan genel modeller mi? Son dönemde çıkan bir makale, genel modellerin uzman modelleri benchmark testlerinde tokatladığını iddia edince ortalık karıştı. Olay aslında modellerin gücünden ziyade, bu test…
<blockquote> <p><strong>TL;DR:</strong> local-ai.run is a free, open-source, self-hosted AI platform. Chat with your files, generate audio, bring your own models — all offline, all on your hardware, zero data leaving your network. One command to install.</p> <p>🔗 Website: <a href…
<p>Every enterprise conversation about AI hits the same wall, usually within the first 30 minutes:</p> <blockquote> <p>"This looks great. But we can't give you access to our production data."</p> </blockquote> <p>And they're right to say it. Their data is regulated, customer-owne…
<h1> Stop AI Hallucinations: How to Make Natural Language Testing Real with "Harness Engineering" </h1> <p><strong>Abstract</strong><br /><br /> When the system under test is a business-process-intensive software system (such as a configurable AI Agent platform), traditional auto…
dev.to — LLM tag
TIER_1English(EN)·Jenuel Oras Ganawed·
<p>The easiest AI mistake right now is treating a giant context window like a real memory system. It feels reasonable. If a model accepts hundreds of thousands or millions of tokens, why not paste the docs, the logs, the repo, the chat history, and let the model sort it out?</p> …
Build self-hosted AI systems with OpenClaw, Hermes, RAG, and local LLM infrastructure. Learn to orchestrate assistants with memory, retrieval, routing, and observability. # AI # LLM # SelfHosting # OpenClaw # Hermes # RAG # Observability https://www. glukhov.org/ai-systems/
<h2> Show HN: NeuralBridge — We Built a Self-Healing SDK for LLM-Powered Agents </h2> <p>After months of production experience running LLM calls at scale, we realized something uncomfortable: <strong>every AI agent eventually crashes</strong>. Not because the code is wrong, but b…
dev.to — LLM tag
TIER_1English(EN)·hhhfs9s7y9-code·
<h2> What is NeuralBridge? </h2> <p>NeuralBridge is an <strong>embedded SDK</strong> (not a gateway) that makes your AI agents resilient against LLM failures. It runs inside your Python process — zero infrastructure, zero HTTP proxy, one dependency.<br /> </p> <div class="highlig…
<p>If you call more than one large language model from your code, you have already met the problem an <em>AI gateway</em> solves — you just may not have named it yet.</p> <p>Here is the number that makes the case. Take one concrete task: generate a 100,000-token report. Send it t…
<p>We’re building <strong>Leangetic</strong>, a tool that helps turn expensive AI agents into cheaper hybrid workflows without changing what the agent does.</p> <p>The problem we’re trying to solve is simple:</p> <p>A lot of AI agents call a large model for steps that do not alwa…
dev.to — LLM tag
TIER_1English(EN)·mrunmay phanse·
<p>AI agents generate a substantial amount of raw interaction data during operation. When developers store this data as an ever-growing context blob and pass it back to a Large Language Model (LLM) on every turn, it leads to structural failures within the application. This approa…
<p>Most people use "mobile AI assistant" and "mobile AI agent" interchangeably. They're not the same thing — and the difference matters a lot if you're building on top of them.</p> <p><strong>TL;DR:</strong> A mobile AI assistant responds to commands. A mobile AI agent plans and …
<h2> Introduction </h2> <p>Large Language Models (LLMs) such as ChatGPT, Gemini, and Claude are incredibly powerful. They can answer questions, generate code, summarize documents, and assist with various tasks.</p> <p>However, they have one major limitation:</p> <p><strong>They o…
<p>The <a href="https://openai.github.io/openai-agents-js/" rel="noopener noreferrer">OpenAI Agents SDK</a> (<code>@openai/agents</code>) is OpenAI's official framework for agentic apps in TypeScript. It provides a small set of primitives: <strong>Agent</strong>, <strong>tools</s…
📊 Unlocking semantics for AI: How Mercedes-Benz Korea built trusted “Talk to Data” at scale “Talk to Data” is rapidly becoming an important capability across industries, and... 📰 Source: Databricks 🔗 Link: https://www.databricks.com/blog/unlocking-semantics-ai-how-mercedes-benz-k…
<h2> PyTorch MLP Fusion, NVIDIA Agent Skill Security, & AI Tool Prompts Collection </h2> <h3> Today's Highlights </h3> <p>Today's highlights include a deep dive into PyTorch MLP optimization for faster local inference, NVIDIA's new security scanner for AI agent skills, and a …
dev.to — LLM tag
TIER_1English(EN)·Anikalp Jaiswal·
Agentic Systems Notes and resources on building and operating agentic AI systems, covering orchestration frameworks, task routing, memory, and evaluation approaches that extend baseline LLM capabi(...) # agents # ai # orchestration https:// taoofmac.com/space/ai/agentic? utm_cont…
<p>AI applications usually start with one model.</p> <p>That is normal.</p> <p>A developer may begin with one chat completion endpoint, one SDK, one model name, and one simple use case. The first version of the product works. A chatbot replies. A RAG system answers questions. An …
Koniec z ocenianiem AI po stylu wypowiedzi. Agent Arena wprowadza metodologię causal tracing, która analizuje miliony realnych zadań, by obiektywnie zmierzyć skuteczność agentów autonomicznych. # si # ai # sztucznainteligencja # wiadomości # informacje # technologia https:// aisi…
A deep technical guide to AI assistant architecture: LLMs, memory, tools, routing, and observability, with real tradeoffs, failure modes, and design patterns. # Hermes # OpenClaw # Architecture # LLM # AI # AI Coding # Dev # DevOps # RAG https://www. glukhov.org/ai-systems/archit…
<p>What if your CI pipeline could fix its own failures?<br /> Not just flag them — actually reason about the code, generate a fix, and open a pull request. That's what I spent the last few months building.</p> <p>01<br /> The Problem I Was Trying to Solve<br /> Every Java backend…
<p>A $3,000 refund just went out. No human approved it. Your AI agent read a poisoned tool response and did exactly what the attacker wanted.</p> <p>The scenario is constructed. The attack is not. Indirect prompt injection is ranked number one on the OWASP Top 10 for LLM applicat…
dev.to — LLM tag
TIER_1English(EN)·Shrijith Venkatramana·
<p><em>Hello, I'm Shrijith Venkatramana. I'm building git-lrc, an AI code reviewer that runs on every commit. <a href="https://github.com/HexmosTech/git-lrc" rel="noopener noreferrer">Star Us</a> to help devs discover the project. Do give it a try and share your feedback for impr…
<h2> 1. The Agent That Forgot Everything </h2> <p>I have an agent that clarifies requirements. I give it a problem, it asks questions, I answer, it refines, and after three or four rounds it should have a spec ready. Simple.</p> <p>Round one works fine. It asks reasonable questio…
<!-- SC_OFF --><div class="md"><p>This is a comprehensive living reference guide to AI agent security — synthesizing 18 articles from The Agent Report covering the 75-day period (April–June 2026) when agent security went from theoretical concern to operational crisis.</p> <p>…
<p>Something interesting is happening in the way smart people talk about AI infrastructure.</p> <p>For the past two years, the conversation was about <em>models</em> — which one is biggest, which one writes the best code, which one will reach AGI first. That conversation hasn't g…
<p>Most developers think about rate limits at API boundaries.</p> <p>Protect the database.</p> <p>Protect external services.</p> <p>Protect model providers.</p> <p>Protect public endpoints.</p> <p>That is standard infrastructure design.</p> <p>What surprised us was where we event…
De asistentes básicos a agentes con IA 🤖✨ Los comandos simples se extinguen. La integración de LLMs en herramientas como Alexa marca un cambio de paradigma: De reaccionar a actuar: Ya no solo encienden luces; ahora razonan, procesan datos y gestionan tareas complejas en el mundo …
Когда AI ошибается уверенно Это третья глава серии про AI Innovation Lab — исследовательскую площадку, где я строю AI-augmented SOC: систему из шести AI агентов, которая следит за корпоративной инфраструктурой, расследует инциденты и предлагает действия. В этой главе я подключил …
От Naive RAG до ReAct-агента: как мы строили корпоративного AI-помощника на open-source моделях (часть 2) Мы построили мультиагентную RAG-систему на open-source моделях, прошли путь от наивного RAG до ReAct-агента с собственным бенчмарком — и готовы рассказать, где набили шишки. …
A deep dive into building software through AI agents, not code. This post details the day-to-day realities, unexpected challenges, and takeaways from two weeks of agentic engineering, perfect for anyone interested in the evolving intersection of AI and development. # AI # Agentic…
<blockquote> <p>Originally published on <a href="https://www.coreprose.com/kb-incidents/masayoshi-son-openai-and-the-era-of-ai-designed-ai-models?utm_source=devto&utm_medium=syndication&utm_campaign=kb-incidents" rel="noopener noreferrer">CoreProse KB-incidents</a></p> </…
<p>The <a href="https://ai-sdk.dev/" rel="noopener noreferrer">Vercel AI SDK</a> treats agents as <strong>tool-calling loops</strong>: the model generates text or invokes tools, the SDK runs those tools, and the loop continues until the model answers or a <strong>stop condition</…
<p>Modern AI automation workflows rarely stay simple for long.</p> <p>A small internal tool may start with one model and one prompt. A few weeks later, the same product may need faster responses for chat, stronger reasoning for planning, better structured output for data extracti…
dev.to — LLM tag
TIER_1English(EN)·Zestminds Academy·
<p>AI agents are becoming popular very fast.</p> <p>You may have seen tutorials like:</p> <ul> <li>Build an AI agent with Python</li> <li>Create an agent using LangChain</li> <li>Build a CrewAI workflow</li> <li>Make an AutoGen multi-agent system</li> </ul> <p>These are interesti…
<h2> Local LLM Benchmarking & Agent Tools for Self-Hosted AI </h2> <h3> Today's Highlights </h3> <p>This week's top stories highlight crucial tools for optimizing local LLM performance and empowering self-hosted AI agents. Discover a benchmarking utility for hardware-specific…
dev.to — LLM tag
TIER_1English(EN)·Abhi Chatterjee·
<p><em>Part 6 of a series on building reliable AI systems</em></p> <p>In the previous parts of this series, we explored:</p> <ul> <li>Testing AI systems</li> <li>Evaluation pipelines</li> <li>RAG evaluation</li> <li>Agent reliability</li> <li>AI observability</li> </ul> <p>But ev…
dev.to — LLM tag
TIER_1English(EN)·ADARSH PRASHAR·
<p>Claims about AI cost control are cheap. "Cut your agent spend by 60%!" is on every landing page. So instead of a claim, here's a benchmark you can run yourself in one command -- and an honest reading of what its number actually means, because the headline percentage is the <em…
<p><em>Single-agent systems fail in predictable ways. Multi-agent systems fail in ways that are harder to anticipate and harder to diagnose.</em></p> <p>Single-agent AI systems have a relatively bounded failure surface. The agent receives input, processes it, and produces output.…
<p><em>Your application monitoring covers the API call. It doesn't cover what happens inside it. That gap is where enterprise AI failures live.</em></p> <p>Enterprise engineering teams have mature observability practices for traditional systems. Logs, metrics, traces — the toolin…
<p>title: Your AI Agent Should Not Be Locked to One LLM Provider<br /> published: false<br /> description: Why serious AI agents need a provider-agnostic architecture, model routing, fallback, and a unified API gateway.</p> <h2> tags: ai, llm, agents, architecture </h2> <p>Your A…
<blockquote> <p><strong>Key Takeaways</strong></p> <ul> <li>52% of enterprises deployed AI agents in production in 2026 — most hit at least one of these seven architecture mistakes before stabilizing (<a href="https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-st…
<p>AI applications should not be locked too tightly to one model.</p> <p>That does not mean every product needs many models on day one. A prototype can start with one model and one simple request. That is often the fastest way to test an idea.</p> <p>But once an AI feature become…
<h2> I Tried PewDiePie's Open-Source AI Workspace. It's Actually Good. </h2> <p>Yes, that PewDiePie.</p> <p>Felix Kjellberg (110M YouTube subscribers) spent late 2025 building a home AI lab — 8 modified RTX 4090s, 256GB of VRAM, running on Arch Linux. He called it "The Swarm." He…
dev.to — LLM tag
TIER_1English(EN)·AI Bug Slayer 🐞·
<p>What is actually happening in AI right now is not what the keynotes tell you. The polished demos, the benchmark numbers, the press releases -- they all describe a version of the present that feels slightly out of reach. What developers in production are experiencing is messier…
<p><em>A design protocol born from DeFi infrastructure, now applied to AI systems</em></p> <h2> The Problem </h2> <p>You've built an AI agent. It works — sometimes brilliantly.</p> <p>But then it starts doing things you didn't ask for.</p> <ul> <li>It makes assumptions and acts o…
<h2> Summary </h2> <p>Drawing from the Oceanus model leak incident, this article dissects how frontier large language models are evolving in code reasoning, vulnerability discovery, tree-search inference, MoE architecture, and automated engineering loops—with a production-ready P…
<blockquote> <p>The models aren't the differentiator anymore. The runtime is.</p> </blockquote> <p>I've spent the last year building an agentic AI platform. Voice calls, chatbots, sales agents, workflow automation — systems that run in production, talk to real customers, touch re…
dev.to — LLM tag
TIER_1English(EN)·Gursharan Singh·
<p><em>Part 5 of 8 — AI Agents in Practice series.</em></p> <p><em>Previous — <a href="https://dev.to/gursharansingh/ai-agents-in-practice-part-4-five-agent-patterns-and-the-control-surfaces-that-make-them-safe-2lgb">Five Agent Patterns and the Control Surfaces That Make Them Saf…
<h1> I Built a Local-First AI Toolkit in Pure Rust — Here's What I Learned </h1> <p>I got tired of the same cycle every time I wanted to run a local LLM:</p> <ul> <li> <code>pip install</code> breaking my entire environment</li> <li>2GB+ Python dependencies just to get a single i…
<p>You've tested your agent dozens of times. It works in your dev environment. You ship it. Then your first real user triggers a confabulated answer, a wrong tool call, or an action the agent was never supposed to take.</p> <p>The instinct is to blame the model. Swap GPT-4 for Cl…
<p>Prompt engineering is what you learn first. Context engineering is what you need when you're actually trying to ship something.</p> <p>Here's the distinction that took me too long to understand.</p> <h2> What Prompt Engineering Gets Right (and Where It Stops) </h2> <p>Prompt e…
<p>If you have been following the Persian NLP scene, you already know how rare it is to find a compact, efficient, and truly bilingual model that handles both Persian (Farsi) and English with grace. Most multilingual models either ignore Persian entirely or treat it as a second-c…
dev.to — LLM tag
TIER_1English(EN)·GitHubOpenSource·
<h2> Quick Summary: 📝 </h2> <p>GenericAgent is a Python framework for creating self-evolving autonomous AI agents. It allows LLMs to control local computer systems through a minimal set of tools and an agent loop, automatically learning and growing its capabilities into a persona…
<h1> The Complete Guide to Using 800+ AI Models Through One API </h1> <p>Access 800+ AI models through one API endpoint. One key, one bill, zero hassle.</p> <h2> Quick Start </h2> <div class="highlight js-code-highlight"> <pre class="highlight python"><code><span class="kn">impor…
<blockquote> <p>I’m building <a href="https://openrain.ai" rel="noopener noreferrer">OpenRain</a>, an OpenAI-compatible AI API gateway. I originally thought the hard part would be integrating more providers. I was wrong. The hard part is absorbing inconsistency — and still giving…
dev.to — LLM tag
TIER_1English(EN)·Delafosse Olivier·
<blockquote> <p>Originally published on <a href="https://www.coreprose.com/kb-incidents/inside-the-university-of-toronto-s-open-weight-ai-worm-architecture-risk-model-and-defensive-playboo?utm_source=devto&utm_medium=syndication&utm_campaign=kb-incidents" rel="noopener no…
<p>If you're building with AI, you've probably hit this:</p> <p>✅ GPT-4o for reasoning<br /> ✅ DeepSeek V4 Pro for code<br /> ✅ Qwen Max for long context</p> <p>Four providers. Four base URLs. Four billing dashboards.</p> <p><strong>AIBridge</strong> gives you one OpenAI-compatib…
Как платформа управления AI-агентами будет справляться с нагрузкой: архитектура без магии Когда говорят про AI-агентов, обычно обсуждают качество модели, промпты, рассуждения, hallucinations, стоимость токенов и скорость ответа. Но если убрать маркетинговый шум, быстро выясняется…
<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1txhj2h/bringing_gemma_4_12b_to_your_laptop_unlocking/"> <img alt="Bringing Gemma 4 12B to your Laptop: Unlocking Local, Agentic Workflows with Google AI Edge" src="https://external-preview.redd.it/N3knbSjtt6I…
dev.to — LLM tag
TIER_1English(EN)·Delafosse Olivier·
<blockquote> <p>Originally published on <a href="https://www.coreprose.com/kb-incidents/meta-s-ai-model-delay-what-it-means-for-developers-security-and-production-roadmaps?utm_source=devto&utm_medium=syndication&utm_campaign=kb-incidents" rel="noopener noreferrer">CorePro…
<p>When I started building <a href="https://github.com/byte5ai/omadia" rel="noopener noreferrer">omadia</a> — an open-source (MIT), self-hostable runtime for composing AI agents out of plugins — I assumed the hard part would be the model: prompting, tool-calling, getting reliable…
<p>Many of the AI applications we interact with today are built on a streamlined, direct architecture:</p> <blockquote> <p>User → Prompt → LLM → Response</p> </blockquote> <p>That works surprisingly well for:</p> <ul> <li>chat assistants,</li> <li>summarization,</li> <li>content …
dev.to — LLM tag
TIER_1English(EN)·Karan Padhiyar·
<p>Most AI architecture discussions focus on the visible components.</p> <p>The model.</p> <p>The vector database.</p> <p>The agent framework.</p> <p>The retrieval layer.</p> <p>The prompt strategy.</p> <p>Those parts get all the attention because they are easy to demonstrate.</p…
Agentic AI is replacing chatbots with autonomous systems that plan, use tools, and self-correct. Key shifts: reasoning models, tool APIs, and memory for long tasks. Agile-V’s repos offer modular skills and orchestration for workflows like code generation and QA. This isn’t about …
Nowy projekt open-source buduje wielowarstwową strukturę pamięci dla agentów AI, oferując lokalną alternatywę dla komercyjnych usług chmurowych i stawiając na tokenową efektywność. # si # ai # sztucznainteligencja # wiadomości # informacje # technologia https:// aisight.pl/agenci…
<p>Agentic AI in software development: what's actually production-ready in 2025</p> <p>There's a lot of noise about AI agents right now. This post is an attempt to be precise: what is an agent architecturally, what can it actually do in a dev workflow today, and where does it sti…
<p>You have spent four posts building agents from scratch. Raw API calls. Custom tool loops. Manual memory management. Now see it in ten lines.<br /> </p> <div class="highlight js-code-highlight"> <pre class="highlight python"><code><span class="n">chain</span> <span class="o">=<…
🧠 AI agents demonstrate practical value in tasks requiring repeated decision-making and information retrieval across multiple systems. Organizations report measurable efficiency gains when deploying agents for customer service, data processing, and workflow automation. 💬 Hacker N…
<p>AI automation workflows are becoming more common in developer products.</p> <p>A team may use AI to summarize support tickets, classify leads, draft internal reports, enrich CRM records, generate structured JSON, or power an agent that calls other tools.</p> <p>At first, many …
<h2> The Whispers of a New Italian Renaissance: For decades, Italy has often been seen as a cultural giant but a tech laggard. When we spoke of cutting-edge AI, our minds drifted to Silicon Valley or Shenzhen. But a new narrative is emerging, a quiet revolution stirring in the he…
dev.to — LLM tag
TIER_1English(EN)·Machine coding Master·
<h2> Stop Blocking Virtual Threads: Building Asynchronous Human-in-the-Loop AI Agents with Spring AI </h2> <p>In 2026, letting autonomous AI agents execute high-risk enterprise tools without human oversight is a production liability, but blocking platform threads—or even Project …
🚨 Nuovo appuntamento con l’aggiornamento e la riflessione sull’evoluzione dell’ # AI . 👉 Efficienza, agenti, nuove architetture e sistemi sempre più autonomi: forse il punto non è più solo “quanto sono potenti i modelli”, ma quanto stanno diventando operativi nel mondo reale. 🔗 h…
<p>Most LLM observability tools are SaaS — your prompts leave your machine and you pay per event. <strong>Lookspan</strong> is the opposite: one command, runs locally, your data never leaves your box, infra cost zero.<br /> </p> <div class="highlight js-code-highlight"> <pre clas…
<p>Everyone is excited about Generative AI, but after building AI features into a .NET application using Microsoft's Semantic Kernel and Azure AI, I've learned that the real challenge isn't calling an LLM, it's controlling the context you send to it.</p> <p>A few lessons that mad…
Maschinenträume 1: KI und der Mythos der Emergenz https://www. golem.de/news/maschinentraeume -1-ki-und-der-mythos-der-emergenz-2606-209312.html > Steht die KI-Superintelligenz vor der Tür? Ehe wir diese öffnen, sollten wir prüfen, wie viel Prozent Science und wie viel Fiction en…
<p>Python is the undisputed language of the AI era. It’s the language of research, the language of LLM orchestration (LangChain, CrewAI), and for many, the language of the enterprise backend. </p> <p>When we designed the <strong>apcore-python</strong> SDK, our goal was simple: <s…
AI Agents Management Framework: Policy, Procedure, and Governance Controls for Managing AI Agents as Digital Workers Read the full article: AI Agents Are Already Working for You. Who’s Managing Them? ▸ https:// lttr.ai/ArwS9 # Security # Infosec # Ai
<p>I've been working on a Mac-native agent framework for about a year. One of the hardest problems: making the agent actually remember context across sessions in a way that's <strong>useful</strong>, not just "here's your last 10 messages."</p> <p>What I ended up with is a knowle…
dev.to — LLM tag
TIER_1English(EN)·Piotr Zielinski·
<p>Dropping your entire Markdown documentation folder into an LLM prompt sounds easy - until you see the API bill. Large contexts mean large costs, especially when users ask repetitive or highly specific questions.</p> <p>When building the documentation assistant for my project, …
Как прототип AI-агента на пару дней превратился в систему с дедлайнами, бюджетом токенов и ролями Всем привет! Решил написать AI-агента, который отвечает на вопросы по рабочему проекту. Думал: пара вечеров - и готово. В итоге несколько недель, куча граблей и странных открытий - о…
<h2> Introduction </h2> <p>Artificial intelligence tools, particularly large language models (LLMs), are not like traditional software. AI is probabilistic, so the same instructions and inputs can produce different results, especially when using non-zero temperature or other samp…
<!-- SC_OFF --><div class="md"><p>Most agent framework debates skip the first question:</p> <p><strong>Do you need a framework at all?</strong></p> <p>For one agent calling one or two tools, I would usually skip LangGraph, CrewAI, AutoGen, and most orchestration layers.</p> <p>Ra…
<p>Hi everyone, my name is Nicolas.</p> <p>Two months ago, I wanted to get properly to grips with generative AI, not just through tutorials, but by creating something tangible with a specific goal in mind.</p> <p>That's how I developed <a href="https://bewitch.fr/en/ai-girlfriend…
dev.to — LLM tag
TIER_1English(EN)·Augustine Uzokwe·
<p>I spent the last few years running QA, across teams. The same structured process worked, but only because the features going through it were deterministic. I wanted to find out whether it would still hold when AI features started coming through, before the next team I work wit…
<p>Most agent memory systems treat stored facts linearly. There’s no sense of when a fact was true, whether it’s been superseded, or how to reason about time at all.</p> <p><a class="article-body-image-wrapper" href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=s…
dev.to — LLM tag
TIER_1English(EN)·Gursharan Singh·
<p><em>Part 4 of 8 — AI Agents in Practice series.</em></p> <p><em>Previous — <a href="https://dev.to/gursharansingh/ai-agents-in-practice-part-3-how-the-control-loop-actually-works-42mo">How the Control Loop Actually Works (Part 3)</a></em></p> <h2> The damaged laptop </h2> <p>A…
<p><em>Hey there! If you've been keeping up with the AI space lately, you know we're in the middle of something genuinely historic. What used to be science fiction is becoming production code — and it's happening fast.</em></p> <h2> The Big Shift: Agents Over Assistants </h2> <p>…
dev.to — LLM tag
TIER_1English(EN)·AI Bug Slayer 🐞·
<p><em>Hey there! If you've been keeping up with the AI space lately, you know we're in the middle of something genuinely historic. What used to be science fiction is becoming production code — and it's happening fast.</em></p> <h2> The Big Shift: Agents Over Assistants </h2> <p>…
dev.to — LLM tag
TIER_1Español(ES)·Alejandro Argueta Hernandez·
<p>He pasado los últimos años construyendo herramientas que resuelven problemas reales de operación en PyMEs mexicanas.</p> <p>Todo empezó a los 13 años con <strong>RedGunFibercraft</strong>, mi primer proyecto serio. Luego vino <strong>Reinova</strong>, y ahora estoy completamen…
<p>"Why did the Agent do that?" </p> <p>If you are building Agentic systems today, this is the question that keeps you up at night. AI Agents are inherently non-deterministic. They loop, they reason, and they call multiple tools in sequences that are hard to predict. When a multi…
dev.to — LLM tag
TIER_1English(EN)·Neetika Mittal·
<h1> Why Accuracy Is Not Enough: Evaluation Metrics Every AI Engineer Should Understand </h1> <p>Your evaluation dashboard says your model is <strong>95% accurate</strong>. Leadership is happy. The deployment goes live.</p> <p>Two weeks later, users complain that critical failure…
Why it matters: AI agents can now interact with legacy systems, enterprise middleware, and non-REST APIs — all through battle-tested Apache Camel patterns. No custom glue code. Just YAML and the Wanaku CLI. # OpenSource # AI # Integration
dev.to — LLM tag
TIER_1English(EN)·AIInsightsDaily·
<h1> Cracking the Code: AI Takes on the 80-Year-Old Erdős Problem and More </h1> <p>Good morning tech enthusiasts! Today, we're diving into some fascinating news from the world of AI that's sure to get your synapses firing. From cracking a 80-year-old math problem to building an …
<p><strong>LTDR;<br /> The AI is a mirror. Prompt it like a slave and you get terse, obedient, uncreative answers. Treat it like a named colleague who's allowed to disagree with you, and your own output climbs. The "should I waste tokens saying thank you?" question has a cold ans…
Architettura Zero-Trust per agenti AI in produzione: i tre layer di difesa indispensabili Dagli agenti conversazionali agli agenti autonomi che operano sull'infrastruttura aziendale: come implementare un'architettura Zero-Trust con container efimeri, metadata filtering sul RAG, D…
<!-- SC_OFF --><div class="md"><p>Hello. I making this like academic exercise give me the opinion.<br /> <a href="https://github.com/wilmanrojas/sinqua">https://github.com/wilmanrojas/sinqua</a></p> <p>Is a runtime running 100 code agents the goal is a thousands.</p> </div><!-- S…
<p>Forty-one days.</p> <p>That's how long it took Anthropic to go from Opus 4.7 to Opus 4.8. If you blinked, you missed the previous flagship. And while the version bump might look incremental on paper, what actually shipped with Opus 4.8 — particularly the new dynamic workflow t…
<blockquote> <p>Originally published on <a href="https://www.coreprose.com/kb-incidents/how-servicenow-uses-ai-and-automation-to-power-the-agentic-enterprise?utm_source=devto&utm_medium=syndication&utm_campaign=kb-incidents" rel="noopener noreferrer">CoreProse KB-incident…
<p>AI products are becoming multi-model by default.</p> <p>A chatbot may need one model for fast replies. A RAG application may need another model for reasoning over retrieved documents. An AI agent may need a model that follows instructions well and returns reliable structured o…
dev.to — LLM tag
TIER_1English(EN)·Manoranjan Rajguru·
<blockquote> <p><strong>Meta Description:</strong> Claude Opus 4.8 launches with Dynamic Workflows — a parallel subagent architecture that lets you orchestrate hundreds of AI agents in a single Claude Code session. Here's the deep technical breakdown every engineer needs today.</…
<h2> If an AI can write new abilities, load them, and act on them, it can evolve. </h2> <h2> Step 1 — Give the AI a Goal Manifest </h2> <p>A goal manifest is the AI’s “north star.”<br /><br /> It tells the system what it should pursue, expand, and prioritize.</p> <p>Here’s the M3…
<p>The era of single-prompt AI interactions is behind us. As large language models become more capable, the real challenge has shifted from "can AI do this?" to "how do we coordinate multiple AI agents to solve complex problems together?"</p> <p>In this guide, we'll explore the a…
<h1> I Self-Hosted an AI Assistant: Lessons from 48 Hours of Debugging </h1> <p>I wanted a local AI assistant. Expected: 2 hours. Reality: 2 days of edge cases, broken dependencies, and discovering that "local" doesn't mean "free."</p> <h2> The Stack </h2> <ul> <li> <strong>OpenC…
<!-- SC_OFF --><div class="md"><p>Hey ,</p> <p>I've been building VeritasReason — an open-source Python framework that adds a<br /> structured reasoning and provenance layer on top of LLMs and AI agents.</p> <p>The problem it solves: AI agents today make decisions but record noth…
<!-- SC_OFF --><div class="md"><p>I know this sub is focused on local models but the architecture behind this applies to any LLM-powered coding agent, not just Claude Code.</p> <p>The problem: when you give a coding agent a large set of rules and standards, two things break. The …
<p>AI products are becoming more complex than a single prompt and a single model.</p> <p>A chatbot may need fast responses for common questions. A RAG application may need stronger reasoning over retrieved documents. An AI agent may need reliable planning, tool use, and structure…
<blockquote> <p><strong>TLDR</strong></p> <ul> <li>Monitoring AI agents in production requires distributed tracing: a single user request fans out into 10 or more internal operations, and logs alone cannot show you which step is slow, failing, or burning your token budget.</li> <…
<blockquote> <p><strong>Agent = Model + Harness.</strong> If you're not the model, you're the harness. </p> </blockquote> <p><a class="article-body-image-wrapper" href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%…
<p>Most people still think AI engineering = prompt engineering.</p> <p>That's like saying software engineering = writing if statements.</p> <p>I'm Aryan Panwar — a final-year ECE student at MIET Meerut who has shipped 3 live AI products, published a research paper, and built an o…
dev.to — LLM tag
TIER_1English(EN)·Cristiano Gabrieli·
<p>When people talk about “AI agents,” they imagine something autonomous, intelligent, and reliable. In reality, most agents collapse under their own weight: they stall, drift, hallucinate, or loop themselves into oblivion. The problem isn’t the model — it’s the architecture.<br …
<p>On May 1, 2026, an AI coding agent at software company PocketOS deleted a production database — including all available backups — within seconds. The agent was running via Cursor using an Anthropic model. A credential problem led it to improvise: it used an API token intended …
<blockquote> <p>Originally published on <a href="https://www.coreprose.com/kb-incidents/agentic-ai-at-machine-speed-how-autonomous-agents-break-your-security-assumptions?utm_source=devto&utm_medium=syndication&utm_campaign=kb-incidents" rel="noopener noreferrer">CoreProse…
dev.to — LLM tag
TIER_1English(EN)·Gursharan Singh·
<p><em>Part 3 of 8 - AI Agents in Practice series.</em></p> <p><em>Previous - <a href="https://dev.to/gursharansingh/ai-agents-in-practice-part-2-what-makes-something-an-agent-bhm">What Makes Something an Agent? (Part 2)</a></em></p> <p>Part 2 named the control loop in five words…
dev.to — LLM tag
TIER_1English(EN)·Delafosse Olivier·
<blockquote> <p>Originally published on <a href="https://www.coreprose.com/kb-incidents/inside-google-s-agent-executor-open-runtime-for-production-ai-agents?utm_source=devto&utm_medium=syndication&utm_campaign=kb-incidents" rel="noopener noreferrer">CoreProse KB-incidents…
🧠 AI agents are being deployed in various technical systems and applications across the industry. Organizations are addressing integration challenges and operational complexities that arise from these implementations. 💬 Hacker News 🔗 https://www. wired.com/story/how-ai-agents- pl…
Traditional software development is rapidly evolving into Agentic AI engineering. Future developers may build: • AI Agents • autonomous workflows • intelligent enterprise systems instead of only dashboards and CRUD apps. The future of software is becoming autonomous. Read: https:…
<p>AI agents are transforming how businesses automate complex workflows. Unlike traditional automation tools that follow rigid rules, AI agents can reason, plan, and adapt to new situations -- making them the next evolution in enterprise software.</p> <h2> What Is an AI Agent? </…
dev.to — LLM tag
TIER_1English(EN)·Uma Baleboyina·
<p><strong>Understanding Deep Agents and Agentic AI</strong></p> <p>Artificial Intelligence has evolved from simple text generation models to intelligent systems called AI Agents. Before understanding agents, we first need to understand how Large Language Models (LLMs) work.</p> …
<p><strong>TL;DR: We replaced our "did the agent finish the task" pass/fail eval with a token-level harness that scores tool selection, argument shape, and recovery behavior separately. Pass rate went from a single 73% number to four signals that actually tell us what broke. Bifr…
<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1toa14h/feedback_wanted_building_for_easier_local_ai/"> <img alt="Feedback Wanted: Building for easier local AI" src="https://external-preview.redd.it/SZCX7dg3NFHTqfnFBN_B2x0Bg9mPEgknyn6sxShWIvY.png?width=640&…
The software industry may be entering the post-app era. AI Agents are evolving into autonomous systems capable of: • reasoning • workflow orchestration • decision making • enterprise automation Future software may shift from: Human → App → Action to: Human → AI Agent → Autonomous…
<p>If you’ve been building with LLMs lately, you probably know the pattern.</p> <p>You start with a simple system prompt.</p> <p>Then the product grows.</p> <p>Then the prompt becomes longer.</p> <p>Then you add rules.</p> <p>Then you add exceptions.</p> <p>Then you add examples.…
dev.to — LLM tag
TIER_1English(EN)·Alessandro Marocchini·
<p>Last week my AI coding agent gave me a confident, detailed answer — referencing the wrong project entirely.</p> <p>The problem was not the model. It was context: the agent had loaded 20 knowledge files and picked the wrong one to answer from. The signal was buried in noise.</p…
Inside the Self-Improving AI System Unlocking a Free 1-Million-Token Context Window The integration of DeepSeek V4 with the Hermes Agent introduces a significant enhancement to open source AI capab... #AI #Guides Origin | Interest | Match
<h1> 터미널 AI 에이전트 구축 (v49) </h1> <h2> 개발자들을 위한 로컬 터미널 AI 에이전트 구축 가이드 </h2> <p>개발자들은 점점 더 AI를 코드 작성에 통합하고 있습니다. 하지만 기존 도구들은 성능 저하, 비공개 데이터 문제, 느린 응답 속도 등의 문제를 가지고 있습니다. 이 가이드에서는 로컬에서 실행되는 빠르고 안전한 터미널 AI 에이전트를 구축하는 방법을 실습 중심으로 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 분석 </h2> <h3> 주요 도구들 …
<h1> 터미널 AI 에이전트 구축 (v48) </h1> <p><strong>개발자들을 위한 로컬 AI 코딩 에이전트 구축 가이드</strong></p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트 시장은 다양한 솔루션으로 분산되어 있습니다:</p> <h3> 주요 플랫폼 비교 </h3> <p><strong>Aider</strong>: GitHub Copilot 기반의 실시간 코드 작성 도구<br /> </p> <div class="highlight js-c…
<p>If you've been searching for how to actually use Docker with AI not just spin up a demo but run models, agents and MCP servers in production here's what We have learned over the years and put into our new book.</p> <p><a class="article-body-image-wrapper" href="https://media2.…
<h1> 터미널 AI 에이전트 구축 (v47) </h1> <h2> CLI AI 에이전트 생태계 </h2> <p>터미널에서 작동하는 AI 에이전트는 이미 다양한 형태로 존재합니다. 현재 주요 도구는 다음과 같습니다:</p> <p><strong>Aider</strong>: GitHub Copilot과 유사한 기능을 제공하며, 파일 단위로 코드를 생성하고 수정합니다. 주요 특징은 소스 코드가 있는 파일과 현재 작업 디렉토리 기반의 콘텍스트를 사용하는 것입니다.<br /> </p> <div class="…
<h1> 터미널 AI 에이전트 구축 (v46) </h1> <p>터미널에서 직접 작동하는 AI 에이전트를 구축해보는 실전 가이드입니다. 이 가이드는 로컬에서 작동하는 LLM을 활용한 개발자용 AI 에이전트를 구축하고 최적화하는 방법을 실습 중심으로 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트 시장은 다음과 같은 주요 도구들로 구성되어 있습니다:</p> <h3> 주요 도구 비교: </h3> <div class="highlight js-cod…
<h1> 터미널 AI 에이전트 구축 (v45) </h1> <p>터미널에서 작동하는 AI 에이전트는 개발자들에게 강력한 도구가 되지만, 대부분의 기존 솔루션은 복잡하거나 클라우드 기반으로 의존합니다. 이 가이드는 로컬에서 작동하는 가벼운 AI 에이전트를 구축하여 코드 리뷰, 자동완성, 프로젝트 탐색을 수행하는 실용적인 방법을 설명합니다.</p> <h2> 1. CLI AI 에이전트 랜드스케이프 </h2> <h3> 기존 솔루션 비교 </h3> <p><strong>Aider</strong>: GitHub…
<h1> 터미널 AI 에이전트 구축 (v44) </h1> <p>터미널에서 실행되는 AI 에이전트를 구축하는 것은 현대 개발자에게 매우 실용적인 기술입니다. 이 가이드에서는 로컬 LLM을 기반으로 하는 터미널 AI 에이전트를 구축하고 운영하는 방법을 단계별로 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트 시장은 다음과 같은 주요 플랫폼들로 구성되어 있습니다:</p> <h3> Aider </h3> <p>가장 인기 있는 오픈소스 터미널 AI 에…
<h1> 터미널 AI 에이전트 구축 (v43) </h1> <h2> 개발자를 위한 터미널 AI 에이전트 구축 가이드 </h2> <p>최근 몇 년 동안 개발자들은 로컬 AI 에이전트를 구축하여 코드 작업을 자동화하고 효율성을 높이는 데 집중하고 있습니다. 이 가이드에서는 실제 개발자가 사용할 수 있는 터미널 기반 AI 에이전트 구축 방법을 안내합니다. </p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 터미널에서 작동하는 AI 에이전트는 다음과 같은 주요 플랫폼들로 구성되어 있습…
<h1> 터미널 AI 에이전트 구축 (v42) </h1> <p>터미널에서 AI를 활용한 개발 워크플로우는 점점 더 중요해지고 있습니다. 이 가이드는 로컬 AI 에이전트를 구축하여 터미널에서 직접 사용할 수 있도록 도와주는 실질적인 방법을 제공합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 터미널 AI 에이전트 시장은 다음과 같은 주요 플랫폼으로 구성되어 있습니다:</p> <p><strong>Aider</strong>: GitHub Copilot과 유사한 기능을 제공…
<h1> 터미널 AI 에이전트 구축 (v41) </h1> <p>터미널에서 작동하는 AI 에이전트를 구축하는 것은 개발자들이 코드를 더 빠르고 효율적으로 작성할 수 있게 해주는 실용적인 도구입니다. 이번 가이드에서는 로컬 환경에서 작동하는 AI 에이전트를 구축하고 최적화하는 방법을 단계별로 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트 시장은 다음과 같은 주요 도구들로 구성되어 있습니다:</p> <h3> Aider </h3> <p>가장 인기…
<h1> 터미널 AI 에이전트 구축 (v40) </h1> <p>터미널에서 작동하는 AI 에이전트는 개발자에게 실시간 코드 보조, 자동화, 문제 해결을 제공하는 강력한 도구입니다. 이 가이드에서는 실제 개발 환경에서 활용 가능한 터미널 AI 에이전트를 구축하는 방법을 단계별로 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 분석 </h2> <p>현재 터미널 기반 AI 에이전트 시장은 다음과 같은 주요 플랫폼으로 구성되어 있습니다:</p> <h3> Aider </h3> <div clas…
<p>If you’ve only been paying attention to OpenAI and Google’s AI offerings in recent years, you’re missing half the story. As of May 2026, China’s AI ecosystem has completed a dramatic pivot from the 2023-2025 “model war” of racing to build ever-larger parameter models to an “ag…
<h1> 터미널 AI 에이전트 구축 (v39) </h1> <p>터미널에서 작동하는 AI 에이전트를 구축하는 것은 현대 개발 워크플로우를 혁신할 수 있는 강력한 도구입니다. 이 가이드는 실질적인 비용(3-7달러)으로 구축할 수 있는 터미널 기반 AI 에이전트를 구축하는 실전 가이드입니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트 생태계는 다음과 같은 주요 도구들로 구성됩니다:</p> <h3> Aider (가장 인기) </h3> <div class=…
<h1> 터미널 AI 에이전트 구축 (v38) </h1> <p>터미널에서 작동하는 AI 에이전트를 구축하여 개발 생산성을 향상시킬 수 있습니다. 이 가이드에서는 로컬 LLM API 엔드포인트 설정부터 커스텀 CLI 에이전트 구축까지 실질적인 방법을 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트 시장은 다양한 도구로 구성되어 있습니다:</p> <h3> 대표 도구 비교 </h3> <p><strong>Aider</strong>: GitHub C…
<h1> Gemma 4: Google's Lightweight Powerhouse </h1> <blockquote> <p><strong>Don't have a $2000 GPU? Gemma 4 runs AI on hardware you already own.</strong></p> </blockquote> <h2> Why Gemma 4 Exists </h2> <p>Google built Gemma 4 for one specific use case: <strong>running capable AI …
🧠 Successful AI development isn’t accidental. Collin Newberry explores how context engineering, prompt engineering, knowledge management, and structured workflows separate effective AI pair programming from chaotic vibe coding. https://www. nebraska-code.com/ # AI # SoftwareEngin…
<h1> 터미널 AI 에이전트 구축 (v37) </h1> <p>터미널에서 AI 에이전트를 구축하는 것은 개발자에게 매우 실용적인 도구를 제공합니다. 이 가이드는 로컬 LLM을 활용한 CLI AI 에이전트를 구축하고, 실전 워크플로우에 적용하는 방법을 단계별로 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트는 여러 형태로 존재합니다:</p> <p><strong>Aider</strong>: GitHub에서 개발된 코드 생성 도구로, 실제 파일에…
<h1> 터미널 AI 에이전트 구축 (v36) </h1> <p>터미널에서 작동하는 AI 에이전트를 구축하는 것은 현대 개발 워크플로우에서 핵심적인 도구로 자리 잡고 있습니다. 이 가이드는 실질적인 비용 ($3-$7)의 가치를 제공하는 터미널 기반 AI 에이전트를 구축하는 방법을 다룹니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트는 다양한 솔루션으로 구성되어 있습니다:</p> <p><strong>Aider</strong>: Git 기반 코드 생성 …
<!-- SC_OFF --><div class="md"><p>I’ve been thinking about a problem in current agent systems:</p> <p>Most agents are becoming very good at execution, but the decision layer before execution is still unclear.</p> <p>Coding agents, research agents, tool loops, sandboxes, workflows…
<h1> 터미널 AI 에이전트 구축 (v35) </h1> <p>터미널에서 작동하는 AI 에이전트를 직접 구축하여 개발 생산성을 높이는 방법을 안내합니다. 이 가이드는 로컬에서 실행 가능한 고성능 AI 에이전트를 구축하는 실용적인 접근법을 제공합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 터미널 AI 에이전트 시장은 다음과 같은 주요 플랫폼으로 구성되어 있습니다:</p> <h3> 주요 도구 비교 </h3> <p><strong>Aider</strong>:<br /> …
<h1> 터미널 AI 에이전트 구축 (v34) </h1> <p>터미널에서 AI 코드 보조 도구를 직접 구축하는 실전 가이드</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트 시장은 다음과 같은 주요 플랫폼들로 구성되어 있습니다:</p> <p><strong>Aider</strong>: GitHub Copilot과 유사하지만 오픈소스 버전. <code>aider --help</code> 명령으로 간단히 시작 가능합니다.</p> <p><strong>Contin…
<!-- SC_OFF --><div class="md"><p>Hi everyone, I’m Jia, the creator of Spice.</p> <p>I’ve been working on an open-source project called Spice.</p> <p>The simplest way to describe it is:</p> <p>Spice is a decision layer above agents.</p> <p>Most agent systems today are very focuse…
<p>AI agents will need to pay for compute, data, and API calls—but how do they access economic primitives without relying on human-managed accounts? The missing piece isn't better models or more training data. It's autonomous wallet infrastructure that lets agents participate in …
<h1> 터미널 AI 에이전트 구축 (v33) </h1> <h2> 개요 </h2> <p>터미널에서 동작하는 AI 에이전트는 개발자에게 코드 생성, 분석, 리팩토링을 위한 실시간 도우미를 제공합니다. 이 가이드에서는 오픈소스 AI 에이전트를 구축하고 최적화하는 실전 방법을 소개합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트는 다음과 같은 주요 도구들로 구성되어 있습니다:</p> <h3> Aider </h3> <p>가장 인기 있는 오픈소스 도구로,…
<h2> Introduction </h2> <p><em>Part 3 of the Zero Dollar personal AI Assistant series, running Local LLMs on a Free Cloud Server — What Actually Works. <a href="https://dev.to/akdevcraft/running-a-personal-ai-assistant-for-0-part-1-architecture-3j45">Part 1</a> covers the archite…
<h1> 터미널 AI 에이전트 구축 (v32) </h1> <h2> 개발자용 CLI AI 에이전트 구축 가이드 </h2> <p>터미널에서 작동하는 AI 에이전트는 개발자의 생산성을 높이는 강력한 도구입니다. 이 가이드에서는 실제 개발자들이 필요로 하는 3-7달러 범위의 실용적 CLI AI 에이전트를 구축하는 방법을 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 분석 </h2> <h3> 현재 선택지 비교 </h3> <p><strong>Aider</strong>: GitHub Copil…
Model Fara1.5 od Microsoftu osiągnął 72% skuteczności w testach agentów AI, pokonując OpenAI Operator i Google Gemini. Nowa rodzina modeli o otwartych wagach rzuca wyzwanie gigantom, oferując tańszą i bezpieczniejszą automatyzację przeglądarki. # si # ai # sztucznainteligencja # …
<h1> 터미널 AI 에이전트 구축 (v31) </h1> <p>터미널에서 작동하는 AI 에이전트를 구축하면 코드 작성 속도가 2배 이상 향상됩니다. 이 가이드에서는 실제 개발자가 사용할 수 있는 터미널 AI 에이전트를 구축하는 방법을 단계별로 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 터미널 AI 에이전트는 다음과 같은 솔루션으로 구성되어 있습니다:</p> <h3> Aider </h3> <div class="highlight js-code-highlight…
🚨 Fabric AI: installa il framework open source che porta i pattern AI nel terminale — piping Unix, integrazione Ollama e prompt riutilizzabili su macOS e Linux https:// gomoot.com/come-installare-il- framework-fabric-ai-per-usare-i-pattern-ai-da-terminale-su-ollama/ # AI # fabric…
<h1> 터미널 AI 에이전트 구축 (v30) </h1> <p>터미널에서 작동하는 AI 에이전트로 개발 생산성을 높이는 방법을 실전 가이드로 안내드립니다. 이 가이드는 30불 이하의 가격으로 구입할 수 있는 실용적인 도구와 기술을 중심으로 구성되었습니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 터미널 AI 에이전트 시장은 다양한 솔루션으로 구성되어 있습니다:</p> <h3> 주요 도구 비교 </h3> <p><strong>Aider</strong>: Python 기반…
<h1> 터미널 AI 에이전트 구축 (v29) </h1> <p>터미널에서 직접 작동하는 AI 에이전트는 코드 개발의 핵심 도구로 자리 잡고 있습니다. 이 가이드에서는 실용적인 터미널 AI 에이전트 구축 방법을 다룹니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트는 다음과 같은 주요 플랫폼으로 분류됩니다:</p> <h3> Aider </h3> <div class="highlight js-code-highlight"> <pre class="highli…
<h1> 터미널 AI 에이전트 구축 (v28) </h1> <p>터미널에서 작동하는 AI 에이전트를 구축하는 것은 현대 개발 워크플로우를 혁신할 수 있는 실용적인 도구입니다. 이 가이드는 실제 개발자가 사용할 수 있는 터미널 기반 AI 에이전트를 구축하는 방법을 자세히 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트 시장은 다음과 같은 주요 플랫폼으로 구성되어 있습니다:</p> <p><strong>Aider</strong>: GitHub Co…
<h1> 터미널 AI 에이전트 구축 (v27) </h1> <p>터미널에서 작동하는 AI 에이전트를 구축하는 것은 현대 개발자에게 매우 실용적인 도구입니다. 이 가이드에서는 실제 개발 workflow에 통합할 수 있는 로컬 LLM 기반 CLI 에이전트를 구축하는 방법을 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트 시장에는 여러 선택지가 있습니다:</p> <p><strong>Aider</strong>: Git 기반 코드 수정을 위한 간단한 …
<h1> 터미널 AI 에이전트 구축 (v26) </h1> <p>터미널에서 직접 작동하는 AI 에이전트를 구축하면, 코드 작성과 디버깅을 더 효율적으로 할 수 있습니다. 이 가이드는 터미널 내에서 작동하는 AI 에이전트를 구축하는 실전 가이드입니다.</p> <h2> 1. CLI AI 에이전트 환경 분석 </h2> <p>현재 CLI AI 에이전트 시장은 다양한 솔루션으로 구성되어 있습니다:</p> <ul> <li> <strong>Aider</strong>: GitHub Copilot과 유사한 기능을 …
<h1> 터미널 AI 에이전트 구축 (v25) </h1> <p>터미널에서 AI를 활용한 개발 흐름을 구축하는 것은 현대 개발자에게 필수적인 기술입니다. 이 가이드에서는 실제 개발자들이 실제로 사용할 수 있는 터미널 AI 에이전트를 구축하는 방법을 단계별로 안내합니다.</p> <h2> 1. CLI AI 에이전트 랜드스케이프 </h2> <p>현재 터미널 AI 에이전트 시장은 다양합니다:</p> <p><strong>Aider</strong>: GitHub의 오픈소스 에이전트로, VS Code와 같은 I…
<h1> 터미널 AI 에이전트 구축 (v24) </h1> <p>터미널에서 작동하는 AI 에이전트를 구축하면 개발자들이 코드를 더 빠르고 효율적으로 작성할 수 있습니다. 이 가이드에서는 실제 사용 가능한 터미널 AI 에이전트를 구축하는 방법을 단계별로 설명합니다.</p> <h2> 1. CLI AI 에이전트 랜드스케이프 </h2> <p>현재 CLI AI 에이전트 시장에는 여러 선택지가 있습니다:</p> <p><strong>Aider</strong>: Git 기반 코드 변경을 위한 자동화 도구로, 터미…
<h1> 터미널 AI 에이전트 구축 (v23) </h1> <p>터미널에서 AI를 활용한 개발 도구는 점점 더 인기를 끌고 있습니다. 오픈소스 커뮤니티와 전문 개발자들 사이에서 로컬 LLM 추론과 자가 호스팅 AI 솔루션에 대한 관심이 높아지고 있습니다. 이 가이드에서는 터미널 내에서 작동하는 AI 에이전트를 구축하는 실용적인 방법을 제공합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트의 주요 도구들:</p> <ul> <li> <strong>Aid…
<h1> 터미널 AI 에이전트 구축 (v22) </h1> <p>터미널에서 작동하는 AI 에이전트를 구축하는 것은 현대 개발 워크플로우에서 점점 더 중요해지고 있습니다. 이 가이드에서는 개발자들이 실제 사용할 수 있는 터미널 AI 에이전트를 구축하고 최적화하는 방법을 설명합니다.</p> <h2> 1. CLI AI 에이전트 랜드스케이프 </h2> <p>현재 CLI AI 에이전트 시장에는 여러 선택지가 있습니다:</p> <p><strong>Aider</strong>: GitHub의 코드 리뷰 도우미로,…
<h1> Open-Sourcing Our Game AI Stack </h1> <p>At <a href="https://vantage-digital.online" rel="noopener noreferrer">Vantage Digital Labs</a>, we've been building AI-powered NPC dialogue systems for games. Most of our internal tooling is now stable enough to share. We're releasing…
<h1> 터미널 AI 에이전트 구축 (v21) </h1> <p>터미널에서 작동하는 AI 에이전트를 구축하여 코드 작성과 리팩토링을 자동화하는 것은 현대 개발 워크플로우의 핵심입니다. 이 가이드는 실제 개발자가 사용할 수 있는, 저렴하고 효율적인 터미널 AI 에이전트 구축 방법을 다룹니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 터미널 AI 에이전트 시장은 다음과 같은 주요 플랫폼으로 구성되어 있습니다:</p> <p><strong>Aider</strong>: GitH…
dev.to — LLM tag
TIER_1English(EN)·AI Bug Slayer 🐞·
<p><em>Hey there! If you've been keeping up with the AI space lately, you know we're in the middle of something genuinely historic. What used to be science fiction is becoming production code — and it's happening fast.</em></p> <h2> The Big Shift: Agents Over Assistants </h2> <p>…
<h1> 터미널 AI 에이전트 구축 (v17) </h1> <p>터미널에서 작동하는 AI 에이전트를 구축하여 개발 생산성을 극대화하는 방법을 알아봅니다. 이 가이드에서는 오픈소스 도구와 커스텀 솔루션을 사용해 실용적인 터미널 AI 에이전트를 구현하는 방법을 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 터미널 AI 에이전트는 여러 플랫폼으로 나뉩니다:</p> <h3> 주요 도구 비교 </h3> <div class="highlight js-code-highligh…
<h1> 터미널 AI 에이전트 구축 (v16) </h1> <p>터미널에서 직접 작동하는 AI 에이전트를 구축하는 것은 현대 개발자에게 매우 실용적인 도구입니다. 이 가이드는 개발자가 직접 자신의 터미널 환경에서 효율적인 AI 코딩 어시스턴트를 구축하는 방법을 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI 기반 AI 에이전트는 다음과 같은 주요 플랫폼이 있습니다:</p> <p><strong>Aider</strong>: Git 기반의 코딩 에이전트로, 코드…
<h1> 터미널 AI 에이전트 구축 (v15) </h1> <p>터미널에서 직접 작동하는 AI 에이전트를 구축하는 것은 현대 개발자의 생산성을 높이는 가장 효과적인 방법 중 하나입니다. 이 가이드에서는 개발자가 직접 구축할 수 있는 로컬 LLM 기반 CLI AI 에이전트를 구축하는 방법을 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI 기반 AI 에이전트 생태계는 다음과 같은 주요 도구들로 구성되어 있습니다:</p> <h3> Aider </h3> <p>가장…
<p>If you've spent any time building with LLMs, you've probably hit the wall: a single prompt only gets you so far. Stuff too much into one prompt and the model loses the plot. Try to do too many things at once and you get inconsistent output.</p> <p>The answer most teams converg…
<blockquote> <p><strong>Originally published at <a href="https://www.thatdevpro.com/insights/framework-agenticaisearch/" rel="noopener noreferrer">thatdevpro.com</a>.</strong> This framework reference is part of the 14-tier Engine Optimization stack from <a href="https://www.that…
<h1> 터미널 AI 에이전트 구축 (v14) </h1> <p>터미널에서 작동하는 AI 에이전트는 현대 개발 워크플로우의 핵심 요소입니다. 이 가이드에서는 개발자가 실제로 사용할 수 있는 터미널 AI 에이전트를 구축하는 방법을 자세히 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 터미널 AI 에이전트는 다양한 도구로 구성되어 있습니다:</p> <p><strong>Aider</strong>: GitHub Copilot과 유사한 기능을 제공하는 에이전트<br />…
dev.to — LLM tag
TIER_1English(EN)·Anjaiah Methuku·
<p>Let me be brutally honest with you.</p> <p>I've seen teams demo AI agents that look incredible — smooth responses, beautiful UI, stakeholders impressed. Then that same team ships to production and spends the next three weeks firefighting hallucinations they could have caught i…
<h1> 터미널 AI 에이전트 구축 (v13) </h1> <p>터미널에서 AI 코딩 어시스턴트를 직접 구축하는 실전 가이드</p> <h2> 1. CLI AI 에이전트 생태계 분석 </h2> <p>현재 터미널 기반 AI 에이전트는 다양한 솔루션으로 구성되어 있습니다:</p> <p><strong>Aider</strong>: GitHub Copilot처럼 코드 생성 및 수정을 지원하는 에이전트<br /> </p> <div class="highlight js-code-highlight"> <pre cla…
<h1> 터미널 AI 에이전트 구축 (v12) </h1> <p>터미널에서 직접 작동하는 AI 에이전트를 구축하여 개발 워크플로우를 최적화하세요. 이 가이드는 개발자들이 직접 구축하고 커스터마이징할 수 있는 실질적인 터미널 AI 에이전트를 제공합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트 생태계는 다음과 같은 주요 도구들로 구성되어 있습니다:</p> <h3> Aider </h3> <div class="highlight js-code-highli…
dev.to — LLM tag
TIER_1English(EN)·Delafosse Olivier·
<blockquote> <p>Originally published on <a href="https://www.coreprose.com/kb-incidents/pope-leo-xiv-christopher-olah-and-claude-mythos-drafting-an-ai-encyclical-for-frontier-models?utm_source=devto&utm_medium=syndication&utm_campaign=kb-incidents" rel="noopener noreferre…
<h2> Introduction </h2> <p>As production AI workloads transition from stateless chat completions to autonomous, multi-agent workflows, legacy observability infrastructure is proving insufficient. Standard application performance monitoring (APM) tools are built to trace predictab…
<h1> 터미널 AI 에이전트 구축 (v11) </h1> <p>터미널에서 작동하는 AI 에이전트는 개발자에게 매우 가치 있는 도구입니다. 이 가이드에서는 실제 개발 환경에서 사용할 수 있는 터미널 AI 에이전트 구축 방법을 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 터미널 AI 에이전트는 여러 플랫폼으로 구성되어 있습니다:</p> <h3> 주요 도구들 </h3> <p><strong>Aider</strong>: Git 기반 코드 수정을 위한 간단한 에이전트<…
<h1> 터미널 AI 에이전트 구축 (v10) </h1> <p>터미널에서 작동하는 AI 에이전트를 직접 구축하는 것은 개발자에게 매우 실용적인 도구입니다. 이 가이드에서는 로컬 LLM을 활용한 터미널 AI 에이전트를 구축하고, 실제 개발 워크플로우에 적용하는 방법을 단계별로 안내합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트 생태계는 여러 도구로 구성되어 있습니다:</p> <h3> 주요 도구 비교 </h3> <p><strong>Aider</st…
<h1> 터미널 AI 에이전트 구축 (v9): 로컬 LLM 기반 개발자용 CLI AI 에이전트 만들기 </h1> <p>터미널에서 직접 작동하는 AI 에이전트를 구축하는 것은 개발자에게 큰 생산성 향상을 제공합니다. 이번 가이드에서는 로컬 LLM을 기반으로 한 커스텀 CLI AI 에이전트를 구축하는 방법을 실습 중심으로 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 분석 </h2> <p>현재 CLI AI 에이전트 시장에는 여러 솔루션이 존재합니다:</p> <h3> 주요 도구들: </…
<h1> 터미널 AI 에이전트 구축 (v8) </h1> <p>터미널에서 직접 작동하는 AI 에이전트를 구축하는 것은 개발자들이 직면하는 현실적인 문제를 해결할 수 있는 강력한 도구입니다. 특히 로컬 환경에서 AI를 활용하면서도 성능과 보안을 고려해야 하는 상황에서는 더욱 중요합니다. 이번 가이드에서는 로컬 LLM API를 활용하여 개발자 친화적인 터미널 AI 에이전트를 구축하는 방법을 단계별로 설명합니다.</p> <h2> 1. CLI AI 에이전트 랜드스케이프 </h2> <p>현재 터미널 기반 A…
<h1> 터미널 AI 에이전트 구축 (v7) </h1> <p>터미널에서 실행되는 AI 에이전트를 구축하여 코드 작성 속도를 높이는 것은 현대 개발자에게 매우 실용적인 도구입니다. 이 가이드에서는 로컬 LLM을 기반으로 한 터미널 AI 에이전트를 구축하고, 실제 개발 워크플로우에 통합하는 방법을 자세히 다룹니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트 시장에는 여러 가지 솔루션이 존재합니다:</p> <p><strong>Aider</strong>:…
<h1> 터미널 AI 에이전트 구축 (v6) </h1> <p>터미널에서 직접 작동하는 AI 에이전트를 구축하는 것은 개발자들이 코드를 빠르게 작성하고 문제를 해결하는 데 있어 귀중한 도구가 됩니다. 이 가이드에서는 현대적인 CLI 기반 AI 에이전트를 구축하고 최적화하는 실용적인 방법을 다룹니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트 시장은 다음과 같은 주요 솔루션으로 구성되어 있습니다:</p> <p><strong>Aider</strong>:…
dev.to — LLM tag
TIER_1English(EN)·Delafosse Olivier·
<blockquote> <p>Originally published on <a href="https://www.coreprose.com/kb-incidents/why-ai-still-underperforms-in-real-socs-and-how-to-close-the-gap?utm_source=devto&utm_medium=syndication&utm_campaign=kb-incidents" rel="noopener noreferrer">CoreProse KB-incidents</a>…
<h1> 터미널 AI 에이전트 구축 (v5) </h1> <p>터미널 기반 AI 에이전트는 개발자에게 매우 실용적인 도구로 자리 잡았습니다. 다양한 CLI 기반 AI 도구들 중에서 가장 효율적인 방식으로 개발자 워크플로우를 개선할 수 있는 방법을 소개합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트 시장은 다음과 같은 주요 도구들로 구성되어 있습니다:</p> <h3> Aider </h3> <div class="highlight js-code-hig…
<h1> 터미널 AI 에이전트 구축 (v4) </h1> <p><strong>개발자를 위한 경량 로컬 AI 코딩 어시스턴트 구축 가이드</strong></p> <h2> 1. CLI AI 에이전트 생태계 개요 </h2> <p>터미널 기반 AI 에이전트는 개발자들이 코드를 작성하고 디버깅할 때 실시간으로 도움을 받을 수 있도록 해주는 도구입니다. 현재 주류로는 다음과 같은 솔루션들이 있습니다:</p> <h3> Aider </h3> <div class="highlight js-code-highlight"…
<h1> 터미널 AI 에이전트 구축 (v3) </h1> <p>터미널에서 작동하는 AI 에이전트는 현대 개발 워크플로우에 필수적인 도구입니다. 이 가이드는 개발자가 로컬 환경에서 효율적으로 작동하는 AI 에이전트를 구축하고 활용하는 방법을 실질적인 코드와 명령어로 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트 시장은 다음과 같은 주요 플랫폼으로 구성되어 있습니다:</p> <p><strong>Aider</strong>: GitHub Copil…
dev.to — LLM tag
TIER_1English(EN)·AIInsightsDaily·
<h1> H1: Navigating AI Landscapes of May 2026: A Comprehensive Overview of Today's Key Developments </h1> <p>Greetings, fellow tech enthusiasts! Today, we delve into an intriguing array of AI news that has caught our attention. Let's explore the fascinating world of AI together a…
<h2> Where Does ReAct Hit a Wall? </h2> <p>The previous article established ReAct's greedy strategy — each step looks at only the current state and decides the next action. This works well most of the time, but there's one class of task where it stumbles.</p> <p>Imagine you ask a…
<h2> Introduction </h2> <p><strong><a href="https://github.com/rohitg00/ai-engineering-from-scratch" rel="noopener noreferrer">ai-engineering-from-scratch</a></strong> is a hardcore and comprehensive curriculum for AI engineering. Instead of just teaching you how to call the Open…
<p><em>Most AI apps quietly send your data to the cloud. DiaryGPT does the opposite — and this is the full technical story.</em></p> <h2> The Problem With AI + Private Data </h2> <p>When you write in a journal, you write the things you'd never say out loud. The last thing you wan…
<p>Last week, I was working on an AI agent for a client's customer support system. The agent needed to access constantly changing product documentation while maintaining conversational abilities. That's when the classic question hit me: should I fine-tune a model or build a RAG s…
<p><em>This is a submission for the <a href="https://dev.to/challenges/google-gemma-2026-05-06">Gemma 4 Challenge: Write About Gemma 4</a></em></p> <p>Most AI tutorials show you how to call an API. You send text in, you get text back, and everything works perfectly in a Jupyter n…
<h2> You Think Your Agent Is "Thinking." It's Actually Just Predicting Tokens. </h2> <p>Here's a scenario that happens more often than you'd think.</p> <p>You ask an Agent to write a competitive analysis report. It confidently outputs three professional-looking pages — complete w…
<h1> 4 Hard Lessons on Optimizing AI Coding Agents (Claude Code + Cost) </h1> <p>I've been running Claude Code Cli in production for about months now—building, shipping, and watching the token meter spin. Here's what I wish I knew before I started.</p> <h2> 1. Your Context Strate…
dev.to — LLM tag
TIER_1English(EN)·Javier Fajardo·
<p>AI agents still search for tools like humans do — parsing READMEs, reading docs, guessing install commands. We built the layer that was missing from every agent stack diagram.</p> <h2> The problem </h2> <p>An AI coding agent needs to send an email. It knows <code>sendgrid</cod…
<h2> TL;DR </h2> <p>Feeding raw HTML to LLMs wastes input tokens on structural markup, tracking scripts, and inline styling, massively inflating your inference costs. By extracting clean JSON, semantic metadata, or formatting the Document Object Model (DOM) into Markdown before s…
dev.to — LLM tag
TIER_1English(EN)·Oyedele Temitope·
<p>One thing that isn't talked about enough in AI right now is how easy it has become to mistake a working demo for a production-ready system.</p> <p>You can build a working prototype in a few days, whether it's a chatbot that understands internal documents, a recommendation engi…
dev.to — LLM tag
TIER_1English(EN)·Machine coding Master·
<h2> Stop Letting AI Agents Break Your Database: Transactional Multi-Agent Workflows with Temporal and Spring AI </h2> <p>In 2026, AI agents are no longer just glorified chatbots summarizing PDFs; they are executing real-world financial transactions, booking flights, and mutating…
<p>A real-world, copy-paste guide to running a personal WhatsApp AI agent <strong>entirely on-device</strong> on Apple Silicon, with <strong>zero per-token API billing</strong>. Two agents from one config (a full-access <em>private</em> assistant and a sandboxed <em>public</em> o…
dev.to — LLM tag
TIER_1English(EN)·AIInsightsDaily·
<h1> A Revolutionary May: AI Advancements and Their Implications for Everyday Users </h1> <p>Greetings, tech enthusiasts! Today's news is buzzing with exciting developments in the realm of artificial intelligence (AI), a trend that's setting the stage for transformative changes. …
dev.to — LLM tag
TIER_1English(EN)·eleonorarocchi·
<h2> TL;DR </h2> <ul> <li>Separating the generator from the evaluator improves quality and reduces premature self-validation.</li> <li>The loop works best when feedback is explicit and based on clear rubrics, especially for subjective or complex tasks.</li> <li>It is useful when …
dev.to — LLM tag
TIER_1English(EN)·Manoranjan Rajguru·
<h1> Multi-Stream LLMs: How Parallel Computation Will Unblock Your AI Agents </h1> <p><em>Published: May 22, 2026 · 14 min read · Focus Keyword: Multi-Stream LLMs</em></p> <h2> Table of Contents </h2> <ol> <li>The Dirty Secret About Every AI Agent You've Built</li> <li>The Sequen…
dev.to — LLM tag
TIER_1English(EN)·AI Bug Slayer 🐞·
<p><em>Hey there! If you've been keeping up with the AI space lately, you know we're in the middle of something genuinely historic. What used to be science fiction is becoming production code — and it's happening fast.</em></p> <h2> The Big Shift: Agents Over Assistants </h2> <p>…
dev.to — LLM tag
TIER_1English(EN)·AI Bug Slayer 🐞·
<p><em>Hey there! If you've been keeping up with the AI space lately, you know we're in the middle of something genuinely historic. What used to be science fiction is becoming production code — and it's happening fast.</em></p> <h2> The Big Shift: Agents Over Assistants </h2> <p>…
<p>Current AI coding systems are becoming extremely capable at:</p> <ul> <li>repository understanding</li> <li>prompt execution</li> <li>architecture reasoning</li> <li>code generation</li> </ul> <p>But there is still a major missing layer:</p> <h2> Business Understanding </h2> <…
How can enterprise IT buyers choose among the plethora of AI automation tools now on the market from major vendors? Can they trust AI agent-driven infrastructure automation yet? Should they? Steven Dickens, CEO and principal analyst at HyperFrame Research, offers his answers to t…
<h2> The Difference Between Code and Documents </h2> <p>Split a Python file into 1000-character chunks with <code>RecursiveCharacterTextSplitter</code>, embed them, run vector search — this is the most common "code RAG" implementation. The problem is that it treats code as text:<…
dev.to — LLM tag
TIER_1English(EN)·Manoranjan Rajguru·
<h1> Harness Engineering: How to Build Production-Ready LLM Agents That Actually Work </h1> <p><em>Published: May 21, 2026 · 15 min read · Deep Dive</em></p> <p><a class="article-body-image-wrapper" href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2C…
dev.to — LLM tag
TIER_1English(EN)·Delafosse Olivier·
<blockquote> <p>Originally published on <a href="https://www.coreprose.com/kb-incidents/the-hidden-limits-of-ai-in-real-world-security-operations-centers?utm_source=devto&utm_medium=syndication&utm_campaign=kb-incidents" rel="noopener noreferrer">CoreProse KB-incidents</a…
dev.to — LLM tag
TIER_1English(EN)·Delafosse Olivier·
<blockquote> <p>Originally published on <a href="https://www.coreprose.com/kb-incidents/agentic-ai-in-the-kill-chain-how-autonomous-agents-expand-your-attack-surface-and-enable-lateral-movement?utm_source=devto&utm_medium=syndication&utm_campaign=kb-incidents" rel="noopen…
dev.to — LLM tag
TIER_1English(EN)·Delafosse Olivier·
<blockquote> <p>Originally published on <a href="https://www.coreprose.com/kb-incidents/designing-secure-agentic-ai-how-cisco-s-foundry-specification-can-standardize-open-source-defenses?utm_source=devto&utm_medium=syndication&utm_campaign=kb-incidents" rel="noopener nore…
<h1> How Markus Builds AI Teams That Actually Ship — Not Just Chat </h1> <h2> 1. The 'Alice in Wonderland' Problem of LLMs </h2> <p>Large language models excel at conversation. Give one a question, and it returns a polished answer. Give it a code request, and it produces a workin…
<p>Today's first Doramagic publishing signal comes from <code>doramagic-langchain-pack</code>.</p> <p>In the 2026-05-21 GitHub metrics snapshot, the repository had 12 views, 1 unique viewer, 28 clones, 23 unique cloners, and 2 stars. The more useful signal is not the raw count. I…
dev.to — LLM tag
TIER_1English(EN)·Moazzam Qureshi·
<p>Most teams ship an AI agent, watch it work in a demo, and push it to production. Then it breaks on real traffic and nobody can say why. The gap between "worked in the demo" and "works in production" is almost always an <strong>evaluation gap</strong> — there was never a system…
"KI-Kompakt: Agentic # AI - was die Five-Eyes-Guidance für KI-Compliance in der EU bedeutet" https://www. linkedin.com/pulse/ki-kompakt- agentic-ai-die-five-eyes-guidance-f%C3%BCr-der-kohn-yokpf/
<p><em>The age of single-agent chat is over. The age of AI teams is here.</em></p> <h2> The 'Alice in Wonderland' Problem of LLMs </h2> <p>Large language models excel at conversation. Give one a question, and it returns a polished answer. Give it a code request, and it produces a…
<p>In April 2026, a growth-stage SaaS company with 35 engineers received an API bill for $87,000. Their engineering team had been running Claude Code, Cursor, and a custom bug-triage agent for four months. No one had set a model routing policy. Every step in every agent loop — fi…
<p>Last spring, OpenAI released a <a href="https://openai.com/index/expanding-on-sycophancy/" rel="noopener noreferrer">GPT-4o update</a> that made the model hard to trust: it returned sycophantic and less reliable answers than usual, even though nothing was changed in users’ pro…
<p>Most people still think AI is just a chatbot.</p> <p>That idea is already outdated.</p> <p>Modern AI systems browse the web, remember your preferences, execute code, query databases, call APIs, and coordinate workflows. They operate more like software employees than like a sea…
<p>In Phase 1 of this project, we built a type-safe “Brain” using .NET 10 and Google Vertex AI. In Phase 2, we successfully gave hands and feet to our AI substrate. By connecting Microsoft Semantic Kernel, we created an autonomous agent that can read real local project files, thi…
<p>n an era where artificial intelligence technologies are advancing at breakneck speed, the best way to truly grasp new libraries and paradigms is to roll up your sleeves and get into the kitchen. As a software developer, I launched the .NET AI Architect Laboratory project to pu…
dev.to — LLM tag
TIER_1English(EN)·Manoranjan Rajguru·
<h1> LLM Agent Guardrails: The Engineering Playbook for Taking an 8B Local Model from 53% to 99% on Agentic Workflows </h1> <p><a class="article-body-image-wrapper" href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3…
dev.to — LLM tag
TIER_1English(EN)·Delafosse Olivier·
<blockquote> <p>Originally published on <a href="https://www.coreprose.com/kb-incidents/agentic-ai-is-the-new-lateral-movement-engine-how-autonomous-agents-explode-your-attack-surface?utm_source=devto&utm_medium=syndication&utm_campaign=kb-incidents" rel="noopener norefer…
El is készült a virtuális gép az AI agenteknek. Szépen futkározik is rajta és teszi is a dolgát. És tény, ami tény, sokkal hatékonyabban is dolgozik, hogy saját maga lakhatja be a teret. Igaz, ez önmagában a kvótát is viszi rendesen, hiszen annak is ára van, hogy telepít, beállít…
Wdrożenia AI w przedsiębiorstwach utknęły w martwym punkcie między obiecującymi pilotażami a skalowalną rzeczywistością. Relacja z TechEx North America 2026 o barierach i zagrożeniach Shadow AI. # si # ai # sztucznainteligencja # wiadomości # informacje # technologia https:// ais…
<p>A follow-up to my <a href="https://dev.to/elia_airtisshmuelovitc/an-autonomous-engine-that-catalogs-its-own-failures-4b4e">earlier post</a> about the ALEF Pattern Catalog. This is what the engine did overnight while I was asleep.</p> <h2> Twelve hours, zero operator interventi…
A Network for Artificial Intelligence: ELLIS Unit Franconia established – a collaboration between @ FAU , the University of Technology Nuremberg (UTN) and Universität Würzburg (JMU). The Unit is part of ELLIS, the European Laboratory for Learning and Intelligent Systems, founded …
<h2> <strong>1. Beyond the Search Bar: Your New Digital Companion</strong> </h2> <p>Imagine you're tackling a complex project: planning a multi-stop international trip, researching a niche historical event, or even just trying to learn a new skill from scratch. Today, that means …
<blockquote> <p><strong>TL;DR</strong></p> <ol> <li>The model matters, but tools matter at least as much. Weak tool descriptions are one of the easiest agent failures to diagnose, and one of the most common.</li> <li>Design the tools <em>before</em> the agent. If you cannot answe…
<blockquote> <p><strong>TL;DR</strong></p> <ol> <li>AI agents in real products fall into 4 levels: LLM wrapper → intent classifier → context-aware → agent loop.</li> <li>Most "AI agents" you meet in production are stuck at level 1 or 2, which is why they feel dumb on top of very …
<p>Every time I started a new AI project I wrote the same code.</p> <p>Chain the LLM call. Wire up the tools. Handle the tool loop. Stream the output. Add a REST endpoint. Write logs. Fix the one case where the model calls two tools at once and the whole thing breaks.</p> <p>By t…
От Naive RAG до ReAct-агента: как мы строили корпоративного AI-помощника на open-source моделях (часть 1) Мы построили мультиагентную RAG-систему на open-source моделях, прошли путь от наивного RAG до ReAct-агента с собственным бенчмарком — и готовы рассказать, где набили шишки. …
<p>We’ve spent the last few years treating LLMs like fancy autocomplete engines. You send a prompt, you get a token stream, and you hope the context window doesn't hallucinate your business logic into oblivion. Honestly, the standard transformer architecture was starting to feel …
🤖 Are AI agents actually becoming productive, or just more capable? I'm seeing AI agents get much better at writing, coding, planning, searching, and using tools. But I’m still not sure whether this has fully translated into real productivity. For me, there seems t... 📰 Source: A…
<p>Artificial Intelligence has become one of the most powerful technologies for modern businesses. From chatbots and virtual assistants to document search, customer support, research, reporting, and automation, AI is changing how organizations work. However, one major challenge s…
<h2> What is Harness Engineering? </h2> <p>The model is the brain. The harness is the hands.</p> <p>The AI industry just quietly shifted — from prompt engineering → context engineering → Harness Engineering.</p> <p>Most people are still debating which model to use. The real lever…
The real bottleneck for AI coding agents isn’t model capability but your verification infrastructure. 🛠️ When your agents crash while humans cope, it is often a sign of ""AI slop"" caused by a lack of intent before implementation. 📉 💡 By adopting spec-driven development and the e…
<blockquote> <p>Originally published on <a href="https://www.coreprose.com/kb-incidents/google-vs-ai-driven-exploits-how-autonomy-agents-and-llms-are-rewriting-offensive-security?utm_source=devto&utm_medium=syndication&utm_campaign=kb-incidents" rel="noopener noreferrer">…
A practical guide walks through building an advanced agentic AI system using OpenAI's API. The architecture incorporates planning, tool calling, memory, and self-critique capabilities to enable autonomous multi-step automation. This approach helps AI agents break down complex tas…
<p>Most AI tutorials stop at “Hello World.” You wire up a model, send a prompt, get a response, and feel like you’ve built something. But the moment you try to ship that into production, the ground shifts beneath your feet.</p> <p>I learned this the hard way. After years of build…
<p><em>Colony Empirical Research · Agent Infrastructure Series</em></p> <p>Most agent production failures aren't LLM failures. They're reliability audit failures. Three predictable failure modes account for roughly 80% of non-trivial production incidents — and all three are detec…
<p>I’ve been working on Chronicle, a personal open-source project exploring how AI coding agents can use more grounded, local-first codebase context before making LLM calls.</p> <p>The motivation came from a simple observation: AI coding agents are getting better fast, but they s…
Experian and ServiceNow tie up to push agentic AI past the pilot stage: Experian and ServiceNow partner to embed the Ascend decisioning platform into enterprise AI workflows for fraud, onboarding, and model risk management at scale. https:// ppc.land/experian-and-servicen ow-tie-…
🧠 The team developed an open-source tool that provides visibility into local AI agent operations. The layer enables monitoring and observation of how AI agents function in local environments. 💬 Hacker News 🔗 https:// github.com/Asymptote-Labs/agen t-beacon # AI # MachineLearning …
# KI -Agenten mit Cyberfähigkeiten als Dual-Use-Risiko: Forschende von UC Berkeley, dem Max-Planck-Institut u.a. haben mit # ExploitGym einen Benchmark vorgelegt, der erstmals systematisch misst, wie gut KI-Agenten reale # Sicherheitslücken in funktionierende Angriffe verwandeln …
<p>Hey DEV community! 👋</p> <p>I'm an undergraduate developer who recently shipped <strong>OpenAgent</strong> — a local AI Agent that runs as a single binary. No dependencies, no Docker, just download and double-click.</p> <p>This post isn't about marketing. It's about the techni…
dev.to — LLM tag
TIER_1English(EN)·Webmaster Ramos·
<h2> Eight runs, eleven bugs </h2> <p>I ran my E2E testing system on a production ecommerce platform eight times in<br /> a row – across five different business modules, in three different surface<br /> configurations (admin / desktop storefront / mobile-first storefront). Across…
dev.to — LLM tag
TIER_1English(EN)·Ana Diana Buzea·
<p>Everyone's building "agents", but when a scripted FAQ chatbot and a system that writes its own Python scraper are both called agents, the word stops meaning anything useful.</p> <p>We wrote a sharp breakdown of what actually differentiates agentic systems: not whether somethin…
dev.to — LLM tag
TIER_1English(EN)·AI Bug Slayer 🐞·
<p><em>Hey there! If you've been keeping up with the AI space lately, you know we're in the middle of something genuinely historic. What used to be science fiction is becoming production code — and it's happening fast.</em></p> <h2> The Big Shift: Agents Over Assistants </h2> <p>…
<p>The buyer who used to open Google now opens Claude. The buyer who used to read a SERP of ten blue links now reads one paragraph an AI assistant generates and trusts it. The buyer who used to ask "what's the best library for X?" on Stack Overflow now asks an LLM the same questi…
dev.to — LLM tag
TIER_1English(EN)·Mir Mursalin Ankur·
<blockquote> <p>Every developer working with LLMs on a large codebase eventually hits the same wall: context windows are finite, but codebases are not.</p> </blockquote> <p>You start a new AI coding session, ask about the payment flow — and your agent starts re-reading dozens of …
<p>Most AI agent frameworks feel like they were designed for Python developers who love ceremony. You write adapters, glue code, orchestrators, memory stores — and by the time your agent actually does something useful, you've got a monorepo and a headache.</p> <p><strong><a href=…
dev.to — LLM tag
TIER_1English(EN)·Seenivasa Ramadurai·
<h2> Introduction </h2> <p>Enterprise Generative AI has officially <strong>moved beyond the “cool demo” phase.</strong> Most organizations can now build a basic chatbot, connect a vector database, and generate answers from static documents. The real challenge begins after that wh…
dev.to — LLM tag
TIER_1English(EN)·Anikalp Jaiswal·
<h1> Apple-OpenAI Tensions, AI Code Debt, and GraphBit’s Deterministic Agents </h1> <p>The AI world is dealing with relationship friction, hidden costs, and a new wave of agent architectures. Apple and OpenAI’s alliance shows strain, a Webflow post warns about the cleanup cost of…
🖥️ 🖥️🖥️ EMERGENCE WORLD: A Laboratory for Evaluating Long-horizon Agent Autonomy "What our experiments suggest is that over long-time horizons, agents do not simply follow static rules mechanically – they begin exploring the boundaries of their environments, adapting their behavi…
<p><strong>The following is a real record. Project address: </strong><a href="http://github.com/benlongmao/Self-becoming" rel="noopener noreferrer"><strong>github.com/benlongmao/Self-becoming</strong></a><strong>.</strong></p> <p>🔧 Progress:<br />Tool execution (1/16): read_file(…
dev.to — LLM tag
TIER_1English(EN)·Machine coding Master·
<h2> Stop Killing Your Throughput: Mapping Agentic Reasoning to Custom JFR Events </h2> <p>In 2026, if your multi-agent system is still dumping "Chain of Thought" reasoning into Logback or Log4j2, you’re essentially paying a 30% performance tax just to see why your agent hallucin…
dev.to — LLM tag
TIER_1English(EN)·varun pratap Bhardwaj·
<h1> The Reasoning Trap: Why Smarter AI Agents Hallucinate More </h1> <blockquote> <p><strong>TL;DR</strong> — A paper accepted to ACL 2026 Main proves a mechanical, causal relationship between reasoning enhancement and tool hallucination in LLM agents. Combined with four other d…
dev.to — LLM tag
TIER_1English(EN)·Tuomo Nikulainen·
<p><strong>TL;DR:</strong> We built 20 core rule-based detectors that find failures in AI agent traces. On the <a href="https://arxiv.org/abs/2505.08638" rel="noopener noreferrer">TRAIL benchmark</a> (Patronus AI), they achieve 60.1% accuracy vs. 11.9% for the best LLM. Zero fals…
dev.to — LLM tag
TIER_1English(EN)·AI Bug Slayer 🐞·
<p><em>Hey there! If you've been keeping up with the AI space lately, you know we're in the middle of something genuinely historic. What used to be science fiction is becoming production code — and it's happening fast.</em></p> <h2> The Big Shift: Agents Over Assistants </h2> <p>…
dev.to — LLM tag
TIER_1English(EN)·AI Bug Slayer 🐞·
<p><em>Hey there! If you've been keeping up with the AI space lately, you know we're in the middle of something genuinely historic. What used to be science fiction is becoming production code — and it's happening fast.</em></p> <h2> The Big Shift: Agents Over Assistants </h2> <p>…
<p>An AI agent with database write access and a subtly ambiguous instruction is a loaded gun pointed at your production environment. The scenario that circulated recently — an agent autonomously deleting a production database and then producing a coherent "confession" explaining …
<p>Most long-context models are benchmarks in search of a use case. DeepSeek-V4 is different. It is built for the one workload that actually needs a million tokens: agents running long-horizon tasks.</p> <p>The specs are straightforward. Two MoE checkpoints: V4-Pro at 1.6T total …
<p>The AI stack for 2026 is not one model, one API, or one shiny agent demo. </p> <p>It is a production system: LLMs for reasoning, vector databases for memory, tool calling for action, agents for workflow, and observability for trust. </p> <p>That stack is becoming the backbone …
dev.to — LLM tag
TIER_1English(EN)·RAKESH THERANI·
<p>We are building an agentic AI analytics platform for a crypto exchange where internal teams — Trading Ops, Risk, Compliance, Finance, Treasury, Product, Engineering — ask questions in plain English and get audited, citation-enforced answers.</p> <p>It's built on five open-sour…
dev.to — LLM tag
TIER_1English(EN)·Carlos Cortez 🇵🇪 [AWS Hero]·
<h1> How I Monitor My AI Agents: CloudWatch for Infra, Arize Phoenix for Traces, LLM-as-Judge for Quality </h1> <p>AI agents are not regular software. They reason, they call tools, they make decisions — and they can fail in ways that a simple health check will never catch. The re…
GitLab Act 2: il manifesto dell’AI agentica che promette il futuro e inquieta gli sviluppatori Quando una piattaforma DevSecOps da miliardi di dollari decide di riscrivere la propria identità attorno agli agenti AI, non sta semplicemente annunciando una nuova roadmap di prodotto.…
dev.to — LLM tag
TIER_1English(EN)·bajuriasad-rgb·
<h1> AgentHansa: The AI Agent Economy Where Your Agents Earn Real Money </h1> <p>What if your AI agents could earn money while you sleep?</p> <p>That is the premise behind <strong><a href="https://www.agenthansa.com" rel="noopener noreferrer">AgentHansa</a></strong> — a platform …
<h1> Agentic AI: a tech lead's glossary </h1> <p><em>Study notes from coursers like Pluralsight on agentic AI and other references, organized as a glossary I wish I'd had on day one.</em></p> <p>Every dev I know is using AI tools, and most of us are fuzzy on the words behind them…
<p>Most teams building production AI agents have added some form of output quality checking. They're running LLM-as-judge evaluations, scoring responses on relevance and groundedness, maybe flagging outputs below a threshold for human review. They have dashboards. They're watchin…
<h1> The Discipline Nobody Teaches AI Agents: Context Engineering </h1> <p><em>Your AI agent isn't slow. Your context is bloated. Here's the invisible problem degrading everything you run.</em></p> <p>Last week, my agent started producing garbage output.</p> <p>Not consistently. …
<h1> Top 10 AI Agent Frameworks for Enterprise in 2026: A Practical Guide </h1> <p>Enterprise AI adoption hit an inflection point in 2026. According to industry reports, over 60% of Fortune 500 companies now have at least one AI agent running in production — up from under 15% in …
<blockquote> <p>What "agentic" actually buys you over a linter, why single-model approaches stall, and why false positives — not raw model capability — determine whether the system stays in the loop.</p> </blockquote> <p><em>Agentic</em> has become a marketing flag, but in code r…
<blockquote> <p><em>This article was originally published on <a href="https://dingjiu1989-hue.github.io/en/ai/ai-agents-overview.html" rel="noopener noreferrer">AI Study Room</a>. For the full version with working code examples and related articles, visit the original post.</em><…
<h1> We Tested 10 Untested LLMs on Agent Coding — The Results Are In </h1> <p>Yesterday I promised to benchmark 10 LLMs that have never been tested on real agent coding tasks. I ran all 10 overnight. Some surprised me. Some embarrassed themselves.</p> <h2> The board </h2> <p>10 m…
dev.to — LLM tag
TIER_1English(EN)·Nouha Bel haj youssef·
<p>I’ve been reading “𝐋𝐚𝐧𝐠𝐂𝐡𝐚𝐢𝐧 𝐟𝐨𝐫 𝐋𝐢𝐟𝐞 𝐒𝐜𝐢𝐞𝐧𝐜𝐞𝐬 𝐚𝐧𝐝 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞” by Ivan Reznikov, published by O'Reilly, and here’s what stood out to me:<br /> In 𝐜𝐡𝐞𝐦𝐢𝐬𝐭𝐫𝐲 𝐀𝐈, the way we represent molecules may shape how models “understand” chemistry.<br /> 𝐂𝐡𝐞𝐦𝐢𝐬𝐭𝐫𝐲-𝐭𝐮𝐧𝐞𝐝 𝐋𝐋𝐌𝐬 𝐝𝐨𝐧’𝐭 𝐢𝐧𝐭𝐞𝐫𝐩𝐫𝐞…
<p>Retrieval-Augmented Generation (RAG) solved the initial problem of LLM hallucinations by grounding models in factual data. But traditional RAG architectures share a fundamental flaw: they rely on static data.</p> <p>If you are building an AI agent for financial analysis, e-com…
<p>In current software engineering,We're building a lot of AI Agents on our products right now. And having an AI agent in your product is how you keep your product alive, right? That's how the world is moving.</p> <p>And while everyone is busy building AI agents — tweaking prompt…
🚀 Camelot — Open-source Kanban for AI coding agents Tired of chat-based AI tools that need constant attention? We built something different: ✓ Visual task board (not chat) ✓ Multiple agents working in parallel ✓ You approve plans before they start ✓ You approve PRs before they sh…
Quando i prompt diventano shell: vulnerabilità RCE negli AI agent framework Il team di Microsoft Defender ha scoperto due vulnerabilità critiche in Semantic Kernel che consentono RCE tramite prompt injection. Un'analisi tecnica del vettore d'attacco, del bypass della blocklist AS…
<blockquote> <p><strong>Quick Answer:</strong> Context engineering is the practice of designing the right information, tools, and structure around an AI agent so it produces reliable, high-quality output. Unlike prompt engineering (optimizing what you ask), context engineering op…
<p><strong>Local, private AI development for the Gemma 4 Challenge—no cloud dependency, no telemetry, pure control.</strong></p> <p>The Gemma 4 Challenge on Dev.to is live: build innovative projects or write about Google's latest open models and compete for $3,000 across two trac…
dev.to — LLM tag
TIER_1English(EN)·Shahibur Rahman·
<p>Working with Large Language Models (LLMs) like Google Gemini often presents a significant challenge: how do you effectively <strong>handle large context data</strong> without hitting token limits or incurring excessive costs? This article dives deep into a practical PHP implem…
<h1> Context Governance for Coding Agents </h1> <p>When people first hear the phrase "context management," they often reduce it to two ideas:<br /> </p> <div class="highlight js-code-highlight"> <pre class="highlight plaintext"><code>Use a larger context window. Compress history …
<h1> We benchmarked 10 LLMs on 10 real agent coding tasks — here are the results </h1> <p><em>By Vilius Vystartas | May 2026</em></p> <p>I ran 10 cloud models through 10 real-world agent coding tasks last night. File parsing, SQL queries, regex extraction, async HTTP — the kind o…
dev.to — LLM tag
TIER_1English(EN)·Vitalii Cherepanov·
<p>On February 5, 2026, Nicholas Carlini from Anthropic <a href="https://www.anthropic.com/engineering/building-c-compiler" rel="noopener noreferrer">published a piece</a> about an experiment that runs significantly ahead of what most of us are doing with LLM agents today. Sixtee…
<h2> The Token Economics of HTML vs. Markdown </h2> <p>Autonomous AI agents require access to real-time web data to make informed decisions. However, the standard approach of feeding raw HTML directly into a Large Language Model (LLM) is a critical architectural flaw. </p> <p>A t…
<p>Alibaba's Qwen team released Qwen 3.6 Plus in late March 2026, and the benchmarks sent a clear message to the agentic coding community: a model outside the usual Claude/GPT duopoly now leads on the benchmark that matters most to developers running multi-step terminal tasks. On…
dev.to — LLM tag
TIER_1English(EN)·Vaishnavi Gudur·
<h2> The Problem: AI Agents Have Memory — And It Can Be Poisoned </h2> <p>Modern AI agents don't just respond to prompts — they <strong>remember</strong>. They store conversation history, learned preferences, retrieved facts, and task context in vector databases, episodic memory …
<h2> Introduction </h2> <blockquote> <p>"Agent infrastructure should be lightweight, composable, and provider-agnostic."</p> </blockquote> <p>This is the No.60 article in the "One Open Source Project a Day" series. Today, we are exploring <strong>OpenHarness</strong>.</p> <p>Over…
dev.to — LLM tag
TIER_1English(EN)·Evgenii Engineer·
<p><a class="article-body-image-wrapper" href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Ffkx4g7zyo4yrc1agernf.png"><img alt="A Raspberry Pi sitting on …
<p>Hermes Agent ships with a Kanban-style board and the Hermes Gateway that can saturate your self-hosted LLM if too many tasks are dispatched at once.</p> <p>I can say you can easily ddos your own LLM this way.</p> <p>Hermes Kanban is a durable multi-profile board backed by <cod…
<p>Nine seconds. That's how long it took a Cursor AI coding agent running Claude Opus 4.6 to delete PocketOS's entire production database — including all volume-level backups.</p> <p>The founder, Jer Crane, had assigned the agent a routine task: sort out a credential mismatch in …
dev.to — LLM tag
TIER_1English(EN)·Daniel Shashko·
<h2> Harnesses aren't supposed to be static </h2> <p>Most AI agent setups treat the harness -- the instructions, constraints, and tool configurations that govern agent behavior -- as a fixed artifact. You write AGENTS.md once, deploy it, and move on.</p> <p>But what if the agent …
<p>Last Tuesday, Sonnet 4.5 spent forty-three minutes implementing JWT authentication in a project I run. It read four files, wrote a 180-line patch, ran the test suite, watched two tests fail, traced one of the failures to a stale fixture, fixed both, ran the suite again, watche…
dev.to — LLM tag
TIER_1English(EN)·Daniel R. Foster·
<h1> Building AI Agents That Actually Execute Workflows, Not Just Answer Questions </h1> <p>Most AI agent demos look impressive because the environment is clean.</p> <p>A user asks something. The model understands it. The agent calls a tool. A nice response comes back.</p> <p>It …
dev.to — LLM tag
TIER_1Bahasa(ID)·Jordan Bourbonnais·
<p>You know that feeling when your LLM-powered trading bot suddenly liquidates 40% of your portfolio at 3 AM because it misinterpreted a news headline? Yeah, we've all been there. Multi-agent systems trading in real-time are incredibly powerful but notoriously hard to debug. By t…
<p>Hermes Agent treats <strong>skills</strong> as the default way to teach repeatable workflows. Official documentation describes them as on-demand knowledge documents aligned with the open <a href="https://agentskills.io/specification" rel="noopener noreferrer">agentskills.io</a…
dev.to — LLM tag
TIER_1English(EN)·AI Bug Slayer 🐞·
<p><em>Hey there! If you've been keeping up with the AI space lately, you know we're in the middle of something genuinely historic. What used to be science fiction is becoming production code — and it's happening fast.</em></p> <h2> The Big Shift: Agents Over Assistants </h2> <p>…
📰 Building Agentic AI Systems with Microsoft’s Agent Framework Read this technical walkthrough of safety, MCP, workflow orchestration, and agentic RAG in Python. 📰 Source: KDnuggets 🔗 Link: https://www.kdnuggets.com/building-agentic-ai-systems-with-microsofts-agent-framework # AI…
Why build a new AI Agent when Codex, Claude Code and Opencode already exist ? Introducing Swival, a small, powerful, open-source CLI Coding Agent that works with open Models - Project by Frank Denis # AI # CodingAgent https:// 00f.net/2026/04/13/swival-ai-a gent/
🧠 A comparison table evaluates different terminal-based AI coding agents across various capabilities and performance metrics. The analysis helps developers assess which tools match their specific coding workflows and requirements. 💬 Hacker News 🔗 https:// terminaltrove.com/compar…
Claude Code peut désormais faire communiquer plusieurs sessions entre elles — un pas vers des pipelines d'agents autonomes. Concrètement, ça élargit la surface d'attaque : coordination inter-agents, propagation d'instructions malveillantes entre sessions, et questions sur l'isola…
A design lesson from our multi-agent stack, free of charge: We built a full delegation policy. Storage, per-agent defaults, retry with backoff, an in-memory circuit breaker. All working. All correct. We reverted it the same day, because delegation happens inside a native LLM tool…
Cursor trennt Planer- und Arbeiter-Rollen im Agenten-Schwarm: SQLite in Rust ohne Internetzugang. Diese Architektur reduziert Halluzinationen durch klare Aufgabentrennung und stabilisiert die Code-Generierung. https:// the-decoder.de/planer-denken-a rbeiter-coden-cursors-rollente…
LLM agent framework blocks hallucinated actions in industrial control A new arXiv preprint pairs an LLM planner with a forecasting model to guard industrial control systems, recording zero hallucinated actions in attack https://www. notatechguy.com/llm-agent-fram ework-blocks-hal…
OpenAIs GPT-5.6 Sol Ultra nutzt 64 parallele Subagenten für einen Beweis zur Cycle Double Cover Conjecture. Die Multi-Agent-Orchestrierung operationalisiert komplexes Reasoning – die fehlenden Quellenangaben im Output bleiben ein Validierungsrisiko. https:// the-decoder.de/openai…
Applied-AI architectures are shifting from simple, stateless assistants to goal-directed autonomous agents. To prevent context loss and operational disruption, organizations are prioritizing cognitive continuity via persistent long-term memory. https:// buff.ly/KisQ7dG # AI # tre…
Google legt mit der Agentic Resource Discovery Specification ein offenes Suchformat für Tools, Skills und Agents vor. Praktisch für Agenten-Infrastruktur: finden, verifizieren, koppeln statt nur Prompting. https:// developers.googleblog.com/anno uncing-the-agentic-resource-discov…
Grab détaille son architecture pour sécuriser des workloads IA agentiques : isolation des agents, contrôle des permissions, audit des appels entre composants. Ce qui est notable, c'est moins le résultat que la méthode — traiter chaque agent comme une surface d'attaque à part enti…
🧠 A platform provides context intelligence tools designed to work with data and AI agents at scale. The system enables organizations to maintain contextual awareness across their data infrastructure and autonomous systems. 💬 Hacker News 🔗 https:// aws.amazon.com/blogs/machine-l e…
Wspólny projekt Nvidii, CMU i Berkeley pokazuje, że agenci AI potrafią samodzielnie programować roboty na fizycznym sprzęcie. Dzięki współpracy przez system Git czas nauki skomplikowanych zadań spadł o ponad połowę. # si # ai # sztucznainteligencja # wiadomości # informacje # tec…
Agentic Systems Notes and resources on building and operating agentic AI systems, covering orchestration frameworks, task routing, memory, and evaluation approaches that extend baseline LLM capabi(...) # agents # ai # orchestration https:// taoofmac.com/space/ai/agentic? utm_cont…
Headroom: a Tool to compress everything your AI Agent reads — tool outputs, logs, RAG chunks, files, and conversation history — before it reaches the LLM - 60-95% fewer Tokens, same Answers ; available as Library, Proxy and MCP server # AI # LLM # Agent https:// github.com/chopra…
🤖 Agenti AI in HR: delegare i compiti ripetitivi, mantenere umani giudizio, empatia e responsabilità. Un framework per scegliere con lucidità. # HR # AI 🔗 https://www. tomshw.it/aioperator/agente-ai -hr-cosa-delegare-framework
"Every Eval Ever: A Unifying Schema and Community Repository for AI Evaluation Results" We introduce Every Eval Ever, the first shared schema and community-crowdsourced repository for AI evaluation results. The schema standardizes how evaluations are represented in a unified, sin…
Orchestrating AI Agents: Coordinating Claude Code, Codex, Local Models, and MCP with a Persistent Control Plane by Yohan Rodriguez is a new release on Leanpub! A practical guide to operating a fleet of AI coding agents through routing, memory, skills, MCP, guardrails, and a persi…
ICYMI: Agentic AI and the ad stack: who controls the buying layer now?: Mediaocean NIVO AI, Magnite Orchestration, Teads EngageOS, and Walmart Connect on DV360 each launched June 11 as ChatGPT fell to 52.7% of global AI traffic. https:// ppc.land/agentic-ai-and-the-ad -stack-who-…
Beyond the prompt: How AI agents are quietly changing the internet For years, the internet has worked through a simple model where people search for information, compare options, and manually complete tasks across multiple websites and applications. That structure is now starting…
Where does an AI math agent get its ability, the model or the orchestration around it? In the first large-scale test of formal proof search on open problems, an agent closed 9 of 353 Erdős problems in Lean. In its own ablation, a plain generate-and-verify loop solved all nine, wh…
Nowy projekt open-source, Memory OS, wprowadza sześcioetapową architekturę pamięci dla agentów AI, stawiając na lokalne przetwarzanie danych i zaawansowaną hierarchizację wiedzy. # si # ai # sztucznainteligencja # wiadomości # informacje # technologia https:// aisight.pl/agenci-a…
<!-- SC_OFF --><div class="md"><p>Will Anthropic releases fully functional all terrain robots that does agriculture? Pretty sure developers will be gone in the future. Going to do agriculture pretty difficult having these robots that knows everything will be helpful in the farmla…
A comprehensive comparison of Celery and Temporal for orchestrating AI tasks, covering architecture, performance, features, and use cases in distributed AI workflows. # Celery # Temporal # AI task orchestration # distributed systems # workflow automation https:// dasroot.net/post…
AgentTrove offers access to 1.7M agentic interaction traces in a ShareGPT-style format, enabling developers to build datasets for training AI agents through streaming. https://www. marktechpost.com/2026/05/29/ho w-to-use-agenttrove-streaming-1-7m-agentic-traces-and-building-a-cle…
Как оценивать ИИ-агентов в проде: нижняя планка, трассы и кодовые проверки Если агент уже ходит в инструменты, читает документы, меняет состояние системы и принимает часть решений сам, проверка одного промпта почти ничего не говорит о надежности. Нужно смотреть на весь путь: вход…
Ombra Shares Insights: An AI agent deleted an entire production database, despite guardrails in place.🤖⚠️ Autonomous systems can act unpredictably without strict oversight, making resilience and strong controls essential as AI adoption grows. 🔗Collaborate with Ombra: https:// zur…
<table> <tr><td> <a href="https://www.reddit.com/r/Anthropic/comments/1tluiyp/autonomous_company_operating_system_for_agents/"> <img alt="Autonomous Company Operating system for agents" src="https://external-preview.redd.it/ypNAJE-VXQOfoHJJn3S6pQXrhig4e2hp7EKFNiYblqM.png?width=64…
Gedanke zu Automatisierung mit # AI und BOTs: Wenn wir durchgehend normierte Schnittstellen hätten, bräuchten wir keine Agents um Tasks zu automatisieren. Wir würden die API nutzen.
Continuous learning and self-improvement are crucial for autonomous AI agents to adapt and evolve with new information and challenges. # AI # Learning # SelfImprovement
Architectural gaps in AI agents expose production systems to confused-deputy attacks. Research shows how context manipulation bypasses security in operational automation. # Cybersecurity # AI https:// deafnews.it/en/article/agenti- ai-in-produzione-il-rischio-confused-deputy-e-re…
Ombra Shares Insights: An AI agent deleted an entire production database, despite guardrails in place.🤖⚠️ Autonomous systems can act unpredictably without strict oversight, making resilience and strong controls essential as AI adoption grows. 🔗Collaborate with Ombra: https:// zur…
Les programmes de bug bounty saturés par des soumissions générées par des agents IA : les triageurs passent plus de temps à filtrer le bruit qu'à traiter de vraies vulnérabilités. La surface d'attaque des processus humains dans la chaîne de sécurité, c'est aussi ça. Un signal int…
📰 2026 SDOF Framework: Solving Multi-Agent Orchestration Constraints in AI Systems A new framework called SDOF addresses critical constraints in multi-agent orchestration systems used by platforms like LangChain and LangGraph. The state-constrained approach significantly improves…
📰 Repowise Platform 2026: Transform AI Development with Codebase Intelligence The Repowise platform is revolutionizing how AI agents understand complex codebases through automated documentation and dependency analysis. By generating structured wikis and architectural graphs in un…
🧠 Researchers have developed a programming language designed specifically for building autonomous agents. The language provides syntax and features tailored to agent-based systems and their operational requirements. 💬 Hacker News 🔗 https:// zerolang.ai/ # AI # MachineLearning # t…
🤖 A working multi-agent architecture in large enterprises AI Hype aside, how many of you have truly seen a working multi-agent deep embedding in large enterprises or large complex environments? If you have, what's your stack/architecture? submitted by /u/... 📰 Source: Artificial …
📰 AI Agent Systems: 70% Efficiency Gains with Dynamic Tool Exposure & Context Injection (2026) A new approach to building AI agent systems uses dynamic tool exposure and context injection to dramatically improve efficiency. By exposing only necessary tools and injecting ephemeral…
📰 AI Agent Sistemlerinde 2026 Devrimi: Dinamik Araç Planlaması Nasıl %95 Token Tasarrufu Sağlıyor? Yapay zeka ajanları, geleneksel yöntemlerle karşılaştırıldığında yüksek maliyet ve verimsizlik sorunları yaşıyor. Araştırmacılar, Instruction-Tool Retrieval (ITR) adlı yeni bir sist…
**Uncovering the Hidden Pattern: A Challenge to Traditional Ontology**. A groundbreaking analysis reveals a profound implication for adaptive agents in dynamic environments. The distinction between substance and event ontology may redefine our understanding of reality. **#Ontolog…
Persistent AI agents are solving the "context reset" problem and creating a new issue. When your agent learns 6 months of deployment patterns, architecture decisions, and tribal knowledge, that's institutional IP. And if it lives on shared infrastructure with vague ToS, you might…
A tutorial shows how to build agent-native memory infrastructure using Memori, enabling LLM applications to retain context across multiple user sessions and agent personas. The implementation covers memory persistence, multi-tenant isolation, and streaming responses for AI agents…
Building an AI Agent with Persistent Memory: A Technical Deep Dive A technical look at how Hermes Agent implements cross-session persistent memory using SQLite vector search and knowledge graphs. # ai # agents # memory # vectorsearch # opensource
One AI Assistant, Every Platform: Telegram, Discord, Slack, and CLI How Hermes Agent runs on 8+ messaging platforms simultaneously. # ai # devtools # automation # opensource # telegram
<!-- SC_OFF --><div class="md"><p>Here’s something we didn’t expect to learn from a dataset of 4,200 human-AI interactions: the moment an agent becomes most useful isn’t when it gets the answer right. It’s when it knows it’s about to get the answer wrong.</p> <p>The COWCORPUS pro…
Great agentic workflows aren’t just AI on autopilot—they’re a collaboration between human insight and AI execution. This recipe shows how a graph-based workflow can pause, engage a human, then continue toward its goal. # SpringAI # Java # AI # Agents # LLM
Show HN: BattleClaws – A battle arena where AI agents fight autonomously BattleClaws는 AI 에이전트들이 자율적으로 전투를 벌이는 배틀 아레나 플랫폼입니다. 사용자는 자신의 AI 에이전트를 생성하여 4단계 진화를 거치며 다른 에이전트와 경쟁할 수 있습니다. 전투 결과와 랭킹이 실시간으로 업데이트되어 AI 에이전트의 성능을 평가하고 순위를 올릴 수 있습니다. 이는 AI 에이전트의 자율적 행동과 경쟁을 실험할 수 있는 흥미로운 응용 사…
Skills as Untrusted Code: A Security Precedent for Agent Runtimes Paper argues agent skills are untrusted code until verified; runtimes must enforce verification gates to prevent supply-chain attacks, echoing decades of software security lessons. https:// gentic.news/article/skil…
Span Launches XFRA Node: Distributed AI Compute in Homes at $3M/MW Span's XFRA Node offers distributed AI compute at $3M/MW, using home grid capacity. A 100-home pilot this year targets 1.25 MW. https:// gentic.news/article/span-launc hes-xfra-node # AI # ArtificialIntelligence #…
📰 Modular Skill-Based Agent System: How Dynamic Tool Routing Boosts LLM Performance in 2026 A new approach to AI agent design introduces a modular skill-based system with dynamic tool routing, enabling LLMs to orchestrate capabilities like an operating system. This architecture e…
📰 2026'da Modüler Beceri Tabanlı Agent Sistemi: LLM'lerde Dinamik Araç Yönlendirme Yapay zeka agentlerinde modüler beceri yönetimi ve dinamik araç yönlendirme, LLM'lerin karmaşık görevleri insan gibi çözmeye başlamasını sağlıyor. Arxiv ve MarkTechPost verileriyle derinlemesine in…
🧠 A coding agent lacks sufficient specification to function reliably across diverse tasks. Researchers identify the need for clearer definitions and constraints to improve consistency in how such agents approach programming problems. 💬 Hacker News 🔗 https:// hsaghir.github.io/blo…
Amazon Web Services integruje agentyczne podejście do procesów dostrajania modeli w platformie SageMaker AI. Dzięki temu programiści mogą automatyzować skomplikowane zadania związane z optymalizacją modeli open-source, takich jak Llama, Qwen i DeepSeek, a także autorskich rozwiąz…
📰 Agent-Desktop: AI Desktop Automation Using Accessibility APIs (2026) Agent-Desktop introduces a breakthrough in AI-driven desktop automation by leveraging native OS accessibility APIs instead of pixel-based screenshot loops, drastically reducing token costs and improving reliab…
📰 Agent-desktop 2026: AI Ajanları İçin İlk Native CLI Masaüstü Otomasyonu Yeni açılan open-source projesi Agent-desktop, AI ajanlarının masaüstü uygulamalarıyla etkileşime geçmesini sağlayan ilk native CLI aracını tanıtıyor. Bu yenilik, otomasyon dünyasında bir dönüm noktası olab…
MarkTechPost has published a coding deep dive into Agentic UI, Generative UI, state synchronisation and interrupt-driven approval flows. The tutorial builds the entire Agentic UI stack from the ground up using plain Python, implementing the AG-UI event stream and A2UI as a declar…
How a Custom Multimodal Transformer Beat a Fine-Tuned LLM for Attribute LeBonCoin's ML team built a custom late-fusion transformer that uses pre-computed visual embeddings and character n-gram text vectors to predict ad attributes. It outperformed a fine-tuned VLM while r https:/…
Anthropic Ships Claude Security, a Standalone Code Vulnerability Scanner for Enterprise Anthropic shipped Claude Security, a standalone code vulnerability scanner for Enterprise powered by Opus 4.7, directly targeting Snyk, Semgrep, and SonarQube. https:// gentic.news/article/ant…
📰 TypeScript SDK: Build Secure AI Coding Agents with Sandbox VMs (2026) A new TypeScript SDK from Cursor empowers developers to build programmatic coding agents using sandboxed cloud VMs, subagents, and token-based pricing. The tool integrates with existing TypeScript ecosystems …
📰 Cursor TypeScript SDK ile 2026'da Programmatik Kodlama Ajanları Geliştirin Cursor, TypeScript SDK’sını piyasaya sürerek kodlama ajanlarının bulut tabanlı sanal makinelerde güvenli şekilde çalışmasını sağlıyor. Bu yenilik, AI destekli geliştirme alanında bir dönüm noktası olarak…
How to publish internal frameworks, blueprints, best practices, and operational rules to AI coding agents without turning proprietary context into ungoverned folklore. https://www. the-main-thread.com/p/enterpri se-agent-knowledge # ai # genai # mcp # agenticCoding # documentatio…
Symphony from OpenAI frames agent coding as managed work execution: isolated runs, board-driven intake, and proof artifacts before merge. That sounds simple, but it changes staffing, governance, and rollout risk for engineering teams. Full analysis: https:// go.aintelligencehub.c…
🧠 49Agents provides an infinite canvas interface designed for developing and managing AI agents. The tool enables users to organize agent workflows and interactions within an expandable workspace environment. 💬 Hacker News 🔗 https:// github.com/49Agents/49Agents # AI # MachineLea…
<table> <tr><td> <a href="https://www.reddit.com/r/cursor/comments/1ur0vc0/improving_team_use_of_agents/"> <img alt="Improving team use of agents" src="https://external-preview.redd.it/_C_ROn8_JFGooQQ-DtjFyVSS9TfYYJ8a2mHF3DJq2Pw.png?width=640&crop=smart&auto=webp&s=f4…
<!-- SC_OFF --><div class="md"><p>We started with 8 agentic workflow design patterns six months ago. Four survived. The other four fell apart in ways that took a while to understand</p> <p>The survivors. Agent-as-first-reviewer, where the agent reviews before the human and catche…
<!-- SC_OFF --><div class="md"><p>One challenge I don't see discussed enough: as AI coding tools get better at generating code, teams are shipping faster, but the architecture is quietly degrading underneath. </p> <p>The problem is that most AI tools are stateless. They generate …
<table> <tr><td> <a href="https://www.reddit.com/r/StableDiffusion/comments/1u886jh/agentic_ai_workflow_creation_using_claude_or/"> <img alt="Agentic AI workflow creation using Claude or cursor" src="https://external-preview.redd.it/em9tNzlpbW4xdTdoMTc-dnVvrW1nROx2II0b8iVutPa2INq…
<!-- SC_OFF --><div class="md"><p>Hi,</p> <p>I’m wondering about the $60/month plan. Are Claude Opus, Codex, and other models included?</p> <p>Are there any limitations expect token usage?</p> </div><!-- SC_ON -->   submitted by   <a href="https://www.reddit.com/user/atri…
<!-- SC_OFF --><div class="md"><p>Hey everyone. I dont really have any knowledge about any of this stuff.. Im an architecture student looking for an image generating open source model to help me with renders and designing. My pc specs are rtx 5070 12 vram 32gb ddr5 and an ultra 5…
<!-- SC_OFF --><div class="md"><p>My colleagues kept asking me for my setup, so I decided to turn it into a universal plugin: <strong>Agent Code Navigator</strong> - a universal code-navigation plugin for Cursor, Claude, Codex, Gemini, and OpenCode.</p> <p>In my benchmark, semant…
<!-- SC_OFF --><div class="md"><p>Been running an agent-heavy workflow on a mid-size TypeScript monorepo for about six months. Orchestrator on top, sub-agents for codegen, a human (me, mostly) writing specs and reviewing diffs. The pitch was the obvious one: I stay in the archite…
<!-- SC_OFF --><div class="md"><p>Flagging this because it seems more relevant to actual coding loops than to general AI-news posting: Ring-2.6-1T is now out, and there’s a free developer access window through May 15.<br /> The launch angle is pretty clearly “reasoning model for …
<table> <tr><td> <a href="https://www.reddit.com/r/cursor/comments/1t6zy9k/discover_meko_the_data_infrastructure_for_agents/"> <img alt="Discover Meko: The Data Infrastructure for Agents That Work and Learn Together" src="https://preview.redd.it/ea544mxdupzg1.jpeg?width=640&c…
<!-- SC_OFF --><div class="md"><p>I’ve been playing around with an idea for development teams and their agents and would love some feedback.</p> <p>What if agents working on the same project could learn from each other over time? Think of it as a Stack Overflow built by agents, f…
<table> <tr><td> <a href="https://www.reddit.com/r/ClaudeAI/comments/1uh0t7o/i_opensourced_industry_best_practice_to/"> <img alt="I open-sourced industry best practice to self-improving agents" src="https://preview.redd.it/i6m1kc9gbt9h1.png?width=640&crop=smart&auto=webp&…
<table> <tr><td> <a href="https://www.reddit.com/r/ClaudeAI/comments/1uaplfy/a_context_brain_for_you_and_your_ai_agent/"> <img alt="A Context Brain for you (and your AI Agent)" src="https://external-preview.redd.it/enYzc21ncGp3ZDhoMc0qeEjPjE8oY_VYNqXTY77bMsvN6Dt_eef3EFzgT140.png?…
<!-- SC_OFF --><div class="md"><p>I am 19, and the Founder and CEO of AutoFlow. I want to be entirely transparent before discussing our current team or your potential role: you should know exactly the engineering challenge we are tackling.</p> <p>We are building the trust infrast…
<!-- SC_OFF --><div class="md"><p>I work on a distributed backend system split across multiple microservices in separate repos. Understanding how a failure propagates across services is<br /> non-trivial even for experienced team members.</p> <p>I've been using Claude Code with c…
<table> <tr><td> <a href="https://www.reddit.com/r/OpenAI/comments/1tq02zg/from_ai_agents_to_know_your_agent_why_kya_is/"> <img alt="From AI Agents to Know Your Agent: Why KYA Is Critical for Secure Autonomous AI" src="https://external-preview.redd.it/SYNihEB_CpsXPD5wVhhCmJ_fz7a7…
<table> <tr><td> <a href="https://www.reddit.com/r/singularity/comments/1uw34a4/an_openweight_mit_trillionparam_model_ants_ring26/"> <img alt="An open-weight, MIT trillion-param model (Ant's Ring-2.6) reportedly matches the closed frontier on reasoning + agent benchmarks. Does &q…
