OpenAI launches AgentKit; Google DeepMind unveils AI coding agents
ByPulseAugur Editorial·
Summary by gemini-2.5-flash-lite from 816 sources
OpenAI has released AgentKit, a comprehensive suite of tools designed to streamline the development, deployment, and optimization of AI agents. This new toolkit includes an Agent Builder for visual workflow creation, a Connector Registry for managing data integrations, and ChatKit for embedding agentic UIs. Concurrently, Google DeepMind has introduced CodeMender, an AI agent focused on automatically identifying and fixing software vulnerabilities, and AlphaEvolve, a Gemini-powered agent for algorithm discovery and optimization. OpenAI also detailed its Computer-Using Agent (CUA), which interacts with digital interfaces like a human, achieving state-of-the-art results on various benchmarks.
AI
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>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…
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:2605.20876v1 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 …
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: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.AI
TIER_1·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: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: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: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 cs.AI
TIER_1·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: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:2605.20190v1 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: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:2605.10787v2 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 cs.AI
TIER_1·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.AI
TIER_1·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: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 cs.CL
TIER_1·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 cs.CL
TIER_1·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: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_1·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.…
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…
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_1·Zvi Mowshowitz·
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, …
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…
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…
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…
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…
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…
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: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.AI
TIER_1·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 cs.AI
TIER_1·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: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_1·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: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: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:2605.06434v1 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:2605.06365v1 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:2605.06230v1 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:2605.05980v1 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 …
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:2605.06614v1 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_1·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_1·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 cs.LG
TIER_1·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:2605.06522v1 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 …
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 cs.AI
TIER_1·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:2605.03213v1 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_1·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…
arXiv cs.AI
TIER_1·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:2605.03675v1 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: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: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_1·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: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: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 cs.AI
TIER_1·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: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: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 cs.AI
TIER_1·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_1·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 …
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: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: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_1·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_1·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: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_1·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: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 cs.AI
TIER_1·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_1·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_1·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_1·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_1·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…
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_1·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…
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: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…
arXiv cs.LG
TIER_1·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:2605.00424v1 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: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 cs.AI
TIER_1·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…
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 cs.LG
TIER_1·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_1·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_1·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…
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: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 …
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: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·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_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: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: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 …
arXiv cs.AI
TIER_1·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…
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 cs.AI
TIER_1·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…
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: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…
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: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_1·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…
arXiv cs.CL
TIER_1·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 cs.CL
TIER_1·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_1·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_1·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 …
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 cs.AI
TIER_1·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 cs.CL
TIER_1·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_1·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 cs.CL
TIER_1·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: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_1·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…
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.LG
TIER_1·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: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.AI
TIER_1·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: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: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 …
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_1·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_1·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.
<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…
<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_1·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_1·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. …
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_1·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_1·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_1·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…
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.
<!-- 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…
<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…
Hacker News — AI stories ≥50 points
TIER_1·maxloh·
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_1·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_1·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?
<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…
<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…
<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…
<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…
<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…
<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…
<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…
<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><…
<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 …
<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…
<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…
<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…
<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…
<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…
<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> …
<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…
<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…
<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_1·Princess Jordan Nwukor·
Email — Every
TIER_1Nederlands(NL)·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. --> Google I/O: Agents, Agents, Agents <!-- N…
Medium — Claude tag
TIER_1·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…
<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…
<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_1·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…
📊 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…
<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/…
<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…
<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.”…
<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
<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…
<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…
<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.</…
<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…
<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_1·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 …
<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…
<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…
<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…
<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…
<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…
<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…
<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://…
<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…
<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…
<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…
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…
<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>:…
<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…
<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…
<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…
<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…
<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…
<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. …
<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 …
<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…
<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><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:<…
<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…
<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…
<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…
<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…
<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…
<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…
<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…
<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…
<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…
<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…
<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=…
<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…
<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(…
<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…
<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…
<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…
<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><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 …
<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_1·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.…
<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…
<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…
<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…
<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…
<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…
<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 …
<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…
<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…
<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…
<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…
<!-- 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…
