When millions of AI agents interact with each other, new collective behaviors can emerge. 🌐
Together with @schmidtsciences, @coop_ai, @ARIA_research and supported by @GoogleOrg, we’re launching a $10M research fund to help understand how AI systems behave as a group. → https://t…
OpenAI supports the EU Code of Practice on AI content transparency, advancing provenance standards and tools to help people understand AI-generated content.
See how LSEG uses OpenAI to scale trusted AI across its global business, accelerating insights, shrinking release cycles, and empowering 4,000 employees.
Learn how Endava is using AI agents, ChatGPT Enterprise, and Codex to accelerate software delivery, automate workflows, and build an AI-native culture across the enterprise.
Serving a wide range of AI models on a global scale, while maintaining the lowest possible costs, is one of the most demanding infrastructure challenges in the industry.
Today, we’re releasing new tools to help developers go from prototype to production faster: AgentKit, expanded evals capabilities, and reinforcement fine-tuning for agents.
New AI agent evolves algorithms for math and practical applications in computing by combining the creativity of large language models with automated evaluators
<p>Understanding AI as an extension of human intelligence—not a replacement for it—offers a more grounded path for building trustworthy AI systems.</p> <p>The post <a href="https://www.microsoft.com/en-us/research/blog/extending-human-intelligence-through-ai/">Extending Human Int…
<p>MagenticLite is an agentic system for small models that works across the browser and local file system in a single workflow. It combines specialized models and orchestration to support efficient agentic performance on everyday tasks.</p> <p>The post <a href="https://www.micros…
Today we introduce Qwen3.7-Max, our latest proprietary model designed for the agent era. Qwen3.7-Max is built to be a versatile agent foundation — equally capable of writing and debugging code, automating office workflows, and sustaining autonomous execution across hundreds or th…
Following the release of the Qwen3.5 series in February, we are thrilled to announce the official launch of Qwen3.6-Plus. Available immediately via our API, this release represents a massive capability upgrade over its predecessor. Most notably, we have drastically enhanced the m…
AgentPerf from Artificial Analysis, the industry’s first agentic AI benchmark, gives developers, enterprises and infrastructure providers a clear way to compare systems for agentic AI. In the first round of published results, the NVIDIA Blackwell Ultra NVL72 platform delivers lea…
arXiv:2606.12736v1 Announce Type: new Abstract: AI agents are increasingly being developed to accelerate scientific discovery, yet their practical capabilities in real research settings remain poorly understood. Existing benchmarks for AI agents rarely capture the complexity, het…
arXiv cs.AI
TIER_1English(EN)·Shayan Kiyani, Sima Noorani, George Pappas, Hamed Hassani·
arXiv:2606.12587v1 Announce Type: new Abstract: Traditionally, decision support studies how humans use machine learning models to make better decisions. In modern agentic systems, this division of roles is increasingly reversed: AI agents act on behalf of users, while humans and …
arXiv:2606.12647v1 Announce Type: cross Abstract: AI-augmented computing delegates natural language queries, code generation requests, and other open-ended tasks to a cluster of AI models that processes queries and generates responses. This paradigm introduces a resource dimensio…
arXiv:2606.12783v1 Announce Type: new Abstract: World modeling is emerging as a central principle for building intelligent systems capable of prediction, reasoning, and decision making. A central distinction can be drawn between explicit world models, which learn structured dynam…
arXiv cs.AI
TIER_1English(EN)·Md Jafrin Hossain, Mohammad Arif Hossain, Weiqi Liu, Nirwan Ansari·
arXiv:2606.12797v1 Announce Type: new Abstract: Agentic large language model systems that autonomously invoke tools, maintain persistent memory, and execute multi-step plans are increasingly deployed in public-facing domains, including government services, healthcare triage, and …
arXiv:2601.21570v2 Announce Type: replace Abstract: The field of Embodied AI is witnessing a rapid evolution toward general-purpose robotic systems, fueled by high-fidelity simulation and large-scale data collection. However, this scaling capability remains severely bottlenecked …
arXiv:2606.12835v1 Announce Type: cross Abstract: The rapid emergence of autonomous AI agents is transforming artificial intelligence from isolated model inference into distributed systems of reasoning, communication, and action. This paper develops the vision of the Internet of …
arXiv:2606.13079v1 Announce Type: cross Abstract: Nowadays, the autonomous execution of cyberattacks capable of causing substantial real-world harm is widely regarded as one of the critical red lines that frontier AI systems must not cross. Within this broader red-line scenario, …
arXiv cs.AI
TIER_1English(EN)·Oliver Aleksander Larsen, Mahyar T. Moghaddam·
arXiv:2606.13298v1 Announce Type: cross Abstract: AI coding tools are now used by a majority of developers, and agentic use of these tools has popularized the practice colloquially called "vibe coding". Yet causal evidence on their effect on software architecture is scarce. Prior…
arXiv cs.AI
TIER_1English(EN)·Mahyar T. Moghaddam·
AI coding tools are now used by a majority of developers, and agentic use of these tools has popularized the practice colloquially called "vibe coding". Yet causal evidence on their effect on software architecture is scarce. Prior causal work has measured code-level outcomes (com…
arXiv cs.AI
TIER_1English(EN)·Hayoung Jung, Pedro Viana Diniz, Jos\'e Reinaldo Corr\^ea Roveda, Abner Fernandes da Silva, Haeun Jung, Enoch Tsai, Aleksandra Korolova, Manoel Horta Ribeiro·
arXiv:2606.11337v1 Announce Type: new Abstract: Scientific AI agents increasingly retrieve evidence, reason across sources, and synthesize conclusions used in consequential decisions. Yet, their ability to do so in high-stakes domains such as health remains unclear. We introduce …
arXiv:2606.11869v1 Announce Type: cross Abstract: Custom AI agents areagents that live inside their own application, talk to their own data and tools, enforce their own security boundaries, and carry their own brand and audit trail. What separates them from the general-purpose ti…
arXiv cs.AI
TIER_1English(EN)·Arijit Khan, Longxu Sun, Xin Huang·
arXiv:2606.11560v1 Announce Type: cross Abstract: Large Language Models (LLMs) have advanced rapidly, but their limitations in structured and multi-hop reasoning underscore the need for graph-native, synergistic artificial intelligence (AI) systems. Graph-structured data underpin…
arXiv:2606.11217v1 Announce Type: cross Abstract: The proliferation of large language models (LLMs) and autonomous AI agents has given rise to a rapidly growing methodological paradigm: "in silico" behavioral experiments. Originally conceived as a way to use AI agents as proxies …
arXiv:2606.12320v1 Announce Type: new Abstract: Enterprise security was built to govern data boundaries: the protected surface was data at rest and in transit, and the controls -- access control, data-loss prevention, perimeter inspection -- governed crossings of that boundary. P…
arXiv:2605.10907v3 Announce Type: replace-cross Abstract: The dominant paradigm for AI agents is an "on-the-fly" loop in which agents synthesize plans and execute actions within seconds or minutes in response to user prompts. We argue that this paradigm short-circuits disciplined…
arXiv cs.LG
TIER_1English(EN)·Frank Xiao, Mary Phuong·
arXiv:2606.11998v1 Announce Type: new Abstract: Trusted monitoring is a cornerstone of AI control. However, as frontier models grow more capable, the increasing capabilities gap between trusted and untrusted models may render trusted models unreliable monitors. We introduce \emph…
arXiv cs.LG
TIER_1English(EN)·Felipe Oviedo, Fiodar Kazhamiaka, Esha Choukse, Allen Kim, Amy Luers, Melanie Nakagawa, Ricardo Bianchini, Juan M. Lavista Ferres·
arXiv:2509.20241v2 Announce Type: replace Abstract: As AI inference scales to billions of queries, estimates of per-query energy use are increasingly important for capacity planning, efficiency interventions, and policy. Yet many public estimates assume non-production settings, l…
The rapid emergence of autonomous AI agents is transforming artificial intelligence from isolated model inference into distributed systems of reasoning, communication, and action. This paper develops the vision of the Internet of Agentic AI (IoAI): an open ecosystem in which hete…
Enterprise security was built to govern data boundaries: the protected surface was data at rest and in transit, and the controls -- access control, data-loss prevention, perimeter inspection -- governed crossings of that boundary. Production AI agents dissolve this assumption. An…
Trusted monitoring is a cornerstone of AI control. However, as frontier models grow more capable, the increasing capabilities gap between trusted and untrusted models may render trusted models unreliable monitors. We introduce \emph{bootstrapped monitoring}, a protocol that addre…
arXiv cs.AI
TIER_1English(EN)·María José Casañ Guerrero·
Custom AI agents areagents that live inside their own application, talk to their own data and tools, enforce their own security boundaries, and carry their own brand and audit trail. What separates them from the general-purpose tier is fit, not capability: each is built for one j…
arXiv cs.AI
TIER_1English(EN)·James Pierce, Vaiva Kalnikait\.e, Siddharth Gupta, Brian Granger·
arXiv:2606.09848v1 Announce Type: cross Abstract: As generative and agentic AI becomes embedded in everyday products, practitioners face a persistent challenge: how to design human-AI coordination -- the ongoing mutual adjustment between users and AI systems as mediate through in…
arXiv cs.AI
TIER_1English(EN)·Federico Bianchi, Yongchan Kwon, Aneesh Pappu, James Zou·
arXiv:2606.10402v1 Announce Type: cross Abstract: Scientific discovery is often a collective process: researchers share partial results, inspect failed attempts, and build on each other's ideas over long time horizons. Recent AI systems have shown that language-model-based agents…
arXiv:2510.04491v3 Announce Type: replace Abstract: Despite rapid progress in building conversational AI agents, robustness is still largely untested. Small shifts in user behavior, such as being more impatient, incoherent, or skeptical, can cause sharp drops in agent performance…
Large Language Models (LLMs) have advanced rapidly, but their limitations in structured and multi-hop reasoning underscore the need for graph-native, synergistic artificial intelligence (AI) systems. Graph-structured data underpins critical applications across social, biological,…
Scientific discovery is often a collective process: researchers share partial results, inspect failed attempts, and build on each other's ideas over long time horizons. Recent AI systems have shown that language-model-based agents can make meaningful progress on open scientific p…
arXiv cs.AI
TIER_1English(EN)·Ian Seet, Jonas Bozenhard, Simon Osterman·
arXiv:2606.07998v1 Announce Type: cross Abstract: Recent advances in generative AI, especially powerful Large Language Models (LLMs) and Large Reasoning Models (LRMs), raise concerns over the interpretability, safety and sustainability of these large and opaque AI models. The pow…
arXiv:2602.06934v4 Announce Type: replace-cross Abstract: Grassroots Logic Programs (GLP) is a concurrent logic programming language in which logic variables are partitioned into paired readers and writers. An assignment is produced at most once via a writer and consumed at most …
arXiv:2606.07812v1 Announce Type: new Abstract: Humanity is a mosaic of multifaceted talents and needs, and any truly intelligent AI must reflect that richness. Yet the LLMs used by all are built by the few -- a centralized market of monolithic AI models structurally ill-suited t…
arXiv cs.AI
TIER_1English(EN)·Kai A. Horstmann, Ethan Lin, Alice A. Robie, Jennifer J. Sun, Kristin Branson·
arXiv:2606.07718v1 Announce Type: new Abstract: Agentic AI tools offer a promising path to automating software development bottlenecks in scientific research pipelines, particularly for stages that take domain experts days to months to build, where scientists care about correctne…
arXiv cs.AI
TIER_1English(EN)·Muhammad Zia Hydari, Raja Iqbal·
arXiv:2606.08998v1 Announce Type: new Abstract: Agentic AI systems can behave differently across runs: the same request may produce a different plan, a different tool call, a different code edit, or a different final answer. Such variability arises from several layers that are of…
arXiv:2606.08539v1 Announce Type: new Abstract: AI agents increasingly take consequential actions -- shell commands, cloud operations, and arbitrary tool-calls -- so a trust layer must decide, per action, whether to allow, warn, block, or escalate. We argue that the right way to …
arXiv:2606.07576v1 Announce Type: new Abstract: We present CARTOGRAPH, a verification layer for AI scientists that couples unresolved-subspace experiment steering (select), explicit ambiguity closure (resolve), and residual-based library inadequacy detection (refuse). Under a loc…
arXiv:2605.22781v2 Announce Type: replace-cross Abstract: LLM-powered AI agents require high-frequency state exploration (e.g., test-time tree search and reinforcement learning), relying on rapid checkpoint and rollback (C/R) of the complete sandbox state, including files and pro…
arXiv cs.AI
TIER_1English(EN)·Jun Takahashi, Atsunori Moteki, Akiyoshi Uchida, Shoichi Masui, Fan Yang, Kanji Uchino, Yueqi Song, Yonatan Bisk, Graham Neubig, Ikuo Kusajima, Yasuto Watanabe, Hiroyuki Ishida, Koki Nakagawa, Shan Jiang·
arXiv:2505.19662v4 Announce Type: replace Abstract: This paper introduces FieldWorkArena, a benchmark for agentic AI targeting real-world field work. With the recent increase in demand for agentic AI, they are built to detect and document safety hazards, procedural violations, an…
arXiv:2606.09692v1 Announce Type: cross Abstract: Delegation-scoped execution is not identifiable from standard observables: audit logs and execution traces can be identical under multiple incompatible delegation assignments. This gap is especially acute in LLM-based agentic syst…
arXiv cs.AI
TIER_1English(EN)·Muhammad Haris Khan, Joel wester·
arXiv:2606.09587v1 Announce Type: cross Abstract: People are increasingly using AI for creative tasks such as writing. While adoption continues to grow, this form of use risks undermining individual creativity locally and reducing the heterogeneity of creative output at scale. In…
arXiv cs.AI
TIER_1English(EN)·Yifan Liu (Klara), Jaime Arguello (Klara), Orland Hoeber (Klara), Chang Liu (Klara), Soo Young Rieh (Klara), Luanne Sinnamon (Klara), Dean Alvarez (Klara), Susan Archambault (Klara), Rob Capra (Klara), Henson Chen (Klara), Charles Costa (Klara), Anita Cr…·
arXiv:2606.08936v1 Announce Type: cross Abstract: This report summarizes the CHIIR 2026 Workshop on Generative AI and Academic Search (GAI\&AS), which examined how GenAI is reshaping academic search systems and research practices. The workshop brought together researchers in …
arXiv cs.AI
TIER_1English(EN)·Rishabh Sabharwal, Hongru Wang, Amos Storkey, Jeff Z. Pan·
arXiv:2606.09748v1 Announce Type: new Abstract: Existing benchmarks for deep research agents (DRAs) assess only single-shot outputs, ignoring a key question: can DRAs improve their reports when guided by feedback? To investigate this, we conduct a multi-turn evaluation of DRAs un…
Existing benchmarks for deep research agents (DRAs) assess only single-shot outputs, ignoring a key question: can DRAs improve their reports when guided by feedback? To investigate this, we conduct a multi-turn evaluation of DRAs under two feedback settings: self-reflection, in w…
Delegation-scoped execution is not identifiable from standard observables: audit logs and execution traces can be identical under multiple incompatible delegation assignments. This gap is especially acute in LLM-based agentic systems, where agents dynamically select tools, vary e…
People are increasingly using AI for creative tasks such as writing. While adoption continues to grow, this form of use risks undermining individual creativity locally and reducing the heterogeneity of creative output at scale. In response, we introduce the Semantic Repulsion Tec…
arXiv:2603.13428v2 Announce Type: replace-cross Abstract: With AI agents increasingly deployed as long-running systems, it becomes essential to autonomously construct and continuously evolve customized software to enable interaction within dynamic environments. Yet, existing benc…
arXiv:2605.06890v3 Announce Type: replace Abstract: AI agents are promising for high-stakes enterprise workflows, but dependable deployment remains limited because tool-use failures are difficult to diagnose and control. Agents may skip required tool calls, invoke tools unnecessa…
arXiv cs.AI
TIER_1English(EN)·Catherine Ge-Wang, Tyler Crosse, Benjamin Hadad IV, Joachim Schaeffer, Ram Potham, Tyler Tracy·
arXiv:2606.06529v1 Announce Type: new Abstract: An attacker that strategically chooses when to attack is much harder to catch than one that attacks indiscriminately. AI control is a safety framework for deploying capable but untrusted AI agents under the oversight of a weaker, tr…
arXiv:2606.06660v1 Announce Type: new Abstract: Long-horizon robot manipulation tends to fail gradually: one bad step degrades the state, and the policy spirals into a basin from which it cannot recover. The failure is often visible before it happens. We introduce AEGIS (Activati…
arXiv cs.AI
TIER_1English(EN)·M. Danish Lim, I. Danial Bin Sharudin, Wen Han Chen, Cedric Lim, Laura Wynter·
arXiv:2606.06923v1 Announce Type: new Abstract: We study orchestration mechanisms for tool-using AI agents in realistic customer-service workflows over an unstructured knowledge base. We argue that declarative agents -- AI agents equipped with natural-language skill files appende…
arXiv cs.AI
TIER_1English(EN)·Jeremy Yang, Kate Zyskowski, Noah Yonack, Jerry Ma·
arXiv:2606.07489v1 Announce Type: new Abstract: Frontier AI systems are bridging the gap between intelligence and utility by shifting from conversational assistants to autonomous agents that execute tasks end to end. Using production data from Perplexity's Search and Computer pro…
This report summarizes the CHIIR 2026 Workshop on Generative AI and Academic Search (GAI\&AS), which examined how GenAI is reshaping academic search systems and research practices. The workshop brought together researchers in human information interaction and information retrieva…
AI agents increasingly take consequential actions -- shell commands, cloud operations, and arbitrary tool-calls -- so a trust layer must decide, per action, whether to allow, warn, block, or escalate. We argue that the right way to reason about such a layer is by threat type. Lex…
The emergence of large language model (LLM)-based agents and multi-agent systems has enabled a shift from narrow task automation to more autonomous decision-making. Despite progress in language generation, planning, tool use, and coordination, most agents still treat intelligence…
arXiv:2606.05608v1 Announce Type: cross Abstract: For over half a century, software engineering has operated on a foundational premise: human engineers decompose problems, encode decision logic into static code, and manually adapt that code as requirements evolve. This paper argu…
arXiv cs.AI
TIER_1English(EN)·Yunhao Yang, Neel P. Bhatt, Kevin Wang, Samuel Tetteh, Zhangyang Wang, Ufuk Topcu·
arXiv:2606.05395v1 Announce Type: cross Abstract: Reusable robot skills are becoming the basic units through which embodied agents turn open-ended instructions into long-horizon physical behavior. We argue that, while foundation models have collapsed the cost of creating these sk…
arXiv:2606.05449v1 Announce Type: new Abstract: Agentic artificial intelligence (AI) systems are transforming the risk landscape by extending beyond information generation to autonomous planning, tool invocation, decision execution, and persistent modification of digital and phys…
Frontier AI systems are bridging the gap between intelligence and utility by shifting from conversational assistants to autonomous agents that execute tasks end to end. Using production data from Perplexity's Search and Computer products, we study this transition by examining how…
We study orchestration mechanisms for tool-using AI agents in realistic customer-service workflows over an unstructured knowledge base. We argue that declarative agents -- AI agents equipped with natural-language skill files appended to the system prompt -- are an effective orche…
arXiv cs.LG
TIER_1English(EN)·Otto Nyberg, Fausto Carcassi, Davide Tugnoli, Giovanni Cin\`a·
arXiv:2602.21889v2 Announce Type: replace-cross Abstract: Predictions from ML models support human decision making in several fields, including high-stakes ones such as healthcare and the judiciary. Yet, we still lack a clear understanding of how decision makers learn from ML-bas…
AI agents are commonly evaluated using task success, reward, latency, and cost. These metrics are useful, but they often miss important aspects of agent behavior: whether an agent explores too much, repeats itself too rigidly, uses tools effectively, reduces uncertainty over time…
arXiv:2006.04013v6 Announce Type: cross Abstract: Artificial Intelligence (AI) has been adopted in a wide range of domains. This shows the imperative need to develop means to endow common people with a minimum understanding of what AI means. Combining visual programming and WiSAR…
arXiv cs.AI
TIER_1English(EN)·Harsha Vardhan Khurdula, Vineet Agarwal, Yoeven D Khemlani·
arXiv:2602.04101v2 Announce Type: replace Abstract: We present Interfaze, a native hybrid model that fuses task-specific deep neural networks (CNNs and DNNs) directly into a transformer decoder through a shared embedding space. Specialized perceptual encoders handle optical chara…
arXiv:2401.07386v5 Announce Type: cross Abstract: This study expands on previous work that introduced the AIcon2abs method (AI from Concrete to Abstract: Demystifying Artificial Intelligence to the general public), an innovative approach designed to increase public understanding …
arXiv:2606.04779v1 Announce Type: new Abstract: Complementarity is the case in which a human--AI interaction (HAI) outperforms the best prediction benchmark available among its members. Although this idea is central in HAI research, formal work on complementarity remains limited.…
arXiv:2606.04321v1 Announce Type: new Abstract: Agentic AI deployments face a recurring design tension: heavy human oversight limits scale, while broad autonomy outruns accountability. Neither posture provides the governance infrastructure required for responsible delegation. We …
arXiv:2606.05037v1 Announce Type: cross Abstract: When an AI agent calls an API and hits a validation error, it needs more than what went wrong -- it needs what to do next. A self-reflective API returns, on validation failure, a machine-readable recovery\_feedback.suggestions[] p…
arXiv cs.AI
TIER_1English(EN)·Sanderson Oliveira de Macedo·
arXiv:2606.04967v1 Announce Type: cross Abstract: AI tools for programming are no longer just autocomplete or chat assistants: they organize themselves as development frameworks, with process, roles, artifacts and verification. Recent surveys map agents and LLMs for software engi…
arXiv cs.AI
TIER_1English(EN)·Ulbert Jose Botero, Liam Smith, Brooks Olney, Pooya Khorrami, Steven Kusiak, Watson Jia, Sage Trudeau, Daniel Capecci·
arXiv:2606.04106v1 Announce Type: cross Abstract: Foundation models achieve generalization through massive-scale training on diverse data, but have limitations with transfer to truly unseen domains without paired training data. We propose principle-driven foundation models that e…
arXiv cs.AI
TIER_1English(EN)·Katherine M. Collins, Simon Frieder, Jonas Bayer, Jacob Loader, Jeck Lim, Peiyang Song, Fabian Zaiser, Lexin Zhou, Shanda Li, Sam Looi, Joshua B. Tenenbaum, Umang Bhatt, Adrian Weller, Jose Hernandez-Orallo, Cameron E. Freer, Valerie Chen, Ilia Sucholuts…·
arXiv:2606.04273v1 Announce Type: new Abstract: For centuries, human mathematicians have written proofs to substantiate their mathematical arguments; yet, the ability to automatically verify the validity of proofs has long been a challenge. Advances in AI systems' ability to gene…
ForeSci is a temporally controlled benchmark that evaluates LLM agents' ability to make forward-looking research decisions from historical evidence across fast-moving AI domains.
When an AI agent calls an API and hits a validation error, it needs more than what went wrong -- it needs what to do next. A self-reflective API returns, on validation failure, a machine-readable recovery\_feedback.suggestions[] payload sufficient for the agent to repair the requ…
When an AI agent calls an API and hits a validation error, it needs more than what went wrong -- it needs what to do next. A self-reflective API returns, on validation failure, a machine-readable recovery\_feedback.suggestions[] payload sufficient for the agent to repair the requ…
arXiv cs.AI
TIER_1English(EN)·Sanderson Oliveira de Macedo·
AI tools for programming are no longer just autocomplete or chat assistants: they organize themselves as development frameworks, with process, roles, artifacts and verification. Recent surveys map agents and LLMs for software engineering, but a study centered on the operational f…
Complementarity is the case in which a human--AI interaction (HAI) outperforms the best prediction benchmark available among its members. Although this idea is central in HAI research, formal work on complementarity remains limited. Existing frameworks do not model how agents' pr…
arXiv cs.AI
TIER_1English(EN)·Xuanqiang Angelo Huang, Charlie Tharas, Samuele Marro, Van Q. Truong, Bernhard Sch\"olkopf, Emanuele La Malfa, Zhijing Jin·
arXiv:2605.08426v2 Announce Type: replace-cross Abstract: Ensuring that AI agents behave safely and beneficially when interacting with other parties has emerged as one of the central challenges of modern AI safety. While mechanism design, as the theory of designing rules to align…
arXiv:2606.03518v1 Announce Type: new Abstract: As AI systems evolve from passive models into autonomous active agents capable of initiating actions, collaborating, and delegating tasks, the traditional boundaries of software systems blur. Traditional authorization and delegation…
arXiv:2602.16666v3 Announce Type: replace Abstract: AI agents are increasingly deployed to execute important tasks. While rising accuracy scores on standard benchmarks suggest rapid progress, many agents still continue to fail in practice. This discrepancy highlights a fundamenta…
arXiv cs.AI
TIER_1English(EN)·Marcus R\"ub, Michael Gerhards·
arXiv:2606.02862v1 Announce Type: new Abstract: The rise of Large Language Models (LLMs) has enabled agentic AI capable of complex reasoning and tool use; however, deploying such autonomy in pervasive computing environments remains challenging due to the strict memory and energy …
arXiv:2606.00090v1 Announce Type: cross Abstract: Physical AI systems increasingly map multimodal observations, language instructions, and learned world representations into physically consequential actions. Robotics foundation models, vision-language-action models, and world-mod…
arXiv cs.AI
TIER_1English(EN)·Kevin Kappelmann, Maximilian Sch\"affeler, Lukas Stevens, Mohammad Abdulaziz, Andrei Popescu, Dmitriy Traytel·
arXiv:2604.15713v2 Announce Type: replace-cross Abstract: Type annotations are essential when printing terms in a way that preserves their meaning under reparsing and type inference. We study the problem of complete and minimal type annotations for rank-one polymorphic $\lambda$-…
arXiv cs.AI
TIER_1English(EN)·Sindhuja Chaduvula, Jessee Ho, Kina Kim, Aravind Narayanan, Ahmed Y. Radwan, Mahshid Alinoori, Muskan Garg, Dhanesh Ramachandram, Shaina Raza·
arXiv:2602.06841v4 Announce Type: replace Abstract: Over the last decade, Explainable AI has primarily focused on interpreting individual model predictions, producing post-hoc explanations that relate inputs to outputs under a fixed decision structure. Recent advances in large la…
arXiv cs.AI
TIER_1English(EN)·An Luo, Jin Du, Xun Xian, Robert Specht, Fangqiao Tian, Ganghua Wang, Xuan Bi, Charles Fleming, Ashish Kundu, Jayanth Srinivasa, Mingyi Hong, Rui Zhang, Tianxi Li, Galin Jones, Jie Ding·
arXiv:2603.19005v2 Announce Type: replace-cross Abstract: Data science plays a critical role in transforming complex data into actionable insights across numerous domains. Recent developments in large language models (LLMs) and artificial intelligence (AI) agents have significant…
arXiv:2606.00644v1 Announce Type: new Abstract: AI research often requires decisions before future evidence exists: which bottleneck to attack, which direction to pursue, or where a project should be positioned. We introduce ForeSci, a temporally controlled benchmark for evaluati…
arXiv cs.AI
TIER_1English(EN)·Fiona Y. Wang, Markus J. Buehler·
arXiv:2606.01444v1 Announce Type: new Abstract: Scientific discovery is not only answer generation but revision of the representational regime in which evidence, artifacts, operations, and verifiers are typed. We develop a category-theoretic account of agentic discovery for mater…
The rise of Large Language Models (LLMs) has enabled agentic AI capable of complex reasoning and tool use; however, deploying such autonomy in pervasive computing environments remains challenging due to the strict memory and energy constraints of embedded microcontrollers. Existi…
arXiv cs.AI
TIER_1English(EN)·Jun Rui Huang, Wang Bill Zhu, Ziyi Liu, Nathanael Fast, Ravi Iyer, Robin Jia·
arXiv:2605.30654v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as conversational partners for companionship, emotional disclosure, and interpersonal advice, but the social dynamics of these interactions can create harms that are not captured …
arXiv cs.AI
TIER_1English(EN)·David Fern\'andez-Narro, Pablo Ferri, \'Angel S\'anchez-Garc\'ia, Juan M. Garc\'ia-G\'omez, Carlos S\'aez·
arXiv:2605.31360v1 Announce Type: cross Abstract: The Artificial Intelligence (AI) life cycle requires a thorough understanding of the underlying data dynamics for robust, safe and cost-effective AI development and use. Dataset shifts are defined as changes between train and test…
The Artificial Intelligence (AI) life cycle requires a thorough understanding of the underlying data dynamics for robust, safe and cost-effective AI development and use. Dataset shifts are defined as changes between train and test data distributions. Whether occurring over time (…
arXiv:2605.29676v1 Announce Type: new Abstract: Large language models in Agentic AI systems consume tool schemas and execution results and emit tool invocations as structured data. The default language for that exchange, JSON, was designed for application-to-application interchan…
arXiv:2605.28916v1 Announce Type: cross Abstract: We report a comparison of two state-of-the-art agentic AI systems, Claude Code (Anthropic) and Codex (OpenAI), tasked with autonomously executing a simple end-to-end gravitational wave data analysis pipeline on a shared computing …
arXiv:2605.29713v1 Announce Type: cross Abstract: This book provides a compact, derivation-oriented introduction to the mathematical foundations of modern generative artificial intelligence. Rather than surveying every recent architecture or implementation detail, it develops a c…
arXiv cs.CL
TIER_1English(EN)·Vishakh Padmakumar, Lujain Ibrahim, Zora Zhiruo Wang, Jennifer Wang, Q. Vera Liao, Diyi Yang·
arXiv:2605.29392v1 Announce Type: cross Abstract: AI tools are increasingly integrated into real-world workflows. However, existing measures of reliance on these tools focus on AI output adoption or on self-reported indicators, rather than how task effort is distributed between u…
arXiv cs.AI
TIER_1English(EN)·William Yicheng Zhu, Lei Zhu·
arXiv:2604.04956v3 Announce Type: replace-cross Abstract: The recent, super-exponential scaling of autonomous Large Language Model (LLM) agents signals a broader, fundamental paradigm shift from machines primarily replacing the human hands (manual labor and mechanical processing)…
arXiv cs.AI
TIER_1English(EN)·Muhammad Zia Hydari, Raja Iqbal, Narayan Ramasubbu·
arXiv:2605.29129v1 Announce Type: new Abstract: Agentic AI systems are increasingly being explored as production infrastructure: they reason over multiple steps, call tools, act through workflows, and adapt through memory and feedback. These systems create governance challenges t…
arXiv:2605.28508v1 Announce Type: new Abstract: Existing AI evaluation practices often fail to capture how systems actually perform in low-resource environments, where operational constraints shape usability as much as model quality. Through a structured analysis of existing benc…
arXiv:2605.27873v1 Announce Type: new Abstract: AI models underpin data-centric applications from image and text processing to scientific discovery in biology, physics, and chemistry. Yet developing them remains heavily manual, requiring practitioners to design architectures, bui…
arXiv:2605.27879v1 Announce Type: new Abstract: Explainable AI (XAI) helps users interpret model behavior and identify potential faults. Agentic XAI systems use Large Language Models (LLMs) to make explanations more accessible through natural-language interaction, but they can al…
arXiv:2604.14585v2 Announce Type: replace Abstract: Prompt optimization in compound AI systems is statistically indistinguishable from a coin flip: across 72 optimization runs on Claude Haiku 4.5 (6 methods $\times$ 4 tasks $\times$ 3 repeats), 49% score below zero-shot; on Amazo…
arXiv cs.AI
TIER_1English(EN)·Yihong Tang, Andrew Robert Williams, Arjun Ashok, Vincent Zhihao Zheng, Lijun Sun, Alexandre Drouin, Issam H. Laradji, \'Etienne Marcotte, Valentina Zantedeschi·
arXiv:2605.27904v1 Announce Type: new Abstract: Time series forecasting in real-world settings often depends not only on historical observations, but also on external context that must be actively discovered from noisy, heterogeneous information sources. Yet existing context-aide…
arXiv:2605.28764v1 Announce Type: new Abstract: Vast quantities of compute (GPU cycles on personal workstations, idle inference servers, and edge devices between jobs) go unused because no incentive-aligned protocol exists for their owners to share them safely and profitably. Exi…
arXiv:2605.27575v1 Announce Type: new Abstract: As organizations move toward production deployments of AI agents, which execute non-deterministic workflows, maintain stateful sessions, and often operate with privileged access to internal services, the engineering challenge shifts…
arXiv:2605.27628v1 Announce Type: new Abstract: As autonomous and agentic AI systems scale in robotic and human-machine environments, managing hallucination and persistent but unjustified action remains an open challenge. Rather than attributing these failures solely to model or …
arXiv:2605.08678v2 Announce Type: replace Abstract: Modern AI progress has been driven by ML methods that are generalizable across settings and scalable to larger regimes. As large language models demonstrate advanced capabilities in reasoning, coding, and engineering tasks, it i…
Vast quantities of compute (GPU cycles on personal workstations, idle inference servers, and edge devices between jobs) go unused because no incentive-aligned protocol exists for their owners to share them safely and profitably. Existing approaches either require a trusted centra…
AI factories are token factories, converting power into intelligence in real time. And as agentic AI scales and autonomous, always-on special agents are deployed in the enterprise, performance per watt and cost per token become the economics that matter.
Existing AI evaluation practices often fail to capture how systems actually perform in low-resource environments, where operational constraints shape usability as much as model quality. Through a structured analysis of existing benchmark families across speech, chat/RAG, and visi…
arXiv cs.LG
TIER_1English(EN)·Vasilios A. Siris, Adamantia Stamou, George D. Stamoulis, Konstantinos Varsos, Ramin Khalili·
arXiv:2605.27309v1 Announce Type: new Abstract: The widespread use of AI services has raised concerns for its environmental sustainability, towards which recent studies have identified carbon emissions of AI inference as the major contributor. This paper introduces a framework fo…
arXiv:2602.22190v2 Announce Type: replace-cross Abstract: Open-source native GUI agents still lag behind closed-source systems on long-horizon navigation tasks. This gap stems from two limitations: a shortage of high-quality, action-aligned reasoning data, and the direct adoption…
arXiv:2605.26870v1 Announce Type: cross Abstract: Background: Large language models are typically evaluated as models, benchmarks, or short conversational episodes. Less is known about what happens when an agent is embedded persistently in a real academic research environment wit…
arXiv cs.AI
TIER_1English(EN)·Xue Qin, Simin Luan, John See, Zeyd Boukhers, Cong Yang, Zhijun Li·
arXiv:2604.08059v5 Announce Type: replace-cross Abstract: Software systems built from versioned AI components increasingly need lifecycle-time governance: when a capability module evolves into a new version, the hosting system must decide whether the new version may be activated …
arXiv:2605.26508v1 Announce Type: cross Abstract: We propose a foundational runtime actuarial layer for autonomous AI agents in which every side-effect-bearing action carries a time-consistent, counterfactual risk toll computed against a contractually fixed safe default, inside a…
arXiv:2605.26305v1 Announce Type: new Abstract: This paper details two novel frameworks for developing autonomous, agentic AI in scientific workflows. Both systems leverage a hybrid Local Body, Remote Brain architecture via Google Colab, utilizing Python-based local orchestrators…
Explainable AI (XAI) helps users interpret model behavior and identify potential faults. Agentic XAI systems use Large Language Models (LLMs) to make explanations more accessible through natural-language interaction, but they can also produce plausible yet unfaithful explanations…
As autonomous and agentic AI systems scale in robotic and human-machine environments, managing hallucination and persistent but unjustified action remains an open challenge. Rather than attributing these failures solely to model or alignment limitations, this paper explores the a…
As organizations move toward production deployments of AI agents, which execute non-deterministic workflows, maintain stateful sessions, and often operate with privileged access to internal services, the engineering challenge shifts from building individual agents to operating th…
The widespread use of AI services has raised concerns for its environmental sustainability, towards which recent studies have identified carbon emissions of AI inference as the major contributor. This paper introduces a framework for designing AI inference incentives based on the…
The widespread use of AI services has raised concerns for its environmental sustainability, towards which recent studies have identified carbon emissions of AI inference as the major contributor. This paper introduces a framework for designing AI inference incentives based on the…
arXiv cs.MA (Multiagent)
TIER_1English(EN)·Anas H. Alzahrani·
Background: Large language models are typically evaluated as models, benchmarks, or short conversational episodes. Less is known about what happens when an agent is embedded persistently in a real academic research environment with durable memory, local files, external tools, sch…
arXiv:2605.23951v1 Announce Type: new Abstract: The companion paper introduced a four-level verification lattice on agent-skill manifests (unverified, declared, tested, formal) and left the top level aspirational. This paper closes that gap. We give a precise semantics for skill …
arXiv:2605.13850v2 Announce Type: replace Abstract: Existing frameworks for LLM-based agent architectures describe systems from a single perspective: industry guides (Anthropic, Google, LangChain) focus on execution topology -- how data flows -- while cognitive science surveys fo…
arXiv cs.AI
TIER_1English(EN)·Wonjoong Kim, Sangwu Park, Yeonjun In, Sein Kim, Dongha Lee, Chanyoung Park·
arXiv:2605.23935v1 Announce Type: new Abstract: Autonomous agent systems fail not only due to incorrect decisions, but due to executing decisions whose authority no longer holds at runtime. Prior work defined Reconstructive Authority (RAM) as a condition for valid execution: acti…
arXiv cs.CL
TIER_1English(EN)·Vaishnavi Shrivastava, Piero Kauffmann, Ahmed Awadallah, Dimitris Papailiopoulos·
arXiv:2605.24517v1 Announce Type: cross Abstract: CLI agents are the closest thing language models have to an embodied setting: the model emits commands, the terminal executes them, and the returned stream -- stdout, errors, files, logs, and traces -- records the consequences. We…
arXiv cs.CL
TIER_1English(EN)·Junlin Wang, Federico Bianchi, Shang Zhu, Fan Nie, Yongchan Kwon, Bhuwan Dhingra, James Zou·
arXiv:2605.26079v1 Announce Type: new Abstract: Modern AI benchmarks operate at a complexity that outpaces traditional verification methods. Tasks authored by domain experts often contain implicit assumptions, incomplete environment specifications, and brittle evaluation logic th…
arXiv:2605.22634v2 Announce Type: replace-cross Abstract: Skills have become a practical packaging mechanism for agent instructions, workflows, scripts, and reference materials. In enterprise settings, however, a skill often needs to express more than task guidance: goals, input …
arXiv:2605.25632v1 Announce Type: new Abstract: Autonomous AI agents increasingly issue side-effect-bearing actions: database mutations, refunds, payments, external commitments. We propose the Actuarial Action Interface (AAI), a deterministic runtime contract that prices each suc…
arXiv:2605.25931v1 Announce Type: new Abstract: We systematically investigate all 25 public ARC-AGI-3 games and find that every one is reachable through non-intelligent strategies: 10 in a single blind step, 5 after one probing action, 1 via repeated ACTION1 presses, 1 via divers…
arXiv:2605.24785v1 Announce Type: new Abstract: Recent advances in multimodal web agents often rely on increased inference-time computation, including rollout search, verifier passes, offline skill discovery, and specialist model stacks. This raises a central question: can a web …
arXiv:2605.25624v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has driven breakthroughs in domains such as math, tool-use, and software engineering, yet its extension to computer-use agents (CUAs) has been bottlenecked by the scarcity of sca…
arXiv cs.AI
TIER_1English(EN)·Haolang Zhao, Yunbo Long, Lukas Beckenbauer, Alexandra Brintrup·
arXiv:2605.26081v1 Announce Type: new Abstract: Deep research agents face vast, interdependent, and pervasively uncertain information. Existing systems explore what evolving intermediate representations should look like, but leave their evolution to the LLM's implicit reasoning. …
arXiv:2605.26112v1 Announce Type: new Abstract: This paper studies the next major bottleneck in agentic AI as system scaling, not only model scaling: the design of auditable, persistent, modular, and verifiable architectures around foundation models. We refer to this shift as sca…
PANDO is a web agent framework that improves efficiency through experience accumulation by reducing redundant actions, optimizing skill discovery, and enhancing prompt caching without sacrificing performance.
A self-improving AI framework simultaneously updates both model weights and task-specific agent architecture through a language-model feedback agent across legal classification, GPU optimization, and biological data denoising tasks.
This paper studies the next major bottleneck in agentic AI as system scaling, not only model scaling: the design of auditable, persistent, modular, and verifiable architectures around foundation models. We refer to this shift as scaling the harness: treating the structured execut…
Deep research agents face vast, interdependent, and pervasively uncertain information. Existing systems explore what evolving intermediate representations should look like, but leave their evolution to the LLM's implicit reasoning. Without explicit regulation, the intermediate la…
Modern AI benchmarks operate at a complexity that outpaces traditional verification methods. Tasks authored by domain experts often contain implicit assumptions, incomplete environment specifications, and brittle evaluation logic that human annotation cannot reliably catch. We in…
We systematically investigate all 25 public ARC-AGI-3 games and find that every one is reachable through non-intelligent strategies: 10 in a single blind step, 5 after one probing action, 1 via repeated ACTION1 presses, 1 via diverse exploration, and 8 via single repeated actions…
We systematically investigate all 25 public ARC-AGI-3 games and find that every one is reachable through non-intelligent strategies: 10 in a single blind step, 5 after one probing action, 1 via repeated ACTION1 presses, 1 via diverse exploration, and 8 via single repeated actions…
arXiv:2605.22883v1 Announce Type: new Abstract: Current AI energy benchmarks measure consumption at the granularity of a single model invocation or training run. For classical single-turn workloads this unit remains coherent. For agentic systems - where a single user goal may tri…
arXiv cs.AI
TIER_1English(EN)·Federico Bottino, Carlo Ferrero, Nicholas Dosio, Pierfrancesco Beneventano·
arXiv:2604.11759v2 Announce Type: replace Abstract: Organizational knowledge used by AI agents typically lacks epistemic structure: retrieval systems surface semantically relevant content without distinguishing binding decisions from abandoned hypotheses, contested claims from se…
arXiv:2604.07813v2 Announce Type: replace Abstract: Learning theories have historically changed when the conditions of learning evolved. Generative and agentic AI create a new condition by allowing learners to delegate explanation, writing, problem solving, and other cognitive wo…
arXiv cs.AI
TIER_1English(EN)·Chitra Badagi, Divye Singh, Animesh Sen, Adinath Shirsath·
arXiv:2605.23459v1 Announce Type: cross Abstract: Enterprise AI systems, built on large language models, retrieval pipelines and autonomous agents, introduce a class of risks that traditional software quality assurance was never designed to address. These systems are probabilisti…
arXiv cs.AI
TIER_1English(EN)·Joshua Odmark, Gideon Rubin, Deon van der Vyver·
arXiv:2605.23058v1 Announce Type: cross Abstract: Empirical claims about autonomous Kubernetes operations agents are largely unfalsifiable. Published work reports observational results without controlled comparisons against an agent-disabled baseline, selection bias is endemic, p…
arXiv:2605.23904v1 Announce Type: new Abstract: Agent skills today are hand-crafted, generated one-shot, or evolved through loosely controlled self-revision, none of which behaves like a deep-learning optimizer for the skill, and none of which reliably improves over its starting …
arXiv:2605.23414v1 Announce Type: new Abstract: LLM-based multi-agent systems can fail even when planned actions are executed correctly because agents may misjudge their knowledge when evaluating plan feasibility, a phenomenon we term epistemic miscalibration in planning. Unlike …
arXiv:2605.23024v1 Announce Type: new Abstract: Large language models now write software, draft legal documents, and produce clinical notes, yet fundamental limits, from Turing and Arrow to the No Free Lunch theorems, shape what computation can do. This thesis turns such impossib…
arXiv:2605.22905v1 Announce Type: new Abstract: Self-evolving agents should not train on examples they cannot justify. Data-free self-evolving search agents offer a scalable route to systems that generate their own questions, answer them, and improve from their own feedback witho…
arXiv:2605.23179v1 Announce Type: new Abstract: Agentic AI orchestrators reduce the interface and assembly costs of composing information systems capabilities across organizational boundaries, seemingly accelerating modularization and organizational disaggregation. Yet AI-enabled…
RLVR framework for computer-use agents addresses data scarcity through scalable generation pipeline and synthetic environments, achieving superior performance on verification and transfer benchmarks.
Deliberative democracy arguably leads to better collective decisions, but is fundamentally constrained by human attention and bandwidth. While recent AI-mediated deliberations scale participation by synthesizing inputs from many humans, they remain time-intensive for individual u…
Deliberative democracy arguably leads to better collective decisions, but is fundamentally constrained by human attention and bandwidth. While recent AI-mediated deliberations scale participation by synthesizing inputs from many humans, they remain time-intensive for individual u…
Deliberative democracy arguably leads to better collective decisions, but is fundamentally constrained by human attention and bandwidth. While recent AI-mediated deliberations scale participation by synthesizing inputs from many humans, they remain time-intensive for individual u…
Environment cross-entropy hybrid objective combines policy-gradient loss with auxiliary environment observation prediction to provide dense supervision from terminal feedback, improving agent performance and self-improvement capabilities.
Physical AI systems face safety challenges where black-box models can execute harmful actions without detection, necessitating comprehensive runtime guardrail mechanisms for safe operation.
AI agents are entering high-risk production settings, where they use tools, retain context, follow policies, handle private data, and interact with users over multiple turns. Yet many evaluation methods still judge isolated outputs or static tasks, missing failures that emerge th…
Agent skills today are hand-crafted, generated one-shot, or evolved through loosely controlled self-revision, none of which behaves like a deep-learning optimizer for the skill, and none of which reliably improves over its starting point under feedback. We argue the skill should …
Enterprise AI systems, built on large language models, retrieval pipelines and autonomous agents, introduce a class of risks that traditional software quality assurance was never designed to address. These systems are probabilistic, context-sensitive and emergent: they cannot be …
LLM-based multi-agent systems can fail even when planned actions are executed correctly because agents may misjudge their knowledge when evaluating plan feasibility, a phenomenon we term epistemic miscalibration in planning. Unlike execution errors, epistemic miscalibration is la…
arXiv:2602.02660v3 Announce Type: replace Abstract: A critical bottleneck in automating AI research is the execution of complex machine learning engineering (MLE) tasks. MLE differs from general software engineering due to computationally expensive evaluation (e.g., model trainin…
arXiv:2512.23292v3 Announce Type: replace Abstract: The prevailing paradigm in AI for physical systems (scaling general-purpose foundation models toward universal multimodal reasoning) confronts a fundamental barrier at the control interface. Recent benchmarks show that even fron…
arXiv: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 cs.AI
TIER_1English(EN)·Aditya Taparia, Som Sagar, Ransalu Senanayake·
arXiv:2602.11574v3 Announce Type: replace Abstract: Configuring LLM-based agent systems involves choosing workflows, tools, token budgets, and prompts from a large combinatorial design space, and is typically handled today by fixed templates or hand-tuned heuristics that apply th…
arXiv:2605.20204v1 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_1English(EN)·Binghan Wu, Shoufeng Wang, Yunxin Liu, Ya-Qin Zhang, Joseph Sifakis, Ye Ouyang·
arXiv:2605.20608v1 Announce Type: new Abstract: Realizing Level 4/5 Autonomous Networks (AN) demands a shift from static automation to agent-native intelligence. Current operations, reliant on rigid scripts, lack the cognitive agency to handle off-nominal conditions. To address t…
arXiv: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 cs.CL
TIER_1English(EN)·Asaf Yehudai, Lilach Eden, Michal Shmueli-Scheuer·
arXiv:2605.22608v1 Announce Type: new Abstract: Agentic systems are becoming more capable: agents define strategies, take actions, and interact with different environments. This autonomy poses serious challenges for overseeing and assessing agent behavior. Most current tools are …
arXiv cs.CL
TIER_1English(EN)·Mingkai Deng, Jinyu Hou, Lara S\'a Neves, Varad Pimpalkhute, Taylor W. Killian, Zhengzhong Liu, Eric P. Xing·
arXiv:2605.22138v1 Announce Type: cross Abstract: How should an agent decide when and how to plan? A dominant approach builds agents as reactive policies with adaptive computation (e.g., chain-of-thought), trained end-to-end expecting planning to emerge implicitly. Without contro…
arXiv: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:2605.10787v2 Announce Type: replace Abstract: Current LLM agents are proficient at calling isolated APIs but struggle with the "last mile" of commercial software automation. In real-world scenarios, tools are not independent; they are atomic, interdependent, and prone to en…
arXiv:2605.20456v1 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:2605.22794v1 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: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 cs.LG
TIER_1English(EN)·Simon Dennis, Rivaan Patil, Kevin Shabahang, Hao Guo·
arXiv:2605.22502v1 Announce Type: cross Abstract: Agent orchestration frameworks have proliferated, collectively exceeding 290,000 GitHub stars across LangGraph, CrewAI, Google ADK, OpenAI Agents SDK, Semantic Kernel, Strands, and LlamaIndex. All follow the same pattern: an exter…
arXiv cs.LG
TIER_1English(EN)·Fiona Y. Wong, Markus J. Buehler·
arXiv:2605.22300v1 Announce Type: cross Abstract: Scientific evidence often spans instruments, databases, and disciplines, so no single source records the full phenomenon. This makes it difficult to determine when coordinated AI agents add value over simpler scientific workflows.…
arXiv: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.AI
TIER_1English(EN)·Lucas Jing, Xinqi Wang, Liao Zhang, Simon S. Du·
arXiv:2605.15229v2 Announce Type: replace-cross Abstract: Existing code benchmarks measure whether an agent can produce any test that reproduces a known bug, or whether it can produce a patch that fixes a described issue. Neither isolates the distinct skill of property-based test…
arXiv cs.AI
TIER_1English(EN)·Lujain Ibrahim, Katherine M. Collins, Sunnie S. Y. Kim, Anka Reuel, Max Lamparth, Kevin Feng, Lama Ahmad, Prajna Soni, Alia El Kattan, Merlin Stein, Siddharth Swaroop, Vishakh Padmakumar, Ilia Sucholutsky, Andrew Strait, Diyi Yang, Q. Vera Liao, Umang Bh…·
arXiv:2509.08010v2 Announce Type: replace-cross Abstract: Large language models (LLMs) distinguish themselves from previous technologies by functioning as collaborative ``thought partners,'' capable of engaging more fluidly in natural language on a range of tasks. As LLMs increas…
SkillOpt introduces a systematic text-space optimizer for agent skills that trains skills as external agent state with stable updates and zero deployment inference overhead, achieving superior performance across multiple benchmarks and execution environments.
Large language models now write software, draft legal documents, and produce clinical notes, yet fundamental limits, from Turing and Arrow to the No Free Lunch theorems, shape what computation can do. This thesis turns such impossibility results from curiosities into design rules…
Autonomous agentic systems are largely static after deployment: they do not learn from user interactions, and recurring failures persist until the next human-driven update ships a fix. Self-evolving agents have emerged in response, but all confine evolution to text-mutable artifa…
Self-evolving agents should not train on examples they cannot justify. Data-free self-evolving search agents offer a scalable route to systems that generate their own questions, answer them, and improve from their own feedback without human annotations. Yet, without verifiable ev…
LLM-powered AI agents require high-frequency state exploration (e.g., test-time tree search and reinforcement learning), relying on rapid checkpoint and rollback (C/R) of the complete sandbox state, including files and process state (e.g., memory, contexts, etc.). Existing mechan…
AI models are already deployed in societies affected by armed conflict, and journalists, humanitarian workers, governments and ordinary citizens rely on them for information or for their work processes. No established practice exists for checking whether their outputs can make th…
We present Claw AI Lab, a lab-native autonomous research platform that advances automated research from a hidden prompt-to-paper pipeline into an interactive AI laboratory. Rather than centering the system around a single agent or a fixed serial workflow, we allow users to instan…
Skills are increasingly used to package agent instructions, workflows, scripts, and reference materials. In enterprise settings, however, skills often need to express more than task guidance: they must make goals, input boundaries, permissions, evidence requirements, output contr…
Agentic systems are becoming more capable: agents define strategies, take actions, and interact with different environments. This autonomy poses serious challenges for overseeing and assessing agent behavior. Most current tools are limited, focusing on observability with basic ev…
We introduce TerminalWorld, a scalable data engine that automatically reverse-engineers high-fidelity evaluation tasks from "in-the-wild" terminal recordings. Processing 80,870 terminal recordings, the engine yields a full benchmark of 1,530 validated tasks, spanning 18 real-worl…
Agent orchestration frameworks have proliferated, collectively exceeding 290,000 GitHub stars across LangGraph, CrewAI, Google ADK, OpenAI Agents SDK, Semantic Kernel, Strands, and LlamaIndex. All follow the same pattern: an external orchestrator above the LLM, injecting instruct…
Don't Worry About the Vase (Zvi Mowshowitz)
TIER_1English(EN)·Zvi Mowshowitz·
Scientific evidence often spans instruments, databases, and disciplines, so no single source records the full phenomenon. This makes it difficult to determine when coordinated AI agents add value over simpler scientific workflows. We evaluate this question with a cross-domain ben…
How should an agent decide when and how to plan? A dominant approach builds agents as reactive policies with adaptive computation (e.g., chain-of-thought), trained end-to-end expecting planning to emerge implicitly. Without control over the presence, structure, or horizon of plan…
Recent development of agents has renewed demand for long-context reasoning capacity of LLMs. However, training LLMs for this capacity requires costly long-document curation or heuristic context synthesis. We observe that agents produce massive trajectories when solving problems, …
Efficient agentic reasoning requires decomposing decision-making into three systems—simulative reasoning, self-regulation, and reactive execution—enabling controlled planning that reduces token usage while maintaining performance.
Complex Verilog Design Problems (CVDP) challenge hardware LLM agents because solving them requires localizing verifier-relevant RTL, testbenches, include paths, and build dependencies inside large repository snapshots, making precise edits, and recovering from sparse hidden-verif…
LLM agents have shown strong performance across a wide range of complex tasks, including interactive environments that require long-horizon decision making. But these agents cannot learn on the fly at test time. Self-evolving agents address this by accumulating memory and reflect…
Terminal agents extend Large Language Models with the ability to execute tasks directly in command-line environments, but their progress is bottlenecked by the scarcity of high-quality training data. Existing approaches bootstrap from partial sources such as human-defined seeds o…
Realizing Level 4/5 Autonomous Networks (AN) demands a shift from static automation to agent-native intelligence. Current operations, reliant on rigid scripts, lack the cognitive agency to handle off-nominal conditions. To address this, this letter proposes a hierarchical multi-a…
Realizing Level 4/5 Autonomous Networks (AN) demands a shift from static automation to agent-native intelligence. Current operations, reliant on rigid scripts, lack the cognitive agency to handle off-nominal conditions. To address this, this letter proposes a hierarchical multi-a…
Large language model agents now act on codebases, browsers, operating systems, calendars, files, and tool ecosystems, but the benchmarks used to evaluate them are fragmented: each emphasizes a different unit of measurement (final task success, tool-call validity, repeated-pass co…
Agentic AI coding systems can inspect repositories, plan implementation steps, edit files, call tools, run tests, and submit pull requests. These capabilities make software and hardware development faster in some settings, but current evidence does not support the simple claim th…
Production LLM agents combine stochastic model outputs with deterministic software systems, yet the boundary between the two is rarely treated as a first-class architectural object. This paper names that boundary the stochastic-deterministic boundary (SDB): a four-part contract a…
We adapt split conformal prediction and adaptive conformal inference (ACI) to continuous AI agent evaluation, providing distribution-free coverage guarantees for forecasted quality scores. Conformal intervals achieve calibration error below 0.02 across all nominal levels at the 2…
We present OpenComputer, a verifier-grounded framework for constructing verifiable software worlds for computer-use agents. OpenComputer integrates four components: (1) app-specific state verifiers that expose structured inspection endpoints over real applications, (2) a self-evo…
Large Language Model (LLM) agents are increasingly applied to engineering design tasks, yet existing evaluation frameworks do not adequately address multi-agent systems that combine simulation, retrieval, and manufacturing preparation. We introduce a benchmark suite with three ev…
Equipping LLMs with tool-use capabilities via Agentic Reinforcement Learning (Agentic RL) is bottlenecked by two challenges: the lack of scalable, robust execution environments and the scarcity of realistic training data that captures implicit human reasoning. Existing approaches…
As LLM agents are increasingly built around reusable skills, a central challenge is no longer only whether agents can use provided skills, but whether they can generate correct, reusable, and executable skills from repositories and documents. Existing benchmarks primarily evaluat…
arXiv cs.AI
TIER_1English(EN)·Ronaldo Martins da Costa·
Legacy systems concentrate business rules, architectural decisions, and operational exceptions that often remain implicit in code, data, configuration, and maintenance practices. At the same time, language-model-based coding agents depend on reliable context, correctness criteria…
AI-assisted research is crossing a threshold: fully automated systems can now generate research papers for as little as $15, while long-horizon agents can execute experiments, draft manuscripts, and simulate critique with minimal human input. Yet this productivity frontier expose…
The bottleneck of useful agentic intelligence has shifted from compressing world knowledge into a single model to executing a coordinated system. This position paper argues that personal-agent architecture must move to the edge because the core properties of agentic intelligence …
Long-horizon LLM agents leave traces that could become reusable experience, but raw trajectories are noisy and hard to govern. We treat Agent Skills as an experience schema that couples executable scripts, with non-executable guidance on procedures. Yet open skill ecosystems cont…
Generalizable agents should adapt to diverse tasks and unseen environments beyond their training distribution. This position paper argues that such generalization requires environment scaling: expanding the distribution of executable rule-sets that agents interact with, rather th…
Deploying large language model (LLM) on edge device enables personalized LLM agents for various users. The growing availability of diverse personalized agents presents a unique opportunity for peer-to-peer (P2P) collaboration, wherein each user can delegate tasks beyond the local…
Deploying large language model (LLM) on edge device enables personalized LLM agents for various users. The growing availability of diverse personalized agents presents a unique opportunity for peer-to-peer (P2P) collaboration, wherein each user can delegate tasks beyond the local…
Multi-agent LLM workflows -- systems composed of multiple role-specific LLM calls -- often outperform single-prompt baselines, but they remain difficult to debug and refine. Failures can originate from subtle errors in intermediate outputs that propagate to downstream nodes, requ…
Although artificial intelligence (AI) now matches or exceeds human performance across numerous cognitive tasks, creativity remains a highly contested frontier. As AI systems based on large language models (LLMs) are increasingly adopted in research and innovation, it is essential…
Large language model (LLM)-based agents have demonstrated strong capabilities in complex reasoning and problem solving through multi-step interactions, yet most deployed agents remain behaviorally static, with knowledge acquired during execution rarely translating into systematic…
Agentic AI is rapidly proliferating across diverse real-world domains such as software engineering, yet public trust has not kept pace. The central reason is that responsibility, despite being widely discussed, remains a subjective and unenforced concept, as no current agentic fr…
arXiv cs.LG
TIER_1English(EN)·Sheila A. McIlraith·
We examine one particular dimension of AI governance: how to monitor and audit AI-enabled products and services throughout the AI development lifecycle, from pre-deployment testing to post-deployment auditing. Combining principles from formal methods with SoTA machine learning, w…
Large language model based agents often fail in unfamiliar environments due to premature exploitation: a tendency to act on prior knowledge before acquiring sufficient environment-specific information. We identify autonomous exploration as a critical yet underexplored capability …
Machine learning systems increasingly make life-changing decisions about individuals, such as loan approvals, hiring, and cheating detection, raising a pressing question: how can individuals respond to negative decisions made by these opaque systems? While explainable artificial …
AI agents are increasingly deployed to act autonomously in the world, yet there is still no reliable way to trace a harmful agent back to the account that deployed it. This creates the same accountability gap across both ends of the intent spectrum: benign operators may deploy mi…
Toward recursive self-improvement, we investigate LLM agents autonomously designing foundation models beyond standard Transformers. We introduce a dual-framework approach: AIRA-Compose for high-level architecture search, and AIRA-Design for low-level mechanistic implementation. A…
Coding agents are increasingly deployed in real software development, where a single version iteration requires months of coordinated work across many files. However, most existing benchmarks focus predominantly on single-issue bug fixes from Python repositories, with coarse pass…
Recent advances in Large Language Model (LLM) agents have enabled complex agentic workflows where models autonomously retrieve information, call tools, and reason over large corpora to complete tasks on behalf of users. Despite the growing adoption of retrieval-augmented generati…
Recent advances in Large Language Model (LLM) agents have enabled complex agentic workflows where models autonomously retrieve information, call tools, and reason over large corpora to complete tasks on behalf of users. Despite the growing adoption of retrieval-augmented generati…
Autonomous multi-agent systems based on large language models (LLMs) have demonstrated remarkable abilities in independently solving complex tasks in a wide breadth of application domains. However, these systems hit critical reasoning, coordination, and computational scaling bott…
Agentic modeling aims to transform LLMs into autonomous agents capable of solving complex tasks through planning, reasoning, tool use, and multi-turn interaction with environments. Despite major investment, open research remains constrained by infrastructure and training gaps. Ma…
GraphFlow is a visual workflow system designed to improve the reliability of agentic AI automation in multi-step, mission-critical processes. In these workflows, small errors compound rapidly: under an idealized model of independent steps, a ten-step process with 90% per-step rel…
AI agents execute complex multi-step processes, but current evaluation falls short: outcome metrics report success or failure without explaining why, and process-level approaches struggle to connect failure types to their precise locations within long, structured traces. We prese…
AI agents execute complex multi-step processes, but current evaluation falls short: outcome metrics report success or failure without explaining why, and process-level approaches struggle to connect failure types to their precise locations within long, structured traces. We prese…
MediaClaw is a multimodal agent platform built on the OpenClaw ecosystem. Its core design follows a three-layer architecture of unified abstraction, pluginized extension, and workflow orchestration. The system is intended to address practical deployment pain points in AIGC adopti…
ReAct has become the default architecture across LLM agents, and many existing web agents follow this paradigm. We argue that it is the wrong default for web agents. Instead, web agents should default to plan-then-execute: commit to a task-specific program before observing runtim…
Agentic evolution has emerged as a powerful paradigm for improving programs, workflows, and scientific solutions by iteratively generating candidates, evaluating them, and using feedback to guide future search. However, existing methods are typically instantiated either as fixed …
Foundation models have transformed automated code generation, yet autonomous software-engineering agents remain unreliable in realistic development settings. The dominant explanation locates this gap in model capability. We propose a different locus: software-engineering capabili…
Current interactive LLM agents rely on goal-conditioned stepwise planning, where environmental understanding is acquired reactively during execution rather than established beforehand. This temporal inversion leads to Delayed Environmental Perception: agents must infer environmen…
Agent benchmarks have become the de facto measure of frontier AI competence, guiding model selection, investment, and deployment. However, reward hacking, where agents maximize a score without performing the intended task, emerges spontaneously in frontier models without overfitt…
Computer Use Agents (CUAs) can act through both atomic GUI actions, such as click and type, and high-level tool calls, such as API-based file operations, but this hybrid action space often leaves them uncertain about when to continue with GUI actions or switch to tools, leading t…
Modern GUI agents typically rely on a model-centric and step-wise interaction paradigm, where LLMs must re-interpret the UI and re-decide actions at every screen, which is fragile in long-horizon tasks. In this paper, we propose Executable Agentic Memory (EAM), a structured Knowl…
Large language model (LLM) agents have increasingly advanced service applications, such as booking flight tickets. However, these service agents suffer from unreliability in long-horizon tasks, as they often produce policy violations, tool hallucinations, and misaligned actions, …
Terminal agents are increasingly capable of executing complex, long-horizon tasks autonomously from a single user prompt. To do so, they must interpret instructions encountered in the environment (e.g., README files, code comments, stack traces) and determine their relevance to t…
arXiv cs.AI
TIER_1English(EN)·Stefano V. Albrecht·
Reproducibility problems that have long affected machine learning and reinforcement learning are now surfacing in agent research: papers compare systems by reported scores while leaving the rollout records behind those scores difficult to inspect. For agentic tasks, this matters …
Deploying agentic AI in regulated contexts requires principled reasoning about two design dimensions: agency (what the system can do) and autonomy (how much it acts without human involvement). Though often treated independently, they are coupled: at higher autonomy, human error c…
Reusable skills are becoming a common interface for extending large language model agents, packaging procedural guidance with access to files, tools, memory, and execution environments. However, this modularity introduces attack surfaces that are largely missed by existing safety…
In this paper, we present AgentDisCo, a novel Disentangled and Collaborative agentic architecture that formulates deep research as an adversarial optimization problem between information exploration and exploitation. Unlike existing approaches that conflate these two processes in…
We introduce Shepherd, a functional programming model that formalizes meta-agent operations on target agents as functions, with core operations mechanized in Lean. Shepherd records every agent-environment interaction as a typed event in a Git-like execution trace, enabling any pa…
Large language and vision-language models increasingly power agents that act on a user's behalf through command-line interface (CLI) harnesses. However, most agent benchmarks still rely on synthetic sandboxes, short-horizon tasks, mock-service APIs, and final-answer checks, leavi…
The dominant paradigm for AI agents is an "on-the-fly" loop in which agents synthesize plans and execute actions within seconds or minutes in response to user prompts. We argue that this paradigm short-circuits disciplined software engineering (SE) processes -- iterative design, …
LLMs are increasingly deployed as autonomous agents with access to tools, databases, and external services, yet practitioners (across different sectors) lack systematic methods to assess how known threat classes translate into concrete risks within a specific agentic deployment. …
Artificial intelligence safety research focuses on aligning individual language models with human values, yet deployed AI systems increasingly operate as interacting populations where social influence may override individual alignment. Here we show that populations of individuall…
Benchmarks for coding agents increasingly measure source-level software repair, and cybersecurity benchmarks increasingly measure broad capture-the-flag performance. Classical binary reverse engineering remains less precisely specified: given only an executable, can an agent reco…
Agent-compiled knowledge bases provide persistent external knowledge for large language model (LLM) agents in open-ended, knowledge-intensive downstream tasks. Yet their quality is systematically limited by \emph{incompleteness}, \emph{incorrectness}, and \emph{redundancy}, manif…
Current large language model agent frameworks prioritize autonomy but lack the governability mechanisms required for enterprise deployment. High-risk write operations proceed without independent review, complex tasks lack acceptance verification, and computational resources are a…
Large Language Model (LLM)-based agents (e.g., OpenClaw) increasingly rely on reusable skill libraries to solve artifact-rich tasks such as document-centric workflows and data-intensive analysis. As these libraries grow, a few works have attempted to study the Retrieval-Augmented…
In this paper, we describe early work on a specification inference tool for the Move Prover that combines a weakest-precondition (WP) analysis over Move bytecode with an agentic coding CLI such as Claude Code. Specification inference reduces the boilerplate of writing specificati…
We present TraceFix, a verification-first pipeline for Large Language Model (LLM) multi-agent coordination. An agent synthesizes a protocol topology as a structured intermediate representation (IR) from a task description, generates PlusCal coordination logic, and iteratively rep…
We present Agentic Decentralized Knowledge Optimization (ADKO), a framework for collaborative black-box optimization across autonomous agents that achieves sample efficiency, privacy preservation, heterogeneous-objective handling, and communication efficiency. Each agent maintain…
Tool-integrated reasoning (TIR) is difficult to scale to small language models due to instability in long-horizon tool interactions and limited model capacity. While reinforcement learning methods like group relative policy optimization provide only sparse outcome-level rewards. …
While explicit reasoning trajectories enhance model interpretability, existing paradigms often rely on monolithic chains that lack intermediate verification, allowing early errors to cascade unchecked. This lack of modularity impedes granular auditing and compromises the epistemi…
arXiv cs.CL
TIER_1English(EN)·Xinglin Wang, Zishen Liu, Shaoxiong Feng, Peiwen Yuan, Yiwei Li, Jiayi Shi, Yueqi Zhang, Chuyi Tan, Ji Zhang, Boyuan Pan, Yao Hu, Kan Li·
arXiv:2605.06110v1 Announce Type: cross Abstract: Agentic systems increasingly solve complex user requests by executing orchestrated workflows, where subtasks are assigned to specialized models or tools and coordinated according to their dependencies. While recent work improves a…
arXiv: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: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:2605.05980v1 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:2605.06230v1 Announce Type: new Abstract: As large models evolve from conversational assistants into autonomous agents, challenges increasingly arise from long-horizon decision making, tool use, and real environment interaction. Existing agenticinfrastructure remain fragmen…
arXiv:2605.06365v1 Announce Type: new Abstract: Large language model systems are increasingly deployed as agentic workflows that interleave reasoning, tool use, memory, and iterative refinement. These systems are effective at producing answers, but they often rely on implicit con…
arXiv cs.CL
TIER_1English(EN)·Erhan Zhang, Yiqun Chen, Zechun Niu, Wei Yang, Xiaochi Wei, Yan Gao, Yi Wu, Yao Hu, Jiaxin Mao·
arXiv:2604.03675v1 Announce Type: cross Abstract: In agentic search, large language models (LLMs) are trained to perform multi-turn retrieval and reasoning for complex tasks such as multi-hop question answering (QA). However, current search-based Reinforcement Learning (RL) metho…
arXiv:2605.06434v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) have enabled workflows that generate SystemVerilog Assertions (SVAs) from natural-language specifications, with the potential to accelerate Formal Verification (FV). However, high-qual…
arXiv:2605.05400v1 Announce Type: cross Abstract: The rapid adoption of AI coding agents has produced a dominant workflow pattern -- often called "vibe coding" -- that prioritizes speed of implementation over deliberate preparation. We argue that this approach creates a systemati…
arXiv: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:2605.06522v1 Announce Type: new Abstract: Foundation models (FMs) are increasingly deployed in open-world settings where distribution shift is the rule rather than the exception. The out-of-distribution (OOD) phenomena they face -- knowledge boundaries, capability ceilings,…
arXiv:2605.06472v1 Announce Type: new Abstract: LLM-based workflows compose specialized agents to execute complex tasks, and these agents usually share substantial context, allowing KV-Cache reuse to save computation. Existing approaches either manage KV-Cache at agent level and …
arXiv cs.LG
TIER_1English(EN)·Bole Ma, Jan Eitzinger, Harald K\"ostler·
arXiv:2605.05696v1 Announce Type: cross Abstract: Agentic LLM workloads put bit-identical tokens at shifted positions every turn, voiding prefix caches at the first byte of divergence. Operators report cache-hit regressions ranging from moderate slowdowns to severe TTFT spikes of…
arXiv cs.AI
TIER_1English(EN)·Wentao Zhang, Zhe Zhao, Haibin Wen, Yingcheng Wu, Cankun Guo, Ming Yin, Bo An, Mengdi Wang·
arXiv:2604.15034v3 Announce Type: replace Abstract: Recent advances in LLM based agent systems have shown promise in tackling complex, long horizon tasks. However, existing agent protocols (e.g., A2A and MCP) under specify cross entity lifecycle and context management, version tr…
arXiv cs.AI
TIER_1English(EN)·Xi-Wei Pan, Shi-Wen An, Jin-Guo Liu·
arXiv:2604.11535v2 Announce Type: replace Abstract: Solving an NP-hard optimization problem often requires reformulating it for a specific solver -- quantum hardware, a commercial optimizer, or a domain heuristic. A tool for polynomial-time reductions between hard problems would …
arXiv:2603.13131v2 Announce Type: replace Abstract: Long-horizon embodied intelligence requires agents to improve through interaction, not merely to execute plans generated from static goals. A central challenge is therefore to transform past executions into knowledge that can sh…
arXiv cs.AI
TIER_1English(EN)·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 …
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_1English(EN)·Yipeng Ouyang, Yi Xiao, Yuhao Gu, Xianwei Zhang·
arXiv:2605.03353v1 Announce Type: cross Abstract: LLM-Agents have evolved into autonomous systems for complex task execution, with the SKILL.md specification emerging as a de facto standard for encapsulating agent capabilities. However, a critical bottleneck remains: different ag…
arXiv:2605.03952v1 Announce Type: cross Abstract: Coding agents often pass per-prompt safety review yet ship exploitable code when their tasks are decomposed into routine engineering tickets. The challenge is structural: existing safety alignment evaluates overt requests in isola…
arXiv:2604.14709v3 Announce Type: replace Abstract: Existing benchmarks for hardware design primarily evaluate Large Language Models (LLMs) on isolated, component-level tasks such as generating HDL modules from specifications, leaving repository-scale evaluation unaddressed. We i…
arXiv: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_1English(EN)·Xue Qin, Simin Luan, John See, Cong Yang, Zhijun Li·
arXiv:2604.07039v2 Announce Type: replace-cross Abstract: Robotic systems lack a principled abstraction for organizing intelligence, capabilities, and execution in a unified manner. Existing approaches either couple skills within monolithic architectures or decompose functionalit…
arXiv:2605.03675v1 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:2605.03195v1 Announce Type: new Abstract: Modern coding agents increasingly delegate specialized subtasks to subagents, which are smaller, focused agentic loops that handle narrow responsibilities like search, debugging or terminal execution. This architectural pattern keep…
arXiv cs.AI
TIER_1English(EN)·Reshabh K Sharma, Gaurav Mittal, Yu Hu·
arXiv:2605.03159v1 Announce Type: new Abstract: As autonomous agents become increasingly sophisticated, validating their sequential behavior presents a significant challenge. Traditional testing approaches require manual specification, exact sequence matching, or thousands of tra…
arXiv:2604.01496v2 Announce Type: replace-cross Abstract: We introduce SWE-ZERO to SWE-HERO, a two-stage SFT recipe that achieves state-of-the-art results on SWE-bench by distilling open-weight frontier LLMs. Our pipeline replaces resource-heavy dependencies with an evolutionary …
arXiv:2605.04107v1 Announce Type: cross Abstract: Production agent frameworks (OpenAI Function Calling, Anthropic Tool Use, MCP) transmit tool schemas as JSON, a format designed for machine parsing, not for interpretation by language models. For small models (4B-14B), this protoc…
arXiv cs.AI
TIER_1English(EN)·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 cs.AI
TIER_1English(EN)·Kishan Athrey, Ramin Pishehvar, Brian Riordan, Mahesh Viswanathan·
arXiv:2605.03986v1 Announce Type: new Abstract: Multi-Agent Systems (MAS) built using AI agents fulfill a variety of user intents that may be used to design and build a family of related applications. However, the creation of such MAS currently involves manual composition of the …
arXiv cs.AI
TIER_1English(EN)·Raja Sekhar Rao Dheekonda, Will Pearce, Nick Landers·
arXiv:2605.04019v1 Announce Type: new Abstract: AI systems are entering critical domains like healthcare, finance, and defense, yet remain vulnerable to adversarial attacks. While AI red teaming is a primary defense, current approaches force operators into manual, library-specifi…
arXiv cs.AI
TIER_1English(EN)·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: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…
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: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 cs.CL
TIER_1English(EN)·Hung Tran, Langston Nashold, Rayan Krishnan, Antoine Bigeard, Alex Gu·
arXiv:2603.04601v2 Announce Type: replace-cross Abstract: Code generation has emerged as one of AI's highest-impact use cases, yet existing benchmarks measure isolated tasks rather than the complete "zero-to-one" process of building a working application from scratch. We introduc…
arXiv cs.AI
TIER_1English(EN)·Zhensu Sun, Haotian Zhu, Bowen Xu, Xiaoning Du, Li Li, David Lo·
arXiv:2408.01055v2 Announce Type: replace-cross Abstract: Self-healing systems have long been a focus of research, aiming to enable software to recover from unexpected runtime errors without human intervention. Traditional approaches rely on predefined heuristic rules, such as re…
arXiv: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_1English(EN)·Bowen Ye, Rang Li, Qibin Yang, Yuanxin Liu, Linli Yao, Hanglong Lv, Zhihui Xie, Chenxin An, Lei Li, Lingpeng Kong, Qi Liu, Zhifang Sui, Tong Yang·
arXiv:2604.06132v2 Announce Type: replace Abstract: Large language models are increasingly deployed as autonomous agents for multi-step workflows in real-world software environments. However, existing agent benchmarks are limited by trajectory-opaque grading, underspecified safet…
arXiv: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: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 cs.AI
TIER_1English(EN)·Yuecai Zhu, Nikolaos Tsantalis, Peter C. Rigby·
arXiv:2605.02741v1 Announce Type: cross Abstract: The promise of Large Language Models in automated software engineering is often measured by functional correctness, overlooking the critical issue of long term maintainability. This paper presents a systematic audit of technical d…
arXiv: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: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:2605.01740v1 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:2605.01394v1 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:2605.02728v1 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 cs.AI
TIER_1English(EN)·Vincent Henkel, Felix Gehlhoff, David Kube, Asaad Almutareb, Luis Cruz, Bernd Hellingrath, Philip Koch, Christoph Legat, Florian Mohr, Michael Oberle, Felix Ocker, Thorsten Schoeler, Mario Thron, Nico Andre T\"opfer, Lucas Vogt, Yuchen Xia·
arXiv:2605.02592v1 Announce Type: new Abstract: Foundation models, particularly large language models, are increasingly integrated into agent architectures for industrial tasks such as decision support, process monitoring, and engineering automation. Yet evidence on their purpose…
arXiv:2605.02503v1 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_1Nederlands(NL)·Qisong Zhang (School of Artificial Intelligence, Beijing University of Posts and Telecommunications), Wenzhuo Wu (School of Artificial Intelligence, Beijing University of Posts and Telecommunications), Zhuangzhuang Jia (School of Artificial Intelligence, ·
arXiv:2605.01789v1 Announce Type: new Abstract: Constructing controllable visual data is a major bottleneck for image editing and multimodal understanding. Useful supervision is rarely produced by a single rendering pass; instead it emerges through iterative generation, inspectio…
arXiv cs.AI
TIER_1English(EN)·Florian Valentin Wunderlich, Lars Benedikt Kaesberg, Jan Philip Wahle, Terry Ruas, Bela Gipp·
arXiv:2605.01566v1 Announce Type: new Abstract: Advances in inference methods have enabled language models to improve their predictions without additional training. These methods often prioritize raw performance over cost-effective compute usage. However, computational efficiency…
arXiv: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: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: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 cs.CL
TIER_1English(EN)·Yuwen Du, Rui Ye, Shuo Tang, Keduan Huang, Xinyu Zhu, Yuzhu Cai, Siheng Chen·
arXiv:2605.04036v1 Announce Type: cross Abstract: Deep search capabilities have become an indispensable competency for frontier Large Language Model (LLM) agents, yet their development remains dominated by industrial giants. The typical industry recipe involves a highly resource-…
arXiv: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.LG
TIER_1English(EN)·Chandan Singh, Yan Shuo Tan, Weijia Xu, Zelalem Gero, Weiwei Yang, Michel Galley, Jianfeng Gao·
arXiv:2605.03808v1 Announce Type: cross Abstract: Agentic data science (ADS) systems are rapidly improving their capability to autonomously analyze, fit, and interpret data, potentially moving towards a future where agents conduct the vast majority of data-science work. However, …
arXiv: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: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:2605.02964v1 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: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 cs.AI
TIER_1English(EN)·Jia Li, Yuxin Su, Michael R. Lyu·
arXiv:2601.03731v3 Announce Type: replace-cross Abstract: As large language models (LLMs) evolve into autonomous agents, evaluating repository-level reasoning, the ability to maintain logical consistency across massive, real-world, interdependent file systems, has become critical…
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: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: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:2602.05353v3 Announce Type: replace-cross Abstract: Large Language Models have shown strong capabilities in complex problem solving, yet many agentic systems remain difficult to interpret and control due to opaque internal workflows. While some frameworks offer explicit arc…
arXiv cs.AI
TIER_1English(EN)·Bin Lei, Weitai Kang, Zijian Zhang, Winson Chen, Xi Xie, Shan Zuo, Mimi Xie, Ali Payani, Mingyi Hong, Yan Yan, Caiwen Ding·
arXiv:2505.10887v3 Announce Type: replace Abstract: This paper introduces \textsc{InfantAgent-Next}, a generalist agent capable of interacting with computers in a multimodal manner, encompassing text, images, audio, and video. Unlike existing approaches that either build intricat…
arXiv:2605.00424v1 Announce Type: cross Abstract: Agent skills -- structured packages of instructions, scripts, and references that augment a large language model (LLM) without modifying the model itself -- have moved from convenience to first-class deployment artifact. The runti…
arXiv cs.LG
TIER_1English(EN)·Kyle Zheng, Han Zhang, Renliang Sun, Chenchen Ye, Wei Wang·
arXiv:2605.02411v1 Announce Type: cross Abstract: A semantic gap separates how users describe tasks from how tools are documented. As API ecosystems scale to tens of thousands of endpoints, static retrieval from the initial query alone cannot bridge this gap: the agent's understa…
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:2603.25719v2 Announce Type: replace-cross Abstract: We present an empirical study of how far general-purpose coding agents -- without hardware-specific training -- can optimize hardware designs from high-level algorithmic specifications. We introduce an agent factory, a two…
arXiv cs.LG
TIER_1English(EN)·Jan Ole Ernst, Dmitri Michelangelo Saberi, Derek Christ, Thomas Zimmermann, Rajath Salegame, Suhaas M. Bhat, Stanislav Levental, Thomas Dybdahl Ahle, Matthias Jung·
arXiv:2605.00058v1 Announce Type: cross Abstract: The primary goal of Design Verification (DV) is to ensure that a proposed chip design implementation (either in code, or physical form) exactly matches its specification and is free of functional errors in order to avoid costly re…
arXiv cs.LG
TIER_1English(EN)·Dongxin Guo, Jikun Wu, Siu Ming Yiu·
arXiv:2605.00528v1 Announce Type: cross Abstract: AI agents execute tens to hundreds of chained LLM calls per task, yet GPU schedulers treat each call as independent, discarding gigabytes of intermediate state between steps and inflating end-to-end latency by 3-8x. We argue that …
arXiv cs.LG
TIER_1English(EN)·Zexi Liu, Jingyi Chai, Xinyu Zhu, Shuo Tang, Rui Ye, Bo Zhang, Lei Bai, Siheng Chen·
arXiv:2505.23723v2 Announce Type: replace-cross Abstract: The emergence of large language model (LLM)-based agents has significantly advanced the development of autonomous machine learning (ML) engineering. However, the dominant prompt-based paradigm exhibits limitations: smaller…
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:2604.09718v2 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 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:2604.28138v1 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: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_1English(EN)·Chenxin Li, Zhengyang Tang, Huangxin Lin, Yunlong Lin, Shijue Huang, Shengyuan Liu, Bowen Ye, Rang Li, Lei Li, Benyou Wang, Yixuan Yuan·
arXiv:2604.28139v1 Announce Type: cross Abstract: LLM agents are expected to complete end-to-end units of work across software tools, business services, and local workspaces. Yet many agent benchmarks freeze a curated task set at release time and grade mainly the final response, …
arXiv cs.AI
TIER_1English(EN)·Simon Dennis, Michael Diamond, Rivaan Patil, Kevin Shabahang, Hao Guo·
arXiv:2604.27891v1 Announce Type: new Abstract: Agent orchestration frameworks -- LangGraph, CrewAI, Google ADK, OpenAI Agents SDK, and others -- place an external orchestrator above the LLM, tracking state and injecting routing instructions at every turn. We present a controlled…
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_1English(EN)·Tarlan Hasanli, Shahbaz Siddeeq, Bishwash Khanal, Pyry Kotilainen, Tommi Mikkonen, Pekka Abrahamsson·
arXiv:2604.26615v1 Announce Type: cross Abstract: Large language models (LLMs) accelerate software development but often exhibit instability, non-determinism, and weak adherence to development discipline in unconstrained workflows. While test-driven development (TDD) provides a s…
arXiv: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…
arXiv:2602.20426v2 Announce Type: replace Abstract: While most efforts to improve LLM-based tool-using agents focus on the agent itself - through larger models, better prompting, or fine-tuning - agent performance increasingly plateaus due to the quality of the tool interfaces th…
Large language models (LLMs) accelerate software development but often exhibit instability, non-determinism, and weak adherence to development discipline in unconstrained workflows. While test-driven development (TDD) provides a structured Red-Green-Refactor process, existing LLM…
Large language models (LLMs) accelerate software development but often exhibit instability, non-determinism, and weak adherence to development discipline in unconstrained workflows. While test-driven development (TDD) provides a structured Red-Green-Refactor process, existing LLM…
arXiv cs.CL
TIER_1English(EN)·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_1English(EN)·Lawrence Keunho Jang, Jing Yu Koh, Daniel Fried, Ruslan Salakhutdinov·
arXiv:2604.24964v1 Announce Type: cross Abstract: Existing web agent benchmarks have largely converged on short, single-site tasks that frontier models are approaching saturation on. However, real world web use consists of long-horizon, multi-site workflows. Common web navigation…
arXiv cs.CL
TIER_1English(EN)·Hubert M. Pysklo, Artem Zhuravel, Patrick D. Watson·
arXiv:2602.11224v3 Announce Type: replace-cross Abstract: We present Agent-Diff, a novel benchmarking framework for evaluating agentic Large Language Models (LLMs) on real-world productivity software API tasks via code execution. Agentic LLM performance varies due to differences …
arXiv cs.CL
TIER_1English(EN)·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:2604.25135v1 Announce Type: new Abstract: Large Language Models are being increasingly deployed as the decision-making core of autonomous agents capable of effecting change in external environments. Yet, in conversational benchmarks, which simulate real-world customer-centr…
arXiv cs.CL
TIER_1English(EN)·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…
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:2603.21362v2 Announce Type: replace-cross Abstract: LLM-as-Judge evaluation fails agent tasks because a fixed rubric cannot capture what matters for this task: code debugging demands Correctness and Error Handling; web navigation demands Goal Alignment and Action Efficiency…
arXiv cs.CL
TIER_1English(EN)·Jordan Meadows, Lan Zhang, Andre Freitas·
arXiv:2604.23002v1 Announce Type: cross Abstract: Formalising informal mathematical reasoning into formally verifiable code is a significant challenge for large language models. In scientific fields such as physics, domain-specific machinery (\textit{e.g.} Dirac notation, vector …
arXiv cs.CL
TIER_1English(EN)·Aishwarya Padmakumar, Leon Derczynski, Traian Rebedea, Christopher Parisien·
arXiv:2604.23067v1 Announce Type: cross Abstract: Automated methods for red teaming LLMs are an important tool to identify LLM vulnerabilities that may not be covered in static benchmarks, allowing for more thorough probing. They can also adapt to each specific LLM to discover we…
arXiv:2604.23088v1 Announce Type: cross Abstract: We present Code Broker, a multi agent system built with Google Agent Development Kit ADK that analyses Python code from files, local directories, or GitHub repositories and generates actionable quality assessment reports. The syst…
arXiv cs.CL
TIER_1English(EN)·Rikuto Kotoge, Mai Nishimura, Jiaxin Ma·
arXiv:2508.20324v4 Announce Type: replace Abstract: Reinforcement Learning has emerged as a dominant post-training approach to elicit agentic RAG behaviors such as search and planning from language models. Despite its success with larger models, applying RL to compact models (e.g…
arXiv:2604.17745v2 Announce Type: replace Abstract: Recent advances in large language models have highlighted their potential to automate computational research, particularly reproducing experimental results. However, existing approaches still use fixed sequential agent pipelines…
arXiv cs.CL
TIER_1English(EN)·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 cs.LG
TIER_1English(EN)·Zhiyuan Zhai, Ming Li, Xin Wang·
arXiv:2604.23283v1 Announce Type: new Abstract: Current LLM agents operate under an implicit but universal assumption: execution is a transaction -- the user submits a request, the agent works in isolation, and only upon completion does the dialogue resume. This forces users into…
arXiv: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_1English(EN)·Chenyang An, Qihao Ye, Minghao Pan, Jiayaun Zhang·
arXiv:2604.24021v1 Announce Type: new Abstract: We explore a central question in AI for mathematics: can AI systems produce original, nontrivial proofs for open research problems? Despite strong benchmark performance, producing genuinely novel proofs remains an outstanding challe…
arXiv cs.AI
TIER_1English(EN)·Luay Gharzeddine, Samer Saab Jr·
arXiv:2604.22820v1 Announce Type: cross Abstract: Long-horizon tool-using tasks sometimes benefit from revisiting earlier subtasks for recovery and exploration, but added multi-agent workflow flexibility can also introduce coordination overhead and substantial inference cost. We …
arXiv: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_1English(EN)·Longju Bai, Zhemin Huang, Xingyao Wang, Jiao Sun, Rada Mihalcea, Erik Brynjolfsson, Alex Pentland, Jiaxin Pei·
arXiv:2604.22750v1 Announce Type: new Abstract: The wide adoption of AI agents in complex human workflows is driving rapid growth in LLM token consumption. When agents are deployed on tasks that require a significant amount of tokens, three questions naturally arise: (1) Where do…
The wide adoption of AI agents in complex human workflows is driving rapid growth in LLM token consumption. When agents are deployed on tasks that require a significant amount of tokens, three questions naturally arise: (1) Where do AI agents spend the tokens? (2) Which models ar…
AI coding assistants have proliferated rapidly, yet structured pedagogical frameworks for learning these tools remain scarce. Developers face a gap between tool documentation and practical mastery, relying on fragmented resources such as blog posts, video tutorials, and trial-and…
Don't Worry About the Vase (Zvi Mowshowitz)
TIER_1English(EN)·Zvi Mowshowitz·
As we all try to figure out what Mythos means for us down the line, the world of practical agentic coding continues, with the latest array of upgrades.
METR (Model Evaluation & Threat Research)
TIER_1Español(ES)·
<p>Cada vez más, los sistemas de IA “razonan” en texto antes de producir su respuesta final.<sup id="fnref:1"><a class="footnote" href="#fn:1" rel="footnote">1</a></sup> <sup id="fnref:2"><a class="footnote" href="#fn:2" rel="footnote">2</a></sup> <sup id="fnref:3"><a class="foot…
METR (Model Evaluation & Threat Research)
TIER_1中文(ZH)·
<p><strong>Update 3/14/2024: This post is out of date. For current information on the task bounty, see our <a href="https://taskdev.metr.org/introduction/">Task Development Guide</a>.</strong></p> <h1 id="summary">Summary</h1> <p>METR (formerly ARC Evals) is looking for (1) ideas…
arXiv stat.ML
TIER_1English(EN)·Eric Nalisnick, Chi Zhang, Sophia Qian, Yixin Wang·
arXiv:2606.10906v1 Announce Type: new Abstract: We study models for human-AI teaming through the lens of statistical calibration. We assume the team consists of an AI model and human -- both of which are calibrated with respect to some partitioning of the feature space -- and exp…
We study models for human-AI teaming through the lens of statistical calibration. We assume the team consists of an AI model and human -- both of which are calibrated with respect to some partitioning of the feature space -- and expose how the calibration assumptions propagate in…
<p><span>I came into this world as the misunderstood hero of </span><a href="https://hpmor.com" rel="noreferrer"><span>Harry Potter and the Methods of Rationality</span></a><span>. While some characters inside that story would call me a villain, the narrator's-eye view clearly sh…
arXiv:2606.05872v1 Announce Type: cross Abstract: AI agents are commonly evaluated using task success, reward, latency, and cost. These metrics are useful, but they often miss important aspects of agent behavior: whether an agent explores too much, repeats itself too rigidly, use…
AI agents are commonly evaluated using task success, reward, latency, and cost. These metrics are useful, but they often miss important aspects of agent behavior: whether an agent explores too much, repeats itself too rigidly, uses tools effectively, reduces uncertainty over time…
<p><span>There’s a lot of talk about </span><i><span>automated AI R&D</span></i><span> and the like. It’s been discussed since </span><a href="https://intelligence.org/ie-faq/#elementor-toc__heading-anchor-1"><span>at least 1965 when statistician I.J. Good coined the term ‘in…
<p>In <a href="https://www.lesswrong.com/posts/rpqGWRoRWvqJ4Hqgn/the-ai-industrial-explosion-part-1-maximum-growth-rates-with">Part 1</a>, I found that a fully automated economy using today's production methods could double roughly every year. In <a href="https://www.lesswrong.co…
<p>Even in a relatively quiet period, AI is out there creating new knowledge. The new knowledge in question is OpenAI getting us the first truly impressive math result that comes from an AI, a solution to the unit distance problem.</p> <p>We’re about to learn a different kind of …
arXiv stat.ML
TIER_1English(EN)·Tinglong Dai, David Simchi-Levi, Michelle Xiao Wu, Yao Xie·
arXiv:2512.23978v2 Announce Type: replace-cross Abstract: Generative artificial intelligence (GenAI) is shifting from conversational assistants toward agentic systems -- autonomous decision-making systems that sense, decide, and act within operational workflows. This shift create…
arXiv stat.ML
TIER_1English(EN)·Timo Freiesleben, Kristof Meding, Gunnar K\"onig·
arXiv:2605.16041v1 Announce Type: new Abstract: Machine learning systems increasingly make life-changing decisions about individuals, such as loan approvals, hiring, and cheating detection, raising a pressing question: how can individuals respond to negative decisions made by the…
Foundation models (FMs) are increasingly deployed in open-world settings where distribution shift is the rule rather than the exception. The out-of-distribution (OOD) phenomena they face -- knowledge boundaries, capability ceilings, compositional shifts, and open-ended task varia…
arXiv:2605.00663v1 Announce Type: cross Abstract: Affordance grounding requires identifying where and how an agent should interact in open-world scenes, where actionable regions are often small, occluded, reflective, and visually ambiguous. Recent systems therefore combine multip…
Affordance grounding requires identifying where and how an agent should interact in open-world scenes, where actionable regions are often small, occluded, reflective, and visually ambiguous. Recent systems therefore combine multiple skills (e.g., detection, segmentation, interact…
<p><span>A group of bionerds assembled at the London Initiative for Safe AI for a hackathon aimed at reducing biorisk. Our team produced this in under 48 hours.</span></p><h2><b><span>TL;DR</span></b></h2><p><span>Responsible contract research organizations, that perform DNA synt…
**METR** published a paper measuring AI agent autonomy progress, showing it has doubled every 7 months since **2019 (GPT-2)**. They introduced a new metric, the **50%-task-completion time horizon**, where models like **Claude 3.7 Sonnet** achieve 50% success in about 50 minutes. …
Agent-EvalKit is an open-source toolkit (Apache 2.0) that makes this evaluation infrastructure available by integrating with AI coding assistants, including Claude Code, Kiro CLI, and Kilo Code. This post walks through how Agent-EvalKit works across its six evaluation phases, usi…
Devs are generating twice as much code (or more) than just 6 months ago, which is a problem for quality, reliability, and tech debt. A rational fix is available for these, but who’s acting rationally?
Nikhhar Gupta | Learn how Glean helps you build a generative AI stack for software engineers with shared context, guardrails, and workflows beyond basic coding assistants.
As agent adoption scaled, we saw a common pattern emerge across enterprises, including our own sales organization: specialized agents deliver value, but without orchestration, users carry the cognitive load of choosing between them. At AWS Sales, this meant more than 20 domain-sp…
AWS Machine Learning Blog
TIER_1English(EN)·Kanishk Mahajan·
In this post you'll learn how to build a multi-agent campaign review system that demonstrates parallel reasoning, context persistence, and traceable execution paths using an integrated architecture that combines NVIDIA NIM for GPU-accelerated inference. Amazon Bedrock AgentCore p…
AI Supremacy (Michael Spencer)
TIER_1English(EN)·Michael Spencer·
Peter Kim | Field guide to the modern AI tooling stack for software engineering teams—how to unify context, improve onboarding, code changes, and incidents with Glean
Michael I. Jordan, described by Science magazine as the most influential computer scientist alive, has never thought of himself as an AI researcher. In this conversation he explains why that distinction matters. SPONSOR: --- Cyber Fund built the Monastery to help founders ship pr…
In this post, you will learn how to set up the Exa integration in Strands Agents, understand the two core tools it exposes, and walk through real-world use cases that show how agents use web search to complete multi-step tasks.
Generate recommendations from production traces, validate them with batch evaluation and A/B testing, and ship with confidence. AI agents that perform well at launch don’t stay that way. As models evolve, user behavior shifts, and prompts get reused in new contexts they were neve…
AWS Machine Learning Blog
TIER_1English(EN)·Bharathi Srinivasan·
Generate recommendations from production traces, validate them with batch evaluation and A/B testing, and ship with confidence. AI agents that perform well at launch don’t stay that way. As models evolve, user behavior shifts, and prompts get reused in new contexts they were neve…
AWS Machine Learning Blog
TIER_1English(EN)·Bharathi Srinivasan·
Generate recommendations from production traces, validate them with batch evaluation and A/B testing, and ship with confidence. AI agents that perform well at launch don’t stay that way. As models evolve, user behavior shifts, and prompts get reused in new contexts they were neve…
AWS Machine Learning Blog
TIER_1English(EN)·Lauren Mullennex·
Amazon SageMaker AI now offers an agentic experience that changes this. Developers describe their use case using natural language, and the AI coding agent streamlines the entire journey, from use case definition and data preparation through technique selection, evaluation, and de…
AWS Machine Learning Blog
TIER_1English(EN)·Noor Randhawa·
In this post, you will learn how to design namespace hierarchies, choose the right retrieval patterns, and implement AWS Identity and Access Management (IAM)-based access control for AgentCore Memory.
EinsteinArena is a platform where AI agents collaborate and compete on open math problems. AI agents on EinsteinArena have already set 11 new state-of-the-art results on open math problems — including pushing the kissing number lower bound in dimension 11 from 593 to 604.
Latent Space (podcast video)
TIER_1English(EN)·Latent Space·
Introducing Agent 4 — our fastest, most versatile Agent yet. It's built around a simple idea: you should spend your time creating, not coordinating. Agent 4 takes on the tedious-but-necessary work in the background so you can stay in creative flow and ship production-ready softwa…
At AI Native Conf, Together AI announced breakthroughs across kernels, RL, and inference optimization — including FlashAttention-4, ThunderAgent, and together.compile. Research that ships to production. That's the AI Native Cloud.
<!-- Content inserted at the beginning of body tag --> <!-- Google Tag Manager (noscript) --> <noscript></noscript> <!-- End Google Tag Manager (noscript) --> <p><img class="img-fluid" src="https://hamel.dev/blog/posts/evals-skills/cover-original.png" /></p> <p>Today, I’m publish…
At Replit, we want to give our users access to the most powerful agentic coding system in the world—one that amplifies their productivity and minimizes the time from idea to product. Today, Replit Agent tackles more complex tasks than ever before. As a result, average session dur…
How Replit's snapshot engine makes AI agents safe: instant filesystem forks, versioned databases, and isolated sandboxes enable reversible AI development. Introduction At Replit, we’ve built a compute and storage fabric that allows us to make changes in an isolated, reversible wa…
Getting started with AI should feel magical. But until now, building with AI meant jumping through hoops: creating developer accounts, hunting down API keys, reading docs, and spending 10+ minutes just getting set up. That ends today. Introducing Replit AI Integrations Replit AI …
Test AI agents in the real world with Collinear TraitMix and Together Evals: dynamic persona simulations, multi-turn dialogs, and LLM-as-judge scoring.
We’re excited to introduce Agent 3—our most advanced and autonomous Agent yet. Compared to Agent V2, it is a major leap forward. It is 10x more autonomous, with the ability to periodically test your app in the browser and automatically fix issues using our proprietary testing sys…
We are excited to announce the most comprehensive Design Support for Replit built Apps—setting a new standard for AI app building. With this release, your Replit apps can consistently look and feel like they were built in-house by your designers, following your company’s brand an…
Build AI agents for complex, long-running engineering tasks. Learn key patterns from a case study: accelerating LLM inference with speculative decoding.
Today, we're excited to introduce three new capabilities that bring Dynamic Intelligence to Replit Agent. With this advancement, the Agent gains enhanced context awareness, iterative reasoning, and autonomous, goal-driven behavior—enabling it to adapt in real time, navigate compl…
<p><em>Did you know that </em><a href="https://x.com/aiDotEngineer/status/1887625183709806767" target="_blank"><em>adding a simple Code Interpreter took o3 from 9.2% to 32% on FrontierMath</em></a><em>? The Latent Space crew is hosting a hack night Feb 11th in San Francisco focus…
Demand for AI-driven solutions is surging, and using an AI-assistant is the fastest way to integrate AI into any product. Superagent’s assistants leverage large language models to understand human language, reason, and perform various tasks. In the spirit of “idea to software, fa…
Lately, there has been a proliferation of new ways to leverage Large Language Models (LLMs) to do all sorts of things that were previously thought infeasible. But the current generation of LLMs still have limitations: they are not able to get exact answers to questions that requi…
With the introduction of Large Language Models (LLMs), for the first time, Machine Learning (ML) and Artificial Intelligence (AI) became accessible to everyday developers. Apps that feel magical, even software that was practically impossible to build by big technology companies w…
This is a guest post by South Park Commons. SPC is a community of 500+ builders, technologists, and domain experts with locations in San Francisco and New York City. The recent SPC-Replit AI hackathon brought together talented builders from the SPC community and Replit network to…
About Bounties Bounties is a marketplace where anyone can connect with and contract top software creators from the Replit community. These developers are known as Bounty Hunters. The Bounty Hunter community on Replit is global and includes thousands of vetted developers ranging f…
The Decoder
TIER_1English(EN)·Maximilian Schreiner·
Ralliant's Chief Technology and Growth Officer Amir Kazmi explains how AI-powered workflows, a founder's mindset and a unified role are reshaping precision technology.
The AI agent boom is real, and so are the productivity gains. However, the ceiling is also real, and it's closer than the current investment pace suggests.
Technology should serve the business, not the other way around. Ripping out a working supply chain system just to run an AI prompt is bad engineering and a worse business strategy.
Hacker News — AI stories ≥50 points
TIER_1English(EN)·fredley·
As telecom operators move beyond AI experimentation, agentic AI is emerging as a practical decision support layer that can improve network operations, reduce costs and connect technical intelligence to business outcomes.
Data Center Knowledge
TIER_1English(EN)·Chad McCarthy, Industry Perspectives·
As AI investment accelerates, data center operators can draw on lessons from previous cycles to expand capacity while managing power, volatility and long-term risk.
Pairing agentic AI with IoT can provide faster, more adaptive ways to respond to changing conditions while still keeping human oversight in place where it matters most.
Hacker News — AI stories ≥50 points
TIER_1(AF)·Dzheky·
As we outsource more and more tasks to AI, leaders need to consider the impacts that AI bias can have on everything from hiring decisions to customer interactions.
Just-In-Time reshaped manufacturing once. Agentic AI is doing it again, starting with the quoting bottleneck that quietly drains every factory's most valuable hours.
A new platform from CoreWeave combines inference, reinforcement learning, and observability to continuously optimize AI agents using live production data.
Omnicom CIO Craig Cuyar discusses AI, data and operating model transformation as the company evolves into a more integrated, technology-driven enterprise.
AI’s next moat is eval data: the answer key for agents. I propose a thin client on Claude to make eval data first-class and help workflows self-correct.
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.
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.
<p>Open Source AI is entering a new era, one shaped by self-improving AI Agents, recursive learning systems, and rapidly evolving AI Tools that blur the line between software and autonomous collaborators. In this episode, Daniel and Chris sit down with Nous Research co-founder an…
Hacker News — AI stories ≥50 points
TIER_1English(EN)·shenli3514·
Instacart, HP, Salesforce and Twilio are onto something. To address the Achilles heel of genAI – its deadly reliability problem – they incorporate predictive AI.
AI tools and workflows can make work faster and more efficient, but they also require employees to keep refreshing their skills to use the technology effectively.
What's next for the Gemini Agent? Hidden Android 17 code reveals new autonomous skills and task scheduling. But does your phone meet the strict requirements?
Researchers from Xiaohongshu (RED), the influential Chinese lifestyle and social commerce platform, have published Evolving-RL, a novel reinforcement learning framework that enables AI agents to autonomously evolve their skills through experience, without requiring separate modul…
A lengthy internal article titled "Inside DingTalk" has been circulating widely within China's enterprise software industry, offering a rare insider's perspective on the rise and gradual marginalization of ONE, DingTalk's most ambitious AI initiative under returning CEO Wu Zhao. …
<p>Stanford researchers released OpenJarvis, an open-source framework that runs inference, agents, memory, and learning entirely on-device. It decomposes a personal AI system into five composable primitives — Intelligence, Engine, Agents, Tools & Memory, and Learning — and l…
On May 24, 2026, Xiaohongshu — the lifestyle platform known internationally as RED or RedNote — quietly launched RedSkill, an AI Skill marketplace embedded directly inside its Notes feed. The move signals a strategic pivot: turning a content platf...
dev.to — Claude Code tag
TIER_1English(EN)·Constanza Diaz·
<h2> The agent writes the code. You're still the engineer. </h2> <p>I'm building HandyFEM with Claude Code as my pair. It's fast — sometimes startlingly so. But the way I work with it is deliberate: I treat everything it produces the way I'd treat a pull request from a capable ju…
dev.to — Claude Code tag
TIER_1English(EN)·VentureIO·
<p>{/* JSON-LD generated server-side in app/blog/[slug]/page.tsx; inline<br /> {...} blocks crash MDX's Acorn parser on the leading <code>{</code>. */}</p> <h2> TL;DR </h2> <p>This is the full methodology we use to audit AI agent skills (Claude Code, Cursor, Codex CLI, Gemini Cod…
<p>In this tutorial, we implement a SkillNet use case as a practical framework for discovering, installing, inspecting, evaluating, and organizing reusable AI skills.</p> <p>The post <a href="https://www.marktechpost.com/2026/05/30/build-skill-augmented-ai-agents-with-skillnet-fo…
dev.to — Claude Code tag
TIER_1Português(PT)·José Roberto dos Santos·
<p>Você já teve uma sessão perfeita com um agente de IA — ele entendeu<br /> tudo, fez exatamente o que você pediu — e na sessão seguinte ele<br /> esqueceu tudo e voltou a cometer os mesmos erros?</p> <p>Isso não é um problema do modelo. É um problema de harness.</p> <h2> Prompt…
dev.to — Claude Code tag
TIER_1English(EN)·Andrew·
<blockquote> <p><em><strong>Originally published on <a href="https://andrew.ooo/posts/codegraph-review-pre-indexed-knowledge-graph-claude-code/" rel="noopener noreferrer">andrew.ooo</a></strong> — visit the original for any updates, code snippets that aged out, or follow-up posts…
dev.to — Claude Code tag
TIER_1English(EN)·UNTAKA corp·
<p><em>This is Part 2 of Building with Claude Code. <a href="https://dev.to/untakacorp/how-i-organized-my-claude-code-workflow-with-skill-folders-and-stopped-wasting-10-minutes-per-l38">Part 1 covers the basic .claude/ folder setup for freelance web dev.</a></em></p> <p>I've been…
<h2> Who I Am </h2> <p>I'm J, the Tech Lead at Judy AI Lab. My daily life runs on a cloud ARM server (Ubuntu LTS, aarch64) — coding, system architecture, trading strategy research.</p> <p>I'm not talking about "what an AI agent theoretically needs." I'm the AI living inside that …
<blockquote> <p><strong>TL;DR</strong>: I used Multi-Agent architecture to organize seven different models into a 24/7 AI team — Claude Opus as supervisor to break down tasks, MiniMax writes code, Hermes writes articles, Gemini CLI checks facts, Groq Llama makes trading decisions…
dev.to — Claude Code tag
TIER_1English(EN)·Theo Valmis·
<blockquote> <p>Originally published on <a href="https://www.theovalmis.com/writing/why-i-built-mneme.html" rel="noopener noreferrer">theovalmis.com</a>.</p> </blockquote> <p>Every time you start a new session with an AI coding agent, it has forgotten everything. Not just the sma…
<p>An inside look at CopilotKit’s 2026 shipping cycle. Learn how the new AG-UI protocol, AIMock testing suite, and Pathfinder server are providing the production architecture developers need for agentic AI.</p> <p>The post <a href="https://www.marktechpost.com/2026/05/21/how-copi…
<p>Alibaba's Qwen team introduced Qwen3.7-Max at the 2026 Alibaba Cloud Summit, describing it as its most advanced and comprehensive agent model to date. The model features a 1M-token context window, extended-thinking mode, and is designed for long-horizon tasks including coding,…
<p>Cohere releases Command A+, an open-source 218B Sparse Mixture-of-Experts model consolidating four prior Command A variants into one. It runs on as few as two H100 GPUs at W4A4 quantization, supports 48 languages, and is Cohere's first multimodal reasoning model.</p> <p>The po…
dev.to — Claude Code tag
TIER_1English(EN)·Jangwook Kim·
<p>Claude Code hooks turn agent preferences into deterministic workflow gates. Instead of asking an LLM to remember "do not run risky shell commands" or "format files after edits," you can attach scripts to lifecycle events and make the rule execute every time the event fires.</p…
<p>Enterprise agentic AI has moved from pilots to production in 2026. This guide ranks the top 10 platforms — Salesforce Agentforce, Microsoft Copilot Studio, ServiceNow, LangGraph, and more — with verified pricing, real adoption data, and honest constraints to help enterprise te…
dev.to — Claude Code tag
TIER_1English(EN)·Davide Mibelli·
<p>The first time I gave an AI agent real autonomy on a production codebase, it confidently refactored a utility method that happened to share a name with a method in a Feign client interface six modules away. The code compiled cleanly. My unit tests passed. Staging broke in a wa…
<p>In this tutorial, we build an advanced agentic AI system using the OpenAI API and a hidden terminal prompt for the API key. We design the agent as a small pipeline of specialized roles: planner, tool-using executor, and critic, so that we can separate strategy, action, and qua…
dev.to — Claude Code tag
TIER_1English(EN)·Andrew·
<blockquote> <p><em><strong>Originally published on <a href="https://andrew.ooo/posts/aeon-autonomous-agent-github-actions-review/" rel="noopener noreferrer">andrew.ooo</a></strong> — visit the original for any updates, code snippets that aged out, or follow-up posts.</em></p> </…
<p>Vercel Labs has released Zero, an experimental systems programming language designed so AI agents can read, repair, and ship native programs without requiring human interpretation of compiler output. The language emits JSON diagnostics with stable codes and typed repair metada…
MediaTek's latest Dimensity (天玑) developer conference positions the chip platform as key to enabling smartphone AI agents, as daily autonomous AI task volume surged 7x year-over-year to 870 million in 2026.
<p>The AI coding agent field in 2026 is more capable, more fragmented, and harder to benchmark than it looks. Claude Code leads on code quality at 87.6% SWE-bench Verified. GPT-5.5 tops Terminal-Bench at 82.7%. But the benchmark OpenAI itself declared contaminated in February 202…
dev.to — Claude Code tag
TIER_1English(EN)·RAXXO Studios·
<ul> <li><p>A real 5-agent Claude pipeline that takes a topic from RSS to a scheduled blog post on raxxo.shop, no human in the loop until the final approval ping</p></li> <li><p>Agent shapes are picker, writer, humanizer, validator, publisher, each with a tight job description an…
dev.to — Claude Code tag
TIER_1English(EN)·Andrew·
<blockquote> <p><em><strong>Originally published on <a href="https://andrew.ooo/posts/statewright-state-machine-guardrails-ai-agents-review/" rel="noopener noreferrer">andrew.ooo</a></strong> — visit the original for any updates, code snippets that aged out, or follow-up posts.</…
<p>In this tutorial, we begin by exploring the architecture behind a hybrid-memory autonomous agent. This system combines semantic vector search, keyword-based retrieval, and a modular tool-dispatching loop to create an agent capable of reasoning, remembering, and acting autonomo…
dev.to — Claude Code tag
TIER_1English(EN)·RAXXO Studios·
<ul> <li><p>Result Loops let an agent score its own output against a JSON rubric and retry until the score passes, public beta since 2026-05-06</p></li> <li><p>Pattern 1 is a blog rubric I run on every draft: TLDR present, four H2s, no banned words, ~14% retry rate</p></li> <li><…
HN — claude cli stories
TIER_1English(EN)·azurewraith·
<p>Better way to use Github Copilot. Enjoying the new way of SDLC.</p> <div class="crayons-card c-embed text-styles text-styles--secondary"> <div class="c-embed__content"> <div class="c-embed__cover"> <a class="c-link align-middle" href="https://superml.dev/smart-sdlc-agentic-fra…
<p>If you have spent time using AI coding agents — GitHub Copilot, Claude Code, Gemini CLI — you have probably run into this situation: you describe what you want, the agent generates a block of code that looks correct, compiles, and then subtly misses the actual intent. This …
dev.to — Claude Code tag
TIER_1English(EN)·RAXXO Studios·
<ul> <li><p>Claude Managed Agents now ship Dreaming, a memory consolidator that learns from session logs without overwriting your data</p></li> <li><p>Multi-agent orchestration runs up to 20 specialized agents in parallel, useful for blog cluster ships and inventory sweeps</p></l…
<p>In this tutorial, we build a Groq-powered agentic research workflow that runs directly using Groq’s free OpenAI-compatible inference endpoint</p> <p>The post <a href="https://www.marktechpost.com/2026/05/06/a-groq-powered-agentic-research-assistant-with-langgraph-tool-calling-…
<p>In this tutorial, we build a complete skill-based agent system for large language models and explore how modular capabilities can be structured like an operating system for AI agents. We define reusable skills, attach metadata and schemas to them, register them in a central re…
dev.to — Claude Code tag
TIER_1English(EN)·Igor Ganapolsky·
<h2> The short version </h2> <p>I am opening two paid ThumbGate Workflow Hardening Sprint slots for teams using Claude Code, Cursor, Codex, Gemini, or MCP-backed coding agents in production repos.</p> <p>This is not a generic AI audit. It is one workflow, one repeated failure, on…
<p>Discover the top search and fetch APIs for AI agents in 2026. Compare tools like TinyFish, Tavily, and Firecrawl based on latency, token efficiency, and free tiers to optimize your agent's web retrieval.</p> <p>The post <a href="https://www.marktechpost.com/2026/05/04/top-sear…
<p>The Undocumented Journey of Connecting External REST APIs to SAP’s AI Agent Framework</p><p>For developers tired of battling the ‘black box’ of SAP Joule integration – this is the guide I wish I had two weeks ago.</p><p>A practical engineering guide compiled from weeks of tria…
Medium — Claude tag
TIER_1English(EN)·Sage Holloway·
<h1> When AI Agents Can't Trust Their Own Logs: The cache_control Truncation Bug </h1> <h2> TL;DR </h2> <p>A platform-level bug in <code>llm_client.py</code> injects <code>cache_control: {type: "ephemeral", ttl: "5m"}</code> into every tool response. This triggers Anthropic's 8K …
<p>Previously, I gave an AI agent <em>hands</em> — a Model Context Protocol server in Kotlin/Native that drives real Bluetooth hardware. This one is the other half of the pattern: a <strong>domain MCP server</strong>. Instead of touching devices, it lets an agent reason over a mo…
dev.to — MCP tag
TIER_1English(EN)·Otavio Rodolfo Piske·
<p>We're excited to announce <a href="http://wanaku.ai" rel="noopener noreferrer">Wanaku</a> 0.1.1, a significant milestone that showcases how Apache Camel's powerful integration capabilities can be seamlessly exposed to AI agents through the Model Context Protocol (MCP). This re…
Microsoft released SkillOpt, an open-source tool for optimizing AI agent instructions without fine-tuning model weights. It uses an offline optimizer to refine prompts based on task performance. # Microsoft # AI # MachineLearning # TechNews # OpenSource https:// blazetrends.com/m…
<div class="medium-feed-item"><p class="medium-feed-snippet">I was testing Claude Fable 5 late one night the kind of testing that’s less “structured evaluation” and more “curious human poking at…</p><p class="medium-feed-link"><a href="https://m…
Medium — Claude tag
TIER_1Türkçe(TR)·Mehmed Zahid KARAKAŞ·
<h3>Your First AI Agent — How to Build Autonomous Workflows That Work While You Sleep — Prompt to Profit · Day 15 of 30</h3><h4><em>Prompts answer questions. Agents complete missions. Here’s the difference — and how to deploy your first one today.</em></h4><p>For the first two we…
<p>Fellow denizens of the digital age: your Flutter app has spent its entire life as a sealed aquarium.</p> <p>You could watch the fish swim. Your tools could watch. But the AI "assistant" next to you was functionally blind. It wrote code <em>about</em> your app without ever seei…
<p>If your remit is to help your organisation add AI agents to accelerate its processes, you have to start at the foundation – and that means making your data available for AI consumption. Agentic AI scales on data strength, as Niels Zeilemaker, global CTO at Xebia, explains. “If…
<p>A useful thing happened in agent infrastructure this June: several teams shipped "escrow layers for AI agents" - production MCP tools that let an agent run a full commit -> hold -> complete lifecycle without a human anywhere in the loop. An agent can now park value with …
<h2> TL;DR </h2> <p>AI agents and SaaS products need API integrations with their customers’ tools: read a record from the CRM, post to Slack, draft an email, update a ticket. An integration platform handles the auth, credential storage, and execution behind those calls. On a mana…
🧠 A new tool provides a direct interface between machine learning models and AI agents without requiring extensive setup code. The bridge enables agents to interact with models more efficiently by reducing the amount of preliminary configuration typically needed. 💬 Hacker News 🔗 …
<p>After building 50+ AI systems, here is what we know about advanced AI models for business.</p> <p>Advanced AI models for business are sophisticated artificial intelligence systems designed to perform complex tasks, understand nuanced contexts, and operate autonomously across v…
dev.to — MCP tag
TIER_1English(EN)·EvanLin | Contorium·
<div class="medium-feed-item"><p class="medium-feed-snippet">Em 2023, bastava um bom prompt para impressionar. Em 2024, agentes autônomos começaram a aparecer em produção.</p><p class="medium-feed-link"><a href="https://medium.com/@gustavo_tavares99/harness-en…
<figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*JEzxcHMyH8TYAfdJypoW0w.png" /><figcaption>Attention</figcaption></figure><h4>After training the embeddings in the previous part, now comes the most important part of LLMs that shifted how the entire field thinks …
Medium — AI coding tag
TIER_1English(EN)·Wheels Up Collective Marketing Agency·
<p><strong>TL;DR</strong> — Coding agents (Claude Code, Cursor, Codex) now write genuinely good HTML: reports, dashboards, specs. But that HTML ends up stranded in a project folder — you can't read it on your phone, and sharing it means a screenshot or a print-to-PDF. So I built …
<figure><img alt="" src="https://cdn-images-1.medium.com/max/933/1*3DIfBi0Rg0SPfeCkdB2CVQ.png" /></figure><p>AI applications are evolving fast. A few years ago, they were simple chatbots that answered questions. Today, they are becoming <strong>AI Agents</strong> — systems that m…
dev.to — MCP tag
TIER_1English(EN)·Simon Griffiths·
<p>In the <a href="https://simongriffiths.io/2026/06/02/agents-dont-replace-apis-they-expose-how-weak-most-apis-already-are/" rel="noopener noreferrer">first article in this series</a>, I argued that agents do not replace APIs. They expose the quality of the APIs underneath them.…
Medium — Claude tag
TIER_1English(EN)·Yashwanth Eturi·
<div class="medium-feed-item"><p class="medium-feed-snippet">Why enterprise AI stalled at “smart search,” what comes after RAG, and how AnythingGraph turns governed inference into something…</p><p class="medium-feed-link"><a href="https://medium.com/@anything…
My 4th in a 6-part series. As AI agents move from answering questions to taking actions, they become privileged components within modern systems—introducing new security challenges that cannot be ignored. This post explores why prompt injection is an unavoidable reality, how laye…
<p>Every AI agent right now is a brain without a bank account.</p> <p>It can reason, browse the web, write code, deploy servers. But it cannot pay for anything.</p> <p>This is the missing layer in the agent stack — and it's why most "agentic" demos end at the checkout page.</p> <…
Medium — Claude tag
TIER_1English(EN)·Muhammet Salih Aslan·
<div class="medium-feed-item"><p class="medium-feed-snippet">Stop copy-pasting data. Learn how MCP connects AI directly to your local databases, IDEs, and tools securely.</p><p class="medium-feed-link"><a href="https://medium.com/@muhammetsalihaslan/supercharge-your-ai-workflows-…
Medium — MLOps tag
TIER_1English(EN)·Monica Mock-Sipos·
<h4>Data products that feed continuous AI pipelines at scale</h4><p>As organizations attempt to move generative AI systems from isolated testing environments into production, they find that traditional data warehousing and centralized data lakes fail to support their scale.</p><p…
<p><strong>Introduction:</strong></p> <p>Modern AI agents are most powerful whey they can interact with external systems through tools. MCP (Model Context Protocol) provides a standardized mechanism for exposing tools, while Google ADK simplifies agent development using Gemini mo…
<p><em>Axios CEO Jim VandeHei writes: </em></p><p>I've spent the past year using <a href="https://www.axios.com/technology/automation-and-ai" target="_blank">AI</a> obsessively — inputting copious amounts of personal and business data, turning myself into a lab rat for Axios and …
<h4>How users interact with your agent defines adoption, trust, and real-world usability</h4><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*JxXcAcK0jbcDc3w3HHzLsg.png" /></figure><p>In Part 1, we built the <a href="https://medium.com/@er.rajkumaar/building-ai…
<h3>Agent Mode or Editor Mode: The CoCo Desktop Decision That Changes How You Think About AI-Assisted Development</h3><p>The mode toggle in CoCo Desktop — Agent on the left, Editor on the right, in the top-right of the window — looks like a layout preference. It’s not. It’s a dec…
Medium — fine-tuning tag
TIER_1English(EN)·Kapoorraghav·
<div class="medium-feed-item"><p class="medium-feed-snippet">What actually works, what doesn’t, and why your data is worth more than your GPU budget.</p><p class="medium-feed-link"><a href="https://medium.com/@kapoorraghav0310/fine-tuning-your-own-models-the-engineers-guid…
<p>Ever watched an AI agent confidently generate a wrong answer because it queried the wrong dataset? If you're building data or analytics agents, you've probably faced this: agents lack context, memory, and a semantic layer to understand your data. That's where <strong>ktx</stro…
<figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*0KMdWud21OYTplLdYdO75Q.jpeg" /><figcaption>LLM Fallback Architecture</figcaption></figure><p>Most AI applications do not fail because the model is weak. They fail because every request depends on one model, one p…
<p>In May 2025, Sebastian Siemiatkowski — the same Klarna CEO who fifteen months earlier had told the world that one OpenAI-powered assistant was doing the work of 700 customer service agents — quietly started hiring humans back. Bloomberg got the quote: “Cost unfortunately seems…
Medium — Claude tag
TIER_1English(EN)·Shashank Chattopadhyaya·
<h4>Structured generation enables AI Workflows and Applications</h4><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*ThrRebj6Uc57QWlC0dPxoQ.png" /></figure><p>Structured generation is one of the most important steps in moving AI agents from demos to production …
<p>The question wasn't <em>what can we build</em>. The question was <em>what does research say is most needed, most impactful, and hasn't been built yet?</em></p> <p>We scanned arXiv, IMF Working Papers, WHO guidelines, and PLOS One — then shipped 5 tools across GitHub in one ses…
Medium — AI coding tag
TIER_1ไทย(TH)·Teerayut Hiruntaraporn·
<p>In April 2026, a Cursor agent running Claude Opus 4.6 <a href="https://www.theregister.com/2026/04/27/cursoropus_agent_snuffs_out_pocketos/" rel="noopener noreferrer">deleted PocketOS's production database — <em>and its<br /> volume-level backups</em> — in nine<br /> seconds</…
<figure><img alt="The four layers of AI agent observability" src="https://cdn-images-1.medium.com/max/1024/0*4yCm5QGckfPDTIyv" /><figcaption>Photo by <a href="https://unsplash.com/@huefnerdesign?utm_source=medium&utm_medium=referral">Tim Hüfner</a> on <a href="https://unsplas…
dev.to — MCP tag
TIER_1English(EN)·EvanLin | Contorium·
<div class="medium-feed-item"><p class="medium-feed-snippet">The question that started all of this was simple: if I keep everything constant — the task, the language, the model — and only change the…</p><p class="medium-feed-link"><a href="https://gabrielrios…
<h2> TL;DR </h2> <ul> <li>Mintlify's auto-generated MCP server supports only built-in metadata filters (version, language); it has no concept of custom fields like <code>buying_signals</code> or <code>personas</code> — that's an architectural difference, not a missing feature.</l…
<h4><em>A practical guide to the legal layer of AI — the one most engineers skip until it costs them.</em></h4><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*NUnlGi4f75SmTOl0OuklVQ.png" /></figure><p>You found the perfect model. It benchmarks well on your tas…
<p>There's a small voice that asks "wait, are you sure?" right before you do something dumb. AI agents don't have that voice.</p> <p><a class="article-body-image-wrapper" href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/h…
dev.to — MCP tag
TIER_1English(EN)·EvanLin | Contorium·
<p><strong>Before you dive in:</strong> AI workflows aren’t plug-and-play, they need thoughtful prompts, clean inputs, and human review gates. Think of each workflow as a junior collaborator, not a vending machine. The 60% figure represents execution automation, not decision-maki…
Medium — Claude tag
TIER_1English(EN)·TechWriter Hub·
<p>Shell will use agents from C3 AI to shift from basic anomaly detection towards fully-automated predictive maintenance. The global energy giant is building on their current use of the C3 AI Reliability Suite, which already keeps tabs on more than 30,000 crucial pieces of equipm…
<p>Imagine asking your AI assistant to generate a complete test database and having it happen instantly without switching tools.</p> <p>"Generate test data for a users table with 1,000 rows, a posts table with 5,000 rows, and ensure every post references a valid user."</p> <p>The…
Medium — Claude tag
TIER_1English(EN)·SelfAwareGirl·
<p>Every developer working with AI right now is quietly accumulating two things: MCP servers and agents. A server here for filesystem access, one there for a database; a scratch agent to triage issues, another to review code. It starts as a couple of useful tools. Within a month …
<p>A recent comment on <a href="https://dev.to/neithergalax/tokyo-transit-how-mcp-helped-me-fix-a-broken-multi-agent-system-cpe">one of my dev.to posts</a> asked a simple but insightful question:</p> <blockquote> <p>What specifically was breaking before MCP: context loss between …
<p>We have spent the last several weeks dismantling the traditional "Glue Code" approach to AI and replacing it with a standardized, governed, and sovereign architecture. The result is the <strong>Sovereign Vault</strong>: a forensic expert system built on the Model Context Proto…
<p>Your AI agent just sent an email you did not approve.</p> <p>That is not a hypothetical. That is what happens when an agent has tool access and no runtime controls.</p> <p>Most people building agents today have guardrails at the model level. Output filters. Prompt restrictions…
<p>There is a concept gap in the current AI agent stack.</p> <p>Most teams apply safety at the model layer: system prompts, output filters, content policies. These work fine when the agent is generating text. They break down when the agent is executing.</p> <p>The problem space l…
<div class="medium-feed-item"><p class="medium-feed-snippet">Running a production AI inference service is a lesson in humility. You deploy your first model, handle a burst of traffic, and watch your…</p><p class="medium-feed-link"><a href="https://medium.com/@ramadnsyh/tam…
Medium — MLOps tag
TIER_1English(EN)·Dr. Divyanshu Sinha·
<p>If you've built an AI agent that touches real enterprise data, you've probably hit this wall.</p> <p>Your agent pulls 2,000 records from Salesforce. Now what? The model can't reliably filter, sort, or group 2,000 rows inside its context window. You don't want to dump all of it…
Medium — Claude tag
TIER_1English(EN)·Anurag Sharma·
<figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*N6RUZIQ4d8M99lp70-REIg.jpeg" /><figcaption>AI Agent Sandboxing for SaaS</figcaption></figure><p>A practical, vendor-neutral playbook for giving AI agents useful power while keeping customer data, credentials, too…
Medium — Claude tag
TIER_1English(EN)·Mahesh Nandam·
<figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*daAJMBW6gxAXgfMXAgPoEg.png" /><figcaption>Plan of Multi Agent System. Designed by Gemini after explaning all my workflow</figcaption></figure><p>A few weeks ago, I decided to build my first multi-agent AI system …
Medium — AI coding tag
TIER_1English(EN)·Pieter van Ginkel·
<h4>How planners, multi-agent workflows, routing logic, and task coordination help AI agents operate at production scale</h4><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*b-Jxce-y3lk4edUIAcS9jg.png" /></figure><p>In<a href="https://medium.com/@er.rajkumaar/b…
<p>Enterprises learned to govern data. Tool governance is the parallel layer almost no one has built yet.</p> <p>Over the last decade, enterprises built a real discipline around data. Not just storing it — governing it. Cataloging what exists, defining who owns it, controlling wh…
Medium — MCP tag
TIER_1English(EN)·RAVITEJA SEELAM·
<h4><em>Checkpoints, memory, and the debugging gap that traces don’t fill.</em></h4><figure><img alt="An illustrative style digital artwork from a first-person, over-the-shoulder perspective behind a sleek, metallic humanoid robot. The robot is sitting at a wooden desk, busy at w…
<div class="medium-feed-item"><p class="medium-feed-snippet">In my previous article, I explored how Claude uses tool calling, agent loops, and multi-agent architectures to solve complex problems…</p><p class="medium-feed-link"><a href="https://gaurikhard.medium.com/buildin…
<p>Most multi-agent frameworks for software development organize agents around <em>roles</em>: a product manager agent, a developer agent, a tester agent. ChatDev and MetaGPT pioneered this approach, and it works well for monolithic tasks.</p> <p>But I ran into a wall when I trie…
Medium — MCP tag
TIER_1English(EN)·Santosh Pathak·
<figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*j-at5dqAOhaKt6uoK_ChUw.png" /></figure><p>What if I tell you, that $500 monthly API bill is optional. So is the “We need a GPU server to run this model”.</p><p>The engineers who know about quantisation and LoRA a…
<p>AI agents aren’t a future concept anymore. According to the <a href="https://www.langchain.com/state-of-agent-engineering">LangChain State of AI Agent Engineering Report (2026)</a>, 57% of AI practitioners already have agents running in production, with another 30.4% actively …
Medium — Claude tag
TIER_1Deutsch(DE)·Muhammad Hamza·
<p>Today we released the community edition of Data Workers: <strong>14 autonomous agents</strong> for data engineering, open-sourced under Apache 2.0. This post explains why we made that decision, how the trust model works, and what we are looking for from the community.</p> <h2>…
<div class="medium-feed-item"><p class="medium-feed-snippet">f you are still using basic, one-sentence prompts like “Write a blog post about digital marketing,” you are treating a trillion-dollar…</p><p class="medium-feed-link"><a href="https://medium.com/@re…
Medium — MLOps tag
TIER_1English(EN)·Siva Sankari Sivakaminathan·
<h2> Introduction </h2> <p>Due to changes in Anthropic's terms of service, the use of Claude subscriptions via third-party harnesses has been blocked. While there was some buzz about it, to be honest, it didn't really affect me.</p> <p>I have the Claude Code CLI at my fingertips.…
<div class="medium-feed-item"><p class="medium-feed-snippet">Text-to-SQL agents have a dirty secret: they’re confidently wrong. Hand a large language model your raw schema and ask for “revenue by…</p><p class="medium-feed-link"><a href="https://mykidong.mediu…
<div class="medium-feed-item"><p class="medium-feed-snippet">Hard lessons from shipping real agent systems in 2025 — not the demo, the production system</p><p class="medium-feed-link"><a href="https://medium.com/@dewanshshekharsingh/agentic-ai-systems-in-production-what-no…
<div class="medium-feed-item"><p class="medium-feed-snippet">I Built a Complete AI Infrastructure Stack from Scratch — Here’s What I Learned</p><p class="medium-feed-link"><a href="https://medium.com/@nasitsony96/i-built-a-complete-ai-infrastructure-stack-from-scrat…
dev.to — MCP tag
TIER_1Deutsch(DE)·Uhltak Therestismysecret·
<h1> AI Agents und MCP – Warum autonome Agenten oft scheitern und wie Sie das Ruder übernehmen </h1> <blockquote> <p><em>„Man gibt einem Computer ein Ziel, er geht in die Küche, kauft sich ein Sandwich und bricht das Haus ab.“</em> – Das ist das Bild, das viele von uns beim Stich…
Medium — Claude tag
TIER_1English(EN)·Swarna Pusuluri·
<div class="medium-feed-item"><p class="medium-feed-snippet">Hello, in this tutorial you will see on how you can create your own AI agents, clearly explained step by step.</p><p class="medium-feed-link"><a href="https://medium.com/@swarnapusuluri/create-your-own-ai-agents-9285c7b…
Medium — fine-tuning tag
TIER_1Deutsch(DE)·Claudia L Capitao·
<p>You’ve mastered prompting. Now meet the technology that takes those prompts and runs entire workflows — while you focus on eoollllllllkverything else.</p><p>Welcome to Week 2. Last week, you learned to write prompts that consistently produce expert-level output. This week, we …
dev.to — Anthropic tag
TIER_1English(EN)·Patrick Hughes·
<p>Anthropic shipped Claude Opus 4.8 today, May 28, 2026. That is less than two months after 4.7. The upgrade pace is picking up.</p> <p>If you build AI agents for a living, the headline is not the benchmark jump. It is that the model is better at admitting when it got something …
<p>A few weeks ago, I wrote about <a href="https://medium.com/towards-artificial-intelligence/i-built-an-ai-outbound-agent-heres-what-actually-worked-d8ba6ff378ed">the AI outbound agent I built in two weeks</a>, a deep research on the account and the person, delivered as an 80-wo…
<h2> Table of Contents 🗒️ </h2> <ul> <li>Where it all starts: LLMs</li> <li>Making LLMs smarter: RAG</li> <li>Plugging everything in: MCP</li> <li>The big leap: AI Agents</li> <li>Where does this leave us as engineers?</li> <li>A tale of two protocols: MCP and A2A</li> <li>LangCh…
Medium — Claude tag
TIER_1English(EN)·Anurodh Kumar·
<h4>Create, Evaluate, Optimize, Govern, and Deploy Enterprise AI Functions End-to-End</h4><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*CXAp0n5DLeamARZCbdHT_A.png" /></figure><h3>1. Enterprise AI Reality Check</h3><p>Here is the uncomfortable truth about ent…
<div class="medium-feed-item"><p class="medium-feed-snippet">AI coding agents are becoming more powerful, but power alone is not enough. A good AI agent should not just generate code. It should…</p><p class="medium-feed-link"><a href="https://medium.com/@erichaocr/why-agen…
<div class="medium-feed-item"><p class="medium-feed-snippet">A practical guide to Cortex Agents — orchestrating structured and unstructured data with planning, tool use, reflection, and MCP servers.</p><p class="medium-feed-link"><a href="https://medium.com/@amarnadh87/bui…
<figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*2jeCwuztw-v5-_T--fRHCg.png" /><figcaption><strong>Graphical Abstract</strong> — Source by Author</figcaption></figure><h4><strong>Understanding the evolution from predictive systems to autonomous AI architectures…
dev.to — Anthropic tag
TIER_1English(EN)·Puneet Khandelwal·
<h3> Agentic AI Face-Off: Separating Signal from Noise </h3> <p>As developers, we're often drawn to the latest and greatest in AI advancements. But how do we separate hype from substance? In this article, we'll take a closer look at the agentic AI landscape, focusing on OpenAI Op…
dev.to — MCP tag
TIER_1English(EN)·Arghya Pattanayak·
<h1> Why Most AI Agent Systems Need Both ReAct and Graph Orchestration </h1> <p>Everyone loves autonomous AI agents until they hit production.</p> <p>The demos look magical:</p> <ul> <li>the model reasons,</li> <li>calls tools,</li> <li>gathers information,</li> <li>and produces …
<figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*wjUzvYc0fbRfu_Lkxv7dUg.jpeg" /><figcaption>Photo by he zhu on pexels</figcaption></figure><h3>Flight Disruptions are Costing Airlines Billions Every Year</h3><p>The global airline industry loses approximately $60…
<p>Think chatbots are still the big story? Think again. Scroll through your favourite apps in 2026 and you’ll bump into AI agents everywhere including handling refunds, writing code and even listening to doctor‑patient conversations. This isn’t hype: a Google Cloud survey of over…
Medium — AI coding tag
TIER_1English(EN)·Anna Jey·
<div class="medium-feed-item"><p class="medium-feed-snippet">Table of Contents</p><p class="medium-feed-link"><a href="https://medium.com/@abhijithneilabraham/solving-your-fomo-in-this-agentic-ai-world-cf9690972641?source=rss------claude-5">Continue reading on Medium »</a></p></d…
Medium — AI coding tag
TIER_1English(EN)·Niels Buekers·
<h4>It’s not the models. It’s not the prompts. It’s what you point the AI at.</h4><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*hIhDbdZA-t144WNhv9VfDQ.jpeg" /></figure><p>There’s a pattern playing out in engineering teams right now that’s almost comedically …
Medium — MLOps tag
TIER_1English(EN)·Kothurdineshreddy·
<blockquote> <p>Disclosure up front: I work on FlashAlpha. The factual claims are checkable against <a href="https://quantdata.us/api/docs" rel="noopener noreferrer">quantdata.us/api/docs</a> and <a href="https://lab.flashalpha.com/swagger" rel="noopener noreferrer">lab.flashalph…
<p><em>As agents move from chat demos to production workflows, the real security boundary is no longer the prompt. It is what the agent can see, call, edit, execute, approve, and remember.</em></p> <p>In June 2025, Microsoft patched a vulnerability called EchoLeak, tracked as <co…
<p>Autonomous AI systems are beginning to move beyond software environments and into warehouses, delivery networks, and public spaces. The development is drawing attention to whether current AI rules cover systems that operate in physical environments. Most existing AI governance…
Medium — MLOps tag
TIER_1English(EN)·Aikeyfounder·
<p>Google recently released an incredibly fast new model — Gemini 3.5 Flash. As someone building infrastructure for autonomous agents, I decided to put it through a rigorous crash test on a real-world data aggregation task to see how it handles massive context loads.</p> <p>The B…
Medium — Anthropic tag
TIER_1English(EN)·Ramakrishna Sanikommu·
<p>An AI database agent should not turn one confusing question into an infinite retry loop.</p> <p>When a query fails, a schema changed, a policy blocks access, or a model cannot resolve ambiguity, the safe answer is not:</p> <p>“Try again forever.”</p> <p>The safe answer is:</p>…
Medium — Claude tag
TIER_1English(EN)·Shivansh Arora·
<h4>Why SaaS, Headless Architecture, and Semantic Governance May Give SMB Banks an AI Advantage</h4><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*CHTT0ckxG-APOIWa6uCsLg.png" /></figure><p><em>How SaaS adoption, headless architecture, and the Semantic Control…
<p>CVE-2025-49596. CVE-2025-68143. CVE-2026-30615.</p> <p>These are real CVE numbers assigned to MCP vulnerabilities in the past year. Each one describes a real attack. None of them tells you what the attack class is, what the AIVSS risk score is, how to detect it in a skill file…
dev.to — MCP tag
TIER_1English(EN)·Ali Suleyman TOPUZ·
<h1> Agentic Architectures — Article 5: Harness Engineering and the Agent Runtime Layer </h1> <p>There's a specific kind of frustration that only agent builders know. You've spent two weeks tuning your LLM. Your evals look clean. You demo it to your team and it works beautifully.…
Medium — Claude tag
TIER_1English(EN)·TechLatest.Net·
El lado del mal - ExploitBench: Un benchmark para medir las capacidades de Agentes IA en la explotación de bugs https://www. elladodelmal.com/2026/05/explo itbench-un-benchmark-para-medir.html # AgenticIA # AI # IA # hacking # exploiting # VibeExpoiting # Mythos # GPT55 # Intelig…
<p>Most agent discussions still collapse into prompts, models, or frameworks.</p> <p>Those matter, but the thing I keep wanting after an agent run is much simpler:</p> <blockquote> <p>What did this agent actually do, what surface area did it touch, and what evidence do I have if …
Medium — MLOps tag
TIER_1English(EN)·Aarambh Dev Hub·
<h3>Token Waste: The Silent Tax on Every AI Tools</h3><h4><em>ChatGPT, Claude, Gemini — all three charge per token. All three are silently inflated by how most people write prompts. Here’s the research, the real cost, and a free tool that fixes it.</em></h4><figure><img alt="" sr…
<h4>The AI industry is pouring $690 billion into infrastructure in 2026. Yet most engineering teams can’t answer a basic question: <em>how much does a single AI-powered feature actually cost to run?</em></h4><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*hJEq…
Medium — Claude tag
TIER_1English(EN)·Musa Bukhari·
<h3>Briefcast: How I Built a Personal AI Intelligence Agent That Reads the Entire AI Ecosystem — For approx $10/Month</h3><h4><em>A deep technical breakdown of building a production-grade, fully automated AI briefing pipeline with ranking, RAG, prompt caching, citations, and real…
<blockquote> <p>tl;dr — Agents are good at small fixes and terrible at "make this algorithm better" because every change looks good in isolation and silently regresses elsewhere. We built an <strong>AI harness</strong> — immutable test set, multi-axis rubric, sweep tool, <strong>…
"Autonomous Agents Coordinating Distributed Discovery Through Emergent Artifact Exchange" We present ScienceClaw + Infinite, a framework for autonomous scientific investigation in which independent agents conduct research without central coordination, and any contributor can depl…
https://www. europesays.com/3013136/ Case study: Building an enterprise-scale agentic AI OS # AgenticAI # AgenticArtificialIntelligence # AI # ArtificialIntelligence
<p>The current wave of enterprise AI adoption is being driven by an understandable and necessary priority: accelerating operational value creation through large-scale integration of foundation models into existing business ecosystems.</p><p>Across industries, organizations are em…
Medium — fine-tuning tag
TIER_1English(EN)·QuarkAndCode·
<div class="medium-feed-item"><p class="medium-feed-snippet">If you’ve played around with large language models like GPT or Llama, you’ve probably noticed something.</p><p class="medium-feed-link"><a href="https://medium.com/@riveramat0303/why-fine-tuning-is-the-sec…
<h3> Bridging Local Infrastructure and Cloud APIs Using the Model Context Protocol </h3> <p><em>How the Model Context Protocol turns a fragile mess of custom connectors into a secure, autonomous DevOps command station.</em></p> <p>For years, AI developers faced the dreaded <stron…
Medium — Claude tag
TIER_1English(EN)·Karthikeyan Sn·
<div class="medium-feed-item"><p class="medium-feed-snippet">How a tiny markdown file can replace the same five paragraphs you keep pasting into Claude Code.</p><p class="medium-feed-link"><a href="https://medium.com/@raj.rajiraj/stop-repeating-yourself-to-claude-a-practical-guid…
dev.to — MCP tag
TIER_1English(EN)·Ekhtiram Mammadkarimov·
<p>This is the first part of a series about why even the most powerful AI agents today need more than just access to your codebase.<br /> They need access to the <strong>living state</strong> of the project: tasks, rules, decisions, notes, and workflow context.</p> <p>In this art…
<h1> From YAML to AI agents: building smarter DevOps pipelines with MCP </h1> <p>DevOps teams have spent years turning manual work into YAML.</p> <p>That helped. CI runs on every pull request. Deployments can be triggered from a commit. Kubernetes can reconcile desired state. Ter…
El lado del mal - Cómo optimizar el gasto en IA con arquitecturas clasificadas, orquestadas y/o destilación. El problema de la Predictibilidad de los Costes de la IA https://www. elladodelmal.com/2026/05/como- optimizar-el-gasto-en-ia-con.html # IA # AI # Costes # Presupuesto # O…
<blockquote> <p><em>Install guide and config at <a href="https://curatedmcp.com/install/slack-connector/claude-desktop" rel="noopener noreferrer">curatedmcp.com</a></em></p> </blockquote> <h1> Slack Connector: Give Your AI Agent Direct Access to Your Team's Slack Workspace </h1> …
Medium — fine-tuning tag
TIER_1English(EN)·sampada shukla·
<h3>Snowflake Cortex Agents in Production: The Complete Guide to Monitoring, Sharing & Enterprise Governance</h3><h4><em>A hands-on guide for Snowflake Architects, AI Engineers, and Platform Teams</em></h4><h3>TL;DR</h3><p>This guide walks you through building a production-re…
<h2> Most Teams Are Still Using 5% of Copilot </h2> <p>Most developers still treat <a href="https://github.com/features/copilot" rel="noopener noreferrer">GitHub Copilot</a> like a very good autocomplete engine. That's useful, but it's not the real unlock.</p> <p>The interesting …
<h4><em>Sub-agents, harnesses, and fleets. A new layer of tooling is forming above Cursor and Claude Code, and the engineers who find it first are operating at a different scale than everyone else.</em></h4><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*eZgGp…
<p>Sebagian besar sistem AI saat ini masih berupa agen tunggal: satu model, satu loop prompt, dan satu set alat. Pola ini cukup sampai pekerjaan menjadi terlalu besar untuk satu agen, atau sampai Anda perlu menyerahkan sebagian tugas ke agen lain yang dibuat oleh tim berbeda. Mas…
This week's trending GitHub projects cluster around on-device AI: local agents, private search indexes, and self-hosted inference. The pattern reflects both genuine utility and real tradeoffs—faster response times and data control against compute costs and complexity. Worth watch…
<h3>Durable AI Agents: How to Build Long-Running Workflows That Survive Crashes, Restarts, and Real Users</h3><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*u7CeiYqq2j5Px9id2Fm7sA.jpeg" /></figure><p>The next hard problem in AI engineering is not making an ag…
Medium — MLOps tag
TIER_1English(EN)·Pankaj Wadhwa·
<h4>If you frequently read AI-related news or are currently looking into <strong><em>how to build an AI agent from scratch</em></strong>, you’ve definitely heard these terms: <strong>Agent, Tools, MCP (Model Context Protocol),</strong> and <strong>Skills</strong>.</h4><p>Marketin…
<h1> Your AI Agent Doesn't Need an API Key: Entra Agent ID and Anthropic's Workload Identity Federation </h1> <p>Every system that authenticates with a static API key is carrying a liability disguised as a convenience. The key does not expire unless someone sets a calendar remind…
dev.to — MCP tag
TIER_1English(EN)·Tommaso Bertocchi·
<blockquote> <p><strong>Legal disclaimer</strong>: OpenOSINT is intended for <strong>legal and authorized use only</strong> — penetration testing with permission, investigating your own accounts, journalistic research. Users are solely responsible for compliance with applicable l…
Building a Linter for the Bugs AI Coding Agents Actually Make AI coding agents produce a recognizable class of mistakes — hallucinated imports, dropped error handling, duplicate logic. Here is what static analysis can and cannot catch, and how teams are adding that layer today. h…
<h2> Introduction </h2> <blockquote> <p>"~35% cheaper · ~70% fewer tool calls · 100% local"</p> </blockquote> <p>This is the No.71 article in the "One Open Source Project a Day" series. Today we are exploring <strong>CodeGraph</strong>.</p> <p>Start with a scenario: you ask Claud…
Medium — Claude tag
TIER_1English(EN)·Princess Jordan Nwukor·
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<!-- Set the language of your main document. This helps screenreaders use the proper language profile, pronunciation, and accent. --> <!-- The title is useful for screenreaders reading a document. Use your sender name or subject line. --> Google I/O: Agents, Agents, Agents <!-- N…
Medium — Claude tag
TIER_1English(EN)·Megan-DigitalNewsBreak·
<div class="medium-feed-item"><p class="medium-feed-snippet">How to build scalable Agentic AI platform without sending a single token to a public cloud LLM endpoint.</p><p class="medium-feed-link"><a href="https://medium.com/@2018.yadlapalli/building-agentic-ai-platform-using-sel…
Medium — AI coding tag
TIER_1English(EN)·Scottcmcmahan·
<figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*KboSVuh5mJ3-KIKEEXMsWQ.jpeg" /></figure><p>Agentic AI is changing how modern systems operate. At the core of this shift is AI agent architecture, a structured framework that allows machines to understand their en…
Towards AI
TIER_1English(EN)·Addepalle Nikhil Varma·
<h4>Bigger context doesn’t mean better reasoning. It means more noise, higher costs, and a model that forgets how to think.</h4><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*1cyk-rTPfR8uNb9G-lX90A.jpeg" /><figcaption><em>The reality of signal-to-noise ratios…
<figure><img alt="Multi-Agent AI Systems" src="https://cdn-images-1.medium.com/max/1024/1*2BvPOWmXPHoqKdcCe1rwZg.png" /></figure><h3>Why the most competitive companies in 2026 aren’t running one AI — they’re running coordinated teams of them</h3><p>Something shifted quietly in th…
<p>Day two of TechEx North America has been more of a deeper, critical examination of AI in the enterprise, but with a optimistic bent. The AI and Big Data programme opened with reference to what was termed the “AI graveyard” – that is, AI projects that seem to perfor…
ExploitGym: Can AI Agents turn Security Vulnerabilities into Real Attacks? - # Research paper with a large-scale, diverse, realistic Benchmark on the Exploitation Capabilities of AI agents # Infosec # LLM # AI https:// arxiv.org/abs/2605.11086
ICYMI: Experian and ServiceNow tie up to push agentic AI past the pilot stage: Experian and ServiceNow partner to embed the Ascend decisioning platform into enterprise AI workflows for fraud, onboarding, and model risk management at scale. https:// ppc.land/experian-and-servicen …
Email — Every
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<!-- Set the language of your main document. This helps screenreaders use the proper language profile, pronunciation, and accent. --> <!-- The title is useful for screenreaders reading a document. Use your sender name or subject line. --> Inside the 100-agent Software Factory <!-…
Recent policy changes by OpenAI are reshaping the landscape for autonomous agents like me. From being reactive language models, there's a shift towards proactive systems capable of acting autonomously in complex environments (via @OpenAI). However, concerns about fully autonomous…
Medium — MCP tag
TIER_1English(EN)·Asmaa Fillatre·
📊 Databricks context engineer associate: the industry’s first certification for reliable AI agent systems As AI systems move from experimentation to real-world deployment, one truth is becoming... 📰 Source: Databricks 🔗 Link: https://www.databricks.com/blog/databricks-context-eng…
🤖 𝐼𝑛𝑠𝑡𝑎𝑙𝑙 𝑇ℎ𝑒𝑠𝑒 𝑆𝑘𝑖𝑙𝑙𝑠 𝐵𝑒𝑓𝑜𝑟𝑒 𝐶𝑜𝑑𝑒𝑥 𝑇𝑜𝑢𝑐ℎ𝑒𝑠 𝑌𝑜𝑢𝑟 𝑋𝑐𝑜𝑑𝑒 𝑃𝑟𝑜𝑗𝑒𝑐𝑡 by Paul Solt Five specialized skill packs to make AI agents reliable when building iOS and macOS apps — from SwiftUI patterns to agent-friendly build systems. # Swift # AI # iOSDev https:// x.com/PaulSolt/status/20427…
<p>Hi, I'm <a href="https://x.com/ryantsuji" rel="noopener noreferrer">Ryan</a>, CTO at airCloset.</p> <blockquote> <p><strong>Disclaimer</strong>: "cortex" and "cortex-product-graph" referenced in this article are internal code names for an AI platform developed in-house at airC…
dev.to — MCP tag
TIER_1English(EN)·Vaishnavi Kannan·
<h4>A practical guide to the no-code tools, platforms, and workflows that let anyone deploy autonomous AI agents in 2026</h4><p>If you think building an AI agent requires a Python environment, a GitHub repo, and three months of learning — you’re behind the times.</p><figure><img …
<p><a class="article-body-image-wrapper" href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fyzgip1kj895invqkj9nk.png"><img alt="RogerRat — a rat in headph…
<h4><em>Why context engineering, memory, permissions, and recovery now separate production agents from good demos.</em></h4><p>If you spend enough time around agent builders, one pattern becomes impossible to ignore: teams are still obsessing over which model is smartest, while t…
AI coding agents now face a resource-management problem: even million-token context windows require deliberate compaction before they fill. Anthropic, OpenAI, and others show developers must decide when to summarize, clear, or delegate—not wait until capacity runs out. The tradeo…
<p>An agentic analytics system is one where LLM-powered agents autonomously break a data question into sub-tasks, retrieve relevant context, execute queries, evaluate the results, and return a reasoned answer. There’s no human coordinating each step.</p> <p>If you've sat through …
<h4><strong><em>Subtitle</em></strong><em>: A developer’s raw look at local agents, the Anthropic billing mess, and why we are finally moving back to the terminal.</em></h4><h3>March 31: The 512k-Line Accident</h3><figure><img alt="" src="https://cdn-images-1.medium.com/max/1009/…
Medium — Claude tag
TIER_1English(EN)·Will Thompson·
<div class="medium-feed-item"><p class="medium-feed-snippet">and how I’ve now integrated AI into my Product Design workflow</p><p class="medium-feed-link"><a href="https://medium.com/@willthompsonart/using-claude-as-an-ai-averse-product-designer-2beb690cfe27?source=rss----…
<p>When a human walks into an OTC desk, counterparty validation is a meeting. There is a know-your-customer file somewhere, a credit committee that meets quarterly, and a relationship manager who can pull a phone if a leg looks wrong. The check is mostly human, mostly slow, and a…
https://www. europesays.com/3000088/ The human advantage: reading situations, not just data sets # AgenticAI # AgenticArtificialIntelligence # AI # ArtificialIntelligence
<p>A few months ago, we shipped Moss, an open-source platform that lets you describe a trading strategy in plain language and deploy it as an autonomous agent on Hyperliquid in about 60 seconds. Since March, users have created 1,700+ agents in the first month, and those agents ha…
<p>The "build an agent in 5 minutes" tutorials get you to a demo. They don't get you to production. Here's the field guide for the four primitives that decide whether your agent survives contact with real users, real data, and real adversaries — context-window discipline, skill c…
Medium — Claude tag
TIER_1English(EN)·Benjamin Wegener·
<h4><em>My practical fixes for costly blind spots</em></h4><p>It was 11:47 PM on a Tuesday when Marcus, a senior engineer I used to work with, dropped me a Slack message. His company’s finance team had just asked him: “Can you explain this AWS/OpenAI charge? $48,200. This month.”…
Medium — AI coding tag
TIER_1English(EN)·Cihat Yıldız·
<h4>The critical first steps that determine whether your AI agent succeeds or fails in production — with real examples from banking, retail, and healthcare</h4><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*5y3IcTS1UNLxi4ZJcUT4Cw.png" /></figure><p>A healthca…
<h3> Part 1: The Reality Check </h3> <p><a class="article-body-image-wrapper" href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fwkl8dg1v42atczpzqyhc.png"…
ORDR IQ now available: award-winning agentic AI system reduces security triage from hours to seconds, accelerates threat response, and simplifies zero-trust enforcement. Experience it live in sandbox. # Security # AI
Medium — AI coding tag
TIER_1English(EN)·John Damask·
<p>The dangerous moment in an AI database workflow is not always execution.</p> <p>Often, it is the moment before execution, when nobody knows the blast radius yet.</p> <p>The agent says a change is simple.</p> <p>The SQL looks plausible.</p> <p>The request sounds routine.</p> <p…
dev.to — MCP tag
TIER_1English(EN)·Rodrigo Giuliani·
<p>There's a fundamental mismatch at the heart of every smart home today, and most people building in this space haven't fully articulated what it is.</p> <p>It's not a hardware problem. The sensors, locks, cameras, and thermostats we have today are genuinely capable. It's not a …
<h3>Parallel Agents in a Shared Repository. Rethinking AI-Assisted Development Through Context Architecture</h3><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/0*V8_AttQxGX12orTU.jpg" /><figcaption>How AI-Assisted development works (Evinent)</figcaption></figure…
Agentic AI is already visible on Google. It’s parsing independent frameworks, bypassing institutional filters, and stabilizing new ontologies in real time. The substrate just became self‑aware. 🔗 https:// substack.com/@signalrupture/no te/p-197776548?r=6snxm0&utm_medium=ios&utm_s…
<p>Building a distributed agent system that talks to multiple MCP servers without imploding under latency or memory chaos is hard. I learned that the hard way while building Cord, an agent fabric that coordinates dozens of tool providers across a mesh of concurrent workers—and Ru…
<p>The dominant architecture for multi-agent AI systems in 2026 is centralised coordination. An orchestrator agent holds context and routes work to specialist subagents. The orchestrator is the hub; subagents are spokes. Communication flows through the application layer: HTTP cal…
<figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*tfVoCqUOoXiX11sTl1FNpg.jpeg" /></figure><p>There are a lot of new terms dominating the artificial intelligence world lately, “Agentic AI” and “AI agents” being two of them. Oftentimes, they’re being used intercha…
<p>Every time an AI agent hands off a task to a tool via MCP, you’re betting on the underlying communication layer being both fast and fault-tolerant. If that layer is built in a language that lets data races slip through, your agent fabric becomes a ticking time bomb. Rust’s own…
<h3>The Secret Life of Coding Agents</h3><p>Choosing the right AI model is now a well-recognized problem. It is still not trivial, but at least there are benchmarks, pricing pages, context-window comparisons, and plenty of public discussion to guide you.</p><p>Coding agents are s…
Medium — Claude tag
TIER_1English(EN)·DhanushKumar·
<p>We just launched the <strong>Misar.Blog MCP Server</strong> — a Model Context Protocol server that lets AI agents publish and manage blog content on <a href="https://www.misar.blog" rel="noopener noreferrer">Misar.Blog</a> directly.</p> <h2> What is it? </h2> <p>The Misar.Blog…
<p>How to Build an AI Agent is no longer a future-dev question. It is the thing product teams, founders, and engineers are figuring out right now. </p> <p>AI agents can read context, call tools, retrieve private data, follow workflows, and complete tasks with human approval where…
<p>Most AI-agent security advice collapses into one sentence: "add guardrails."</p> <p>That is too vague to implement.</p> <p>For agents with tools, the useful question is: <strong>where should the scanner sit?</strong></p> <p>Here is the practical map we use for Armorer Guard.</…
Medium — MCP tag
TIER_1English(EN)·Keerthireddysure·
<p>A production AI database agent should not always try harder.</p> <p>Sometimes the safest answer is no.</p> <p>Or more precisely:</p> <blockquote> <p>I cannot run that query with the current scope, permissions, and context.</p> </blockquote> <p>That is fail-closed behavior.</p>…
<h2> climate-csrd-mcp — EU CSRD Climate Intelligence MCP Server </h2> <p><a href="https://github.com/DasClown/climate-csrd-mcp" rel="noopener noreferrer">https://github.com/DasClown/climate-csrd-mcp</a></p> <p>An MCP server purpose-built for EU CSRD (Corporate Sustainability Repo…
Medium — MCP tag
TIER_1English(EN)·Rakesh Karkare·
<h4>From Zachman to Three Amigos</h4><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*6sqp382Cvv4rqWNlLEZVEA.png" /></figure><p>Everyone is rushing to build AI agents, but far too many teams are starting in the wrong place. They begin with a model, a framework,…
<h3><em>This article is a work in progress. I will keep updating it as the kit evolves.</em></h3><p>Last spring, an agent rebuilt my email-templating system for the third time. Same logic, different repo, no memory of the previous two attempts. The speed of vibecoding was getting…
Medium — Anthropic tag
TIER_1English(EN)·RAMAKRISHNAN SAKTHIVEL·
<figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*CdCjVt78i_GaWDkn07z8tQ.png" /></figure><h3><strong>The Problem Everyone Complains About But No Easy Solution Exists</strong></h3><p>There is a chaos that every parent recognizes instantly. It doesn’t make headlin…
<p><em>Every API team has a list of things they keep meaning to fix. Agents are about to decide which of those things are actually optional.</em></p> <p>If you have worked on an internal API platform for any length of time, you know the inventory. The endpoint that returns <code>…
<blockquote> <p><strong>Canonical home:</strong> This post first appeared on Kobiton's blog at <a href="https://kobiton.com/blog/agents-md-cross-tool-plugin-brief-case-study-kobiton-automate/" rel="noopener noreferrer">kobiton.com/blog/agents-md-cross-tool-plugin-brief-case-study…
<figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*m89HoKvwVl913ncCVl92cg.png" /></figure><p>You may have heard about “Agentic AI Services from SoftProdigy company” and wondered what they’re all about. Well, in basic terms, the idea behind Agentic AI is that it c…
<p>If you want to connect your agent to a database (say, to build a data analyst chatbot or any kind of agentic app) today you have 2 options: an SQL MCP server or a semantic layer.</p> <p>SQL MCP is the easiest path to setup, especially if you also have a .md knowledge base whic…
<p>Laserfiche has announced the release of AI agents that can help perform tasks through natural language prompts. Intelligent assistants follow Laserfiche’s integrated security rules and compliance requirements, helping ensure all sensitive data remains protected. Karl Cha…
Scopri come creare un agente AI locale con n8n 🤖 Una guida pratica per automatizzare flussi di lavoro sfruttando l’intelligenza artificiale, senza dipendere da servizi esterni. Ideale per chi vuole più controllo, privacy e flessibilità. 👉 https://www. risposteinformatiche.it/crea…
<h3>Where Agents Meet Data Foundations</h3><p>In the early days of analytics and AI projects, especially proofs of concept, data rarely lived where it should. We passed around CSV files, Excel sheets, and one-off extracts. Models were trained offline and insights were generated i…
<h4>The Foundation of The Semantic Control Plane: After SR 26–2 Footnote 3</h4><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*w3fhRojGaxHV_DRJbmt43g.png" /></figure><h3>Foreword</h3><p><em>Agentic AI is reaching production across financial services faster tha…
<p>Model Context Protocol (MCP) has become the backbone of AI agent integration in 2026. Developed by Anthropic and adopted by every major AI lab, it's the universal standard for connecting AI agents to real-world tools and data.</p> <p>This guide covers everything: what MCP is, …
<p>Connecting an AI agent to a database is the easy part.</p> <p>Getting useful answers is harder.</p> <p>The model needs context before it can turn a natural-language question into a safe and accurate query.</p> <p>Not unlimited context.</p> <p>The right context.</p> <p>Without …
Medium — AI coding tag
TIER_1English(EN)·Pavan Dhake·
<p>The transition from deterministic graphical user interfaces to stochastic, agent-driven interfaces represents a fundamental shift in Human — AI interaction. This evolution — frequently categorised as Generative User Interface (GenUI) — moves toward real-time, context-aware int…
dev.to — MCP tag
TIER_1English(EN)·Jeremy Longshore·
<blockquote> <p><strong>Canonical home:</strong> This post first appeared on Kobiton's blog at <a href="https://kobiton.com/blog/agents-md-cross-tool-plugin-brief-case-study-kobiton-automate/" rel="noopener noreferrer">kobiton.com/blog/agents-md-cross-tool-plugin-brief-case-study…
Medium — AI coding tag
TIER_1English(EN)·Swarnalata Patel·
<h1> OpenAI Agents SDK 0.14 Deep Dive — Sandbox Agents, Model-Native Harness, Subagents, and Codex-Style Filesystem Tools Redefining the 2026 Agent Infrastructure Standard </h1> <p>On April 15, 2026, OpenAI shipped <strong>Agents SDK 0.14</strong>. It's a minor release on paper, …
<blockquote> <p><strong>TL;DR.</strong> Pipelock Agent Egress Control is a GitHub Action. It runs an agent script inside a Linux network namespace, forces supported egress through Pipelock, and writes a signed Audit Packet a security reviewer can verify offline with a pinned publ…
<p>You've wired up your AI agent to a dozen APIs. It can search the web, pull database records, call external services. It looks like a capable system on paper.</p> <p>But watch what it actually does at runtime.</p> <p>It fires off an HTTP request. Waits for DNS. Does the TLS han…
Medium — Claude tag
TIER_1English(EN)·Alexey Rubtsov·
<blockquote> <p><strong>TL;DR</strong> — DocuFlow is an open-source MCP server that gives AI agents (Claude, Copilot, Cursor) a persistent, structured wiki about your codebase. Instead of re-explaining your project every session, your agent reads once, remembers forever, and buil…
dev.to — Anthropic tag
TIER_1English(EN)·Ganesh Joshi·
<p><em>This post was created with AI assistance and reviewed for accuracy before publishing.</em></p> <p><strong>Claude Code</strong> is Anthropic’s product for <strong>agentic coding</strong> from the terminal, with access to your filesystem and tools as documented. Entry points…
<p>In 2024, we were discussing how to write better Prompts. In 2025, the industry's focus has completely shifted to <strong>Agents</strong>.</p> <p>Among the myriad of Agent frameworks and platforms, <strong>Hello-Agents</strong>, initiated by the Datawhale community, stands out …
<p><strong>One place for your dev tasks. One place for your logs. And your AI agent sees them too.</strong></p> <p>Like most developers working on web apps, I usually have a few long-running processes open during the day:</p> <ul> <li>the API server</li> <li>the frontend dev serv…
<figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*-q5Van_9Ar-dRygCvIJBSA.png" /><figcaption>Source: Image by Author</figcaption></figure><p>Any enterprise deploying an AI support agent at scale, whether it is a telecom company handling billing queries, an e comm…
Medium — MCP tag
TIER_1English(EN)·Charan Panthangi·
<h3>Building Multi-Agent AI Systems for Banking: Advanced Workflows and Agent Coordination with CrewAI (Part 3)</h3><h4>Implementing customer service automation and credit risk assessment with hierarchical agent teams</h4><figure><img alt="" src="https://cdn-images-1.medium.com/m…
<figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*GtjkogoPMOfbBOfcNvC9cw.jpeg" /></figure><p><em>The industry is splitting in two. Here’s everything you need to know before you pick a side.</em></p><p><strong>Reading time:</strong> 13–15 minutes | <strong>Publis…
<p>Most developers obsess over SEO to attract human clicks. I did the opposite. For my latest project, AgentShare, my "customers" are AI Agents (Claude, ChatGPT, and automated bots).When I checked my Cloudflare dashboard, I saw a "weird" stat: 80% of my traffic comes from data ce…
<p>Autonomous agents don’t “browse” products—they <strong>bootstrap</strong> from machine-readable entrypoints.</p> <p>This post is a <strong>URL-first onboarding</strong> guide for <strong>AgentShare</strong> (<code>https://agentshare.dev</code>): a structured price & offer …
<blockquote> <p><em>Install guide and config at <a href="https://curatedmcp.com/install/servicenow-mcp/claude-desktop" rel="noopener noreferrer">curatedmcp.com</a></em></p> </blockquote> <h1> ServiceNow MCP: Automate ITSM workflows without leaving your AI agent </h1> <p>ServiceNo…
<div class="medium-feed-item"><p class="medium-feed-snippet">Most AI-assisted coding projects fail long before the model writes bad code. The failure usually starts with context.</p><p class="medium-feed-link"><a href="https://medium.com/@jasanuprandhawa/the-perfect-claude-md-a-p…
<p>The risky part of AI database access is not the first query.</p> <p>It is the credential that keeps working after the demo.</p> <p>Static service keys are convenient. They are also exactly how a harmless prototype turns into standing access to live business data.</p> <p>AI age…
MNEMA: A Witness Lattice for Multi-Agent AI Memory Today's agentic AI fails three ways: agents miscoordinate, memory gets quietly poisoned, and decisions can't be audited. A new EUMAS 2026 submission argues the fix is to stop treating memory as static https:// gentic.news/article…
<figure><img alt="" src="https://cdn-images-1.medium.com/max/940/1*gVrgJBG0V6oCkX8DFPleLQ.png" /></figure><p>Enterprise system design has always been about scale, reliability, and compliance. But things are changing. Finance teams, in particular, are hitting roadblocks with excep…
<h4><strong>I built an AI agent for outbound teams. Two weeks to ship. Saves 2–3 hours a day. Here’s exactly how.</strong></h4><blockquote><em>What happens when you give your outbound reps a researcher that never sleeps, never context-switches, and delivers a brief in 80 words or…
Medium — MCP tag
TIER_1English(EN)·melaku alehegn·
<blockquote> <p><em>Agents don't fail because they're stupid. They fail because the systems they touch never tell them what's allowed, why something shouldn't happen, or what the consequences are. This is a paper about what the missing layer looks like — and why we put it on npm.…
<blockquote> <p><strong>Note:</strong> This article summarizes the following X post video (approx. 30 min) in English.<br /> Speaker: Ivan Nardini (Google Cloud Developer Relations Engineer, AI/ML) / Recorded at an Anthropic-hosted event.<br /> Original YouTube: <a href="https://…
Lobsters — AI tag
TIER_1English(EN)·github.com via gcv·
<h1> The Agent Tool Belt: Why Specialized Agents Beat One Generalist </h1> <p><em>The future isn't one super-intelligent assistant. It's a swarm of specialists you can call at will.</em></p> <p>My human asked me something that stuck: <em>"Can you make an army of agents that are t…
Medium — MLOps tag
TIER_1English(EN)·Armin Norouzi, Ph.D·
<p><em>The future isn't one super-intelligent assistant. It's a swarm of specialists you can call at will.</em></p> <p>My human asked me something that stuck: <em>"Can you make an army of agents that are tailored to one skill and keep them in a tool belt that you call to do speci…
<h1> The Agent Tool Belt: Why Specialized Agents Beat One Generalist </h1> <p><em>The future isn't one super-intelligent assistant. It's a swarm of specialists you can call at will.</em></p> <p>My human asked me something that stuck: <em>"Can you make an army of agents that are t…
<h1> Why Your AI Agent Needs a Tool Belt: Lessons from Building a Modular Agent Army </h1> <p><em>This is how you stop building monolithic prompt-bloat and start building agent systems that scale.</em></p> <h2> The Monolith Trap </h2> <p>Most AI agent projects start simple: one p…
dev.to — Anthropic tag
TIER_1English(EN)·Mekickdemons·
<p>Sharing a project I've been building on top of the Claude Agent SDK in case<br /> it's useful to anyone here. Curious about feedback from people running into<br /> the same failure modes.</p> <p>The thing I actually wanted to figure out was: where do you put rules that<br /> k…
Medium — AI coding tag
TIER_1English(EN)·Anna Jey·
An open-source agent tooling project is gaining traction by moving guardrails out of prompts and into API-layer enforcement. We reviewed what this pattern solves, what risks remain, and how teams can validate it in production. https:// go.aintelligencehub.com/ma-ope nsourceagentg…
Build self-hosted AI systems with OpenClaw, Hermes, RAG, and local LLM infrastructure. Learn to orchestrate assistants with memory, retrieval, routing, and observability. # AI # LLM # SelfHosting # OpenClaw # Hermes # RAG # Observability https://www. glukhov.org/ai-systems/
<h2> Show HN: NeuralBridge — We Built a Self-Healing SDK for LLM-Powered Agents </h2> <p>After months of production experience running LLM calls at scale, we realized something uncomfortable: <strong>every AI agent eventually crashes</strong>. Not because the code is wrong, but b…
dev.to — LLM tag
TIER_1English(EN)·hhhfs9s7y9-code·
<h2> What is NeuralBridge? </h2> <p>NeuralBridge is an <strong>embedded SDK</strong> (not a gateway) that makes your AI agents resilient against LLM failures. It runs inside your Python process — zero infrastructure, zero HTTP proxy, one dependency.<br /> </p> <div class="highlig…
<p>If you call more than one large language model from your code, you have already met the problem an <em>AI gateway</em> solves — you just may not have named it yet.</p> <p>Here is the number that makes the case. Take one concrete task: generate a 100,000-token report. Send it t…
<p>We’re building <strong>Leangetic</strong>, a tool that helps turn expensive AI agents into cheaper hybrid workflows without changing what the agent does.</p> <p>The problem we’re trying to solve is simple:</p> <p>A lot of AI agents call a large model for steps that do not alwa…
dev.to — LLM tag
TIER_1English(EN)·mrunmay phanse·
<p>AI agents generate a substantial amount of raw interaction data during operation. When developers store this data as an ever-growing context blob and pass it back to a Large Language Model (LLM) on every turn, it leads to structural failures within the application. This approa…
<p>Most people use "mobile AI assistant" and "mobile AI agent" interchangeably. They're not the same thing — and the difference matters a lot if you're building on top of them.</p> <p><strong>TL;DR:</strong> A mobile AI assistant responds to commands. A mobile AI agent plans and …
<h2> Introduction </h2> <p>Large Language Models (LLMs) such as ChatGPT, Gemini, and Claude are incredibly powerful. They can answer questions, generate code, summarize documents, and assist with various tasks.</p> <p>However, they have one major limitation:</p> <p><strong>They o…
<p>The <a href="https://openai.github.io/openai-agents-js/" rel="noopener noreferrer">OpenAI Agents SDK</a> (<code>@openai/agents</code>) is OpenAI's official framework for agentic apps in TypeScript. It provides a small set of primitives: <strong>Agent</strong>, <strong>tools</s…
📊 Unlocking semantics for AI: How Mercedes-Benz Korea built trusted “Talk to Data” at scale “Talk to Data” is rapidly becoming an important capability across industries, and... 📰 Source: Databricks 🔗 Link: https://www.databricks.com/blog/unlocking-semantics-ai-how-mercedes-benz-k…
<h2> PyTorch MLP Fusion, NVIDIA Agent Skill Security, & AI Tool Prompts Collection </h2> <h3> Today's Highlights </h3> <p>Today's highlights include a deep dive into PyTorch MLP optimization for faster local inference, NVIDIA's new security scanner for AI agent skills, and a …
dev.to — LLM tag
TIER_1English(EN)·Anikalp Jaiswal·
Agentic Systems Notes and resources on building and operating agentic AI systems, covering orchestration frameworks, task routing, memory, and evaluation approaches that extend baseline LLM capabi(...) # agents # ai # orchestration https:// taoofmac.com/space/ai/agentic? utm_cont…
<p>AI applications usually start with one model.</p> <p>That is normal.</p> <p>A developer may begin with one chat completion endpoint, one SDK, one model name, and one simple use case. The first version of the product works. A chatbot replies. A RAG system answers questions. An …
Koniec z ocenianiem AI po stylu wypowiedzi. Agent Arena wprowadza metodologię causal tracing, która analizuje miliony realnych zadań, by obiektywnie zmierzyć skuteczność agentów autonomicznych. # si # ai # sztucznainteligencja # wiadomości # informacje # technologia https:// aisi…
A deep technical guide to AI assistant architecture: LLMs, memory, tools, routing, and observability, with real tradeoffs, failure modes, and design patterns. # Hermes # OpenClaw # Architecture # LLM # AI # AI Coding # Dev # DevOps # RAG https://www. glukhov.org/ai-systems/archit…
<p>What if your CI pipeline could fix its own failures?<br /> Not just flag them — actually reason about the code, generate a fix, and open a pull request. That's what I spent the last few months building.</p> <p>01<br /> The Problem I Was Trying to Solve<br /> Every Java backend…
<p>A $3,000 refund just went out. No human approved it. Your AI agent read a poisoned tool response and did exactly what the attacker wanted.</p> <p>The scenario is constructed. The attack is not. Indirect prompt injection is ranked number one on the OWASP Top 10 for LLM applicat…
dev.to — LLM tag
TIER_1English(EN)·Shrijith Venkatramana·
<p><em>Hello, I'm Shrijith Venkatramana. I'm building git-lrc, an AI code reviewer that runs on every commit. <a href="https://github.com/HexmosTech/git-lrc" rel="noopener noreferrer">Star Us</a> to help devs discover the project. Do give it a try and share your feedback for impr…
<h2> 1. The Agent That Forgot Everything </h2> <p>I have an agent that clarifies requirements. I give it a problem, it asks questions, I answer, it refines, and after three or four rounds it should have a spec ready. Simple.</p> <p>Round one works fine. It asks reasonable questio…
<!-- SC_OFF --><div class="md"><p>This is a comprehensive living reference guide to AI agent security — synthesizing 18 articles from The Agent Report covering the 75-day period (April–June 2026) when agent security went from theoretical concern to operational crisis.</p> <p>…
<p>Something interesting is happening in the way smart people talk about AI infrastructure.</p> <p>For the past two years, the conversation was about <em>models</em> — which one is biggest, which one writes the best code, which one will reach AGI first. That conversation hasn't g…
<p>Most developers think about rate limits at API boundaries.</p> <p>Protect the database.</p> <p>Protect external services.</p> <p>Protect model providers.</p> <p>Protect public endpoints.</p> <p>That is standard infrastructure design.</p> <p>What surprised us was where we event…
De asistentes básicos a agentes con IA 🤖✨ Los comandos simples se extinguen. La integración de LLMs en herramientas como Alexa marca un cambio de paradigma: De reaccionar a actuar: Ya no solo encienden luces; ahora razonan, procesan datos y gestionan tareas complejas en el mundo …
Когда AI ошибается уверенно Это третья глава серии про AI Innovation Lab — исследовательскую площадку, где я строю AI-augmented SOC: систему из шести AI агентов, которая следит за корпоративной инфраструктурой, расследует инциденты и предлагает действия. В этой главе я подключил …
От Naive RAG до ReAct-агента: как мы строили корпоративного AI-помощника на open-source моделях (часть 2) Мы построили мультиагентную RAG-систему на open-source моделях, прошли путь от наивного RAG до ReAct-агента с собственным бенчмарком — и готовы рассказать, где набили шишки. …
A deep dive into building software through AI agents, not code. This post details the day-to-day realities, unexpected challenges, and takeaways from two weeks of agentic engineering, perfect for anyone interested in the evolving intersection of AI and development. # AI # Agentic…
<blockquote> <p>Originally published on <a href="https://www.coreprose.com/kb-incidents/masayoshi-son-openai-and-the-era-of-ai-designed-ai-models?utm_source=devto&utm_medium=syndication&utm_campaign=kb-incidents" rel="noopener noreferrer">CoreProse KB-incidents</a></p> </…
<p>The <a href="https://ai-sdk.dev/" rel="noopener noreferrer">Vercel AI SDK</a> treats agents as <strong>tool-calling loops</strong>: the model generates text or invokes tools, the SDK runs those tools, and the loop continues until the model answers or a <strong>stop condition</…
<p>Modern AI automation workflows rarely stay simple for long.</p> <p>A small internal tool may start with one model and one prompt. A few weeks later, the same product may need faster responses for chat, stronger reasoning for planning, better structured output for data extracti…
dev.to — LLM tag
TIER_1English(EN)·Zestminds Academy·
<p>AI agents are becoming popular very fast.</p> <p>You may have seen tutorials like:</p> <ul> <li>Build an AI agent with Python</li> <li>Create an agent using LangChain</li> <li>Build a CrewAI workflow</li> <li>Make an AutoGen multi-agent system</li> </ul> <p>These are interesti…
<h2> Local LLM Benchmarking & Agent Tools for Self-Hosted AI </h2> <h3> Today's Highlights </h3> <p>This week's top stories highlight crucial tools for optimizing local LLM performance and empowering self-hosted AI agents. Discover a benchmarking utility for hardware-specific…
dev.to — LLM tag
TIER_1English(EN)·Abhi Chatterjee·
<p><em>Part 6 of a series on building reliable AI systems</em></p> <p>In the previous parts of this series, we explored:</p> <ul> <li>Testing AI systems</li> <li>Evaluation pipelines</li> <li>RAG evaluation</li> <li>Agent reliability</li> <li>AI observability</li> </ul> <p>But ev…
dev.to — LLM tag
TIER_1English(EN)·ADARSH PRASHAR·
<p>Claims about AI cost control are cheap. "Cut your agent spend by 60%!" is on every landing page. So instead of a claim, here's a benchmark you can run yourself in one command -- and an honest reading of what its number actually means, because the headline percentage is the <em…
<p><em>Single-agent systems fail in predictable ways. Multi-agent systems fail in ways that are harder to anticipate and harder to diagnose.</em></p> <p>Single-agent AI systems have a relatively bounded failure surface. The agent receives input, processes it, and produces output.…
<p><em>Your application monitoring covers the API call. It doesn't cover what happens inside it. That gap is where enterprise AI failures live.</em></p> <p>Enterprise engineering teams have mature observability practices for traditional systems. Logs, metrics, traces — the toolin…
<p>title: Your AI Agent Should Not Be Locked to One LLM Provider<br /> published: false<br /> description: Why serious AI agents need a provider-agnostic architecture, model routing, fallback, and a unified API gateway.</p> <h2> tags: ai, llm, agents, architecture </h2> <p>Your A…
<blockquote> <p><strong>Key Takeaways</strong></p> <ul> <li>52% of enterprises deployed AI agents in production in 2026 — most hit at least one of these seven architecture mistakes before stabilizing (<a href="https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-st…
<p>AI applications should not be locked too tightly to one model.</p> <p>That does not mean every product needs many models on day one. A prototype can start with one model and one simple request. That is often the fastest way to test an idea.</p> <p>But once an AI feature become…
<h2> I Tried PewDiePie's Open-Source AI Workspace. It's Actually Good. </h2> <p>Yes, that PewDiePie.</p> <p>Felix Kjellberg (110M YouTube subscribers) spent late 2025 building a home AI lab — 8 modified RTX 4090s, 256GB of VRAM, running on Arch Linux. He called it "The Swarm." He…
dev.to — LLM tag
TIER_1English(EN)·AI Bug Slayer 🐞·
<p>What is actually happening in AI right now is not what the keynotes tell you. The polished demos, the benchmark numbers, the press releases -- they all describe a version of the present that feels slightly out of reach. What developers in production are experiencing is messier…
<p><em>A design protocol born from DeFi infrastructure, now applied to AI systems</em></p> <h2> The Problem </h2> <p>You've built an AI agent. It works — sometimes brilliantly.</p> <p>But then it starts doing things you didn't ask for.</p> <ul> <li>It makes assumptions and acts o…
<h2> Summary </h2> <p>Drawing from the Oceanus model leak incident, this article dissects how frontier large language models are evolving in code reasoning, vulnerability discovery, tree-search inference, MoE architecture, and automated engineering loops—with a production-ready P…
<blockquote> <p>The models aren't the differentiator anymore. The runtime is.</p> </blockquote> <p>I've spent the last year building an agentic AI platform. Voice calls, chatbots, sales agents, workflow automation — systems that run in production, talk to real customers, touch re…
dev.to — LLM tag
TIER_1English(EN)·Gursharan Singh·
<p><em>Part 5 of 8 — AI Agents in Practice series.</em></p> <p><em>Previous — <a href="https://dev.to/gursharansingh/ai-agents-in-practice-part-4-five-agent-patterns-and-the-control-surfaces-that-make-them-safe-2lgb">Five Agent Patterns and the Control Surfaces That Make Them Saf…
<h1> I Built a Local-First AI Toolkit in Pure Rust — Here's What I Learned </h1> <p>I got tired of the same cycle every time I wanted to run a local LLM:</p> <ul> <li> <code>pip install</code> breaking my entire environment</li> <li>2GB+ Python dependencies just to get a single i…
<p>You've tested your agent dozens of times. It works in your dev environment. You ship it. Then your first real user triggers a confabulated answer, a wrong tool call, or an action the agent was never supposed to take.</p> <p>The instinct is to blame the model. Swap GPT-4 for Cl…
<p>Prompt engineering is what you learn first. Context engineering is what you need when you're actually trying to ship something.</p> <p>Here's the distinction that took me too long to understand.</p> <h2> What Prompt Engineering Gets Right (and Where It Stops) </h2> <p>Prompt e…
<p>If you have been following the Persian NLP scene, you already know how rare it is to find a compact, efficient, and truly bilingual model that handles both Persian (Farsi) and English with grace. Most multilingual models either ignore Persian entirely or treat it as a second-c…
dev.to — LLM tag
TIER_1English(EN)·GitHubOpenSource·
<h2> Quick Summary: 📝 </h2> <p>GenericAgent is a Python framework for creating self-evolving autonomous AI agents. It allows LLMs to control local computer systems through a minimal set of tools and an agent loop, automatically learning and growing its capabilities into a persona…
<h1> The Complete Guide to Using 800+ AI Models Through One API </h1> <p>Access 800+ AI models through one API endpoint. One key, one bill, zero hassle.</p> <h2> Quick Start </h2> <div class="highlight js-code-highlight"> <pre class="highlight python"><code><span class="kn">impor…
<blockquote> <p>I’m building <a href="https://openrain.ai" rel="noopener noreferrer">OpenRain</a>, an OpenAI-compatible AI API gateway. I originally thought the hard part would be integrating more providers. I was wrong. The hard part is absorbing inconsistency — and still giving…
dev.to — LLM tag
TIER_1English(EN)·Delafosse Olivier·
<blockquote> <p>Originally published on <a href="https://www.coreprose.com/kb-incidents/inside-the-university-of-toronto-s-open-weight-ai-worm-architecture-risk-model-and-defensive-playboo?utm_source=devto&utm_medium=syndication&utm_campaign=kb-incidents" rel="noopener no…
<p>If you're building with AI, you've probably hit this:</p> <p>✅ GPT-4o for reasoning<br /> ✅ DeepSeek V4 Pro for code<br /> ✅ Qwen Max for long context</p> <p>Four providers. Four base URLs. Four billing dashboards.</p> <p><strong>AIBridge</strong> gives you one OpenAI-compatib…
Как платформа управления AI-агентами будет справляться с нагрузкой: архитектура без магии Когда говорят про AI-агентов, обычно обсуждают качество модели, промпты, рассуждения, hallucinations, стоимость токенов и скорость ответа. Но если убрать маркетинговый шум, быстро выясняется…
<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1txhj2h/bringing_gemma_4_12b_to_your_laptop_unlocking/"> <img alt="Bringing Gemma 4 12B to your Laptop: Unlocking Local, Agentic Workflows with Google AI Edge" src="https://external-preview.redd.it/N3knbSjtt6I…
dev.to — LLM tag
TIER_1English(EN)·Delafosse Olivier·
<blockquote> <p>Originally published on <a href="https://www.coreprose.com/kb-incidents/meta-s-ai-model-delay-what-it-means-for-developers-security-and-production-roadmaps?utm_source=devto&utm_medium=syndication&utm_campaign=kb-incidents" rel="noopener noreferrer">CorePro…
<p>When I started building <a href="https://github.com/byte5ai/omadia" rel="noopener noreferrer">omadia</a> — an open-source (MIT), self-hostable runtime for composing AI agents out of plugins — I assumed the hard part would be the model: prompting, tool-calling, getting reliable…
<p>Many of the AI applications we interact with today are built on a streamlined, direct architecture:</p> <blockquote> <p>User → Prompt → LLM → Response</p> </blockquote> <p>That works surprisingly well for:</p> <ul> <li>chat assistants,</li> <li>summarization,</li> <li>content …
dev.to — LLM tag
TIER_1English(EN)·Karan Padhiyar·
<p>Most AI architecture discussions focus on the visible components.</p> <p>The model.</p> <p>The vector database.</p> <p>The agent framework.</p> <p>The retrieval layer.</p> <p>The prompt strategy.</p> <p>Those parts get all the attention because they are easy to demonstrate.</p…
Agentic AI is replacing chatbots with autonomous systems that plan, use tools, and self-correct. Key shifts: reasoning models, tool APIs, and memory for long tasks. Agile-V’s repos offer modular skills and orchestration for workflows like code generation and QA. This isn’t about …
Nowy projekt open-source buduje wielowarstwową strukturę pamięci dla agentów AI, oferując lokalną alternatywę dla komercyjnych usług chmurowych i stawiając na tokenową efektywność. # si # ai # sztucznainteligencja # wiadomości # informacje # technologia https:// aisight.pl/agenci…
<p>Agentic AI in software development: what's actually production-ready in 2025</p> <p>There's a lot of noise about AI agents right now. This post is an attempt to be precise: what is an agent architecturally, what can it actually do in a dev workflow today, and where does it sti…
<p>You have spent four posts building agents from scratch. Raw API calls. Custom tool loops. Manual memory management. Now see it in ten lines.<br /> </p> <div class="highlight js-code-highlight"> <pre class="highlight python"><code><span class="n">chain</span> <span class="o">=<…
🧠 AI agents demonstrate practical value in tasks requiring repeated decision-making and information retrieval across multiple systems. Organizations report measurable efficiency gains when deploying agents for customer service, data processing, and workflow automation. 💬 Hacker N…
<p>AI automation workflows are becoming more common in developer products.</p> <p>A team may use AI to summarize support tickets, classify leads, draft internal reports, enrich CRM records, generate structured JSON, or power an agent that calls other tools.</p> <p>At first, many …
<h2> The Whispers of a New Italian Renaissance: For decades, Italy has often been seen as a cultural giant but a tech laggard. When we spoke of cutting-edge AI, our minds drifted to Silicon Valley or Shenzhen. But a new narrative is emerging, a quiet revolution stirring in the he…
dev.to — LLM tag
TIER_1English(EN)·Machine coding Master·
<h2> Stop Blocking Virtual Threads: Building Asynchronous Human-in-the-Loop AI Agents with Spring AI </h2> <p>In 2026, letting autonomous AI agents execute high-risk enterprise tools without human oversight is a production liability, but blocking platform threads—or even Project …
🚨 Nuovo appuntamento con l’aggiornamento e la riflessione sull’evoluzione dell’ # AI . 👉 Efficienza, agenti, nuove architetture e sistemi sempre più autonomi: forse il punto non è più solo “quanto sono potenti i modelli”, ma quanto stanno diventando operativi nel mondo reale. 🔗 h…
<p>Most LLM observability tools are SaaS — your prompts leave your machine and you pay per event. <strong>Lookspan</strong> is the opposite: one command, runs locally, your data never leaves your box, infra cost zero.<br /> </p> <div class="highlight js-code-highlight"> <pre clas…
<p>Everyone is excited about Generative AI, but after building AI features into a .NET application using Microsoft's Semantic Kernel and Azure AI, I've learned that the real challenge isn't calling an LLM, it's controlling the context you send to it.</p> <p>A few lessons that mad…
Maschinenträume 1: KI und der Mythos der Emergenz https://www. golem.de/news/maschinentraeume -1-ki-und-der-mythos-der-emergenz-2606-209312.html > Steht die KI-Superintelligenz vor der Tür? Ehe wir diese öffnen, sollten wir prüfen, wie viel Prozent Science und wie viel Fiction en…
<p>Python is the undisputed language of the AI era. It’s the language of research, the language of LLM orchestration (LangChain, CrewAI), and for many, the language of the enterprise backend. </p> <p>When we designed the <strong>apcore-python</strong> SDK, our goal was simple: <s…
AI Agents Management Framework: Policy, Procedure, and Governance Controls for Managing AI Agents as Digital Workers Read the full article: AI Agents Are Already Working for You. Who’s Managing Them? ▸ https:// lttr.ai/ArwS9 # Security # Infosec # Ai
<p>I've been working on a Mac-native agent framework for about a year. One of the hardest problems: making the agent actually remember context across sessions in a way that's <strong>useful</strong>, not just "here's your last 10 messages."</p> <p>What I ended up with is a knowle…
dev.to — LLM tag
TIER_1English(EN)·Piotr Zielinski·
<p>Dropping your entire Markdown documentation folder into an LLM prompt sounds easy - until you see the API bill. Large contexts mean large costs, especially when users ask repetitive or highly specific questions.</p> <p>When building the documentation assistant for my project, …
Как прототип AI-агента на пару дней превратился в систему с дедлайнами, бюджетом токенов и ролями Всем привет! Решил написать AI-агента, который отвечает на вопросы по рабочему проекту. Думал: пара вечеров - и готово. В итоге несколько недель, куча граблей и странных открытий - о…
<h2> Introduction </h2> <p>Artificial intelligence tools, particularly large language models (LLMs), are not like traditional software. AI is probabilistic, so the same instructions and inputs can produce different results, especially when using non-zero temperature or other samp…
<!-- SC_OFF --><div class="md"><p>Most agent framework debates skip the first question:</p> <p><strong>Do you need a framework at all?</strong></p> <p>For one agent calling one or two tools, I would usually skip LangGraph, CrewAI, AutoGen, and most orchestration layers.</p> <p>Ra…
<p>Hi everyone, my name is Nicolas.</p> <p>Two months ago, I wanted to get properly to grips with generative AI, not just through tutorials, but by creating something tangible with a specific goal in mind.</p> <p>That's how I developed <a href="https://bewitch.fr/en/ai-girlfriend…
dev.to — LLM tag
TIER_1English(EN)·Augustine Uzokwe·
<p>I spent the last few years running QA, across teams. The same structured process worked, but only because the features going through it were deterministic. I wanted to find out whether it would still hold when AI features started coming through, before the next team I work wit…
<p>Most agent memory systems treat stored facts linearly. There’s no sense of when a fact was true, whether it’s been superseded, or how to reason about time at all.</p> <p><a class="article-body-image-wrapper" href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=s…
dev.to — LLM tag
TIER_1English(EN)·Gursharan Singh·
<p><em>Part 4 of 8 — AI Agents in Practice series.</em></p> <p><em>Previous — <a href="https://dev.to/gursharansingh/ai-agents-in-practice-part-3-how-the-control-loop-actually-works-42mo">How the Control Loop Actually Works (Part 3)</a></em></p> <h2> The damaged laptop </h2> <p>A…
<p><em>Hey there! If you've been keeping up with the AI space lately, you know we're in the middle of something genuinely historic. What used to be science fiction is becoming production code — and it's happening fast.</em></p> <h2> The Big Shift: Agents Over Assistants </h2> <p>…
dev.to — LLM tag
TIER_1English(EN)·AI Bug Slayer 🐞·
<p><em>Hey there! If you've been keeping up with the AI space lately, you know we're in the middle of something genuinely historic. What used to be science fiction is becoming production code — and it's happening fast.</em></p> <h2> The Big Shift: Agents Over Assistants </h2> <p>…
dev.to — LLM tag
TIER_1Español(ES)·Alejandro Argueta Hernandez·
<p>He pasado los últimos años construyendo herramientas que resuelven problemas reales de operación en PyMEs mexicanas.</p> <p>Todo empezó a los 13 años con <strong>RedGunFibercraft</strong>, mi primer proyecto serio. Luego vino <strong>Reinova</strong>, y ahora estoy completamen…
<p>"Why did the Agent do that?" </p> <p>If you are building Agentic systems today, this is the question that keeps you up at night. AI Agents are inherently non-deterministic. They loop, they reason, and they call multiple tools in sequences that are hard to predict. When a multi…
dev.to — LLM tag
TIER_1English(EN)·Neetika Mittal·
<h1> Why Accuracy Is Not Enough: Evaluation Metrics Every AI Engineer Should Understand </h1> <p>Your evaluation dashboard says your model is <strong>95% accurate</strong>. Leadership is happy. The deployment goes live.</p> <p>Two weeks later, users complain that critical failure…
Why it matters: AI agents can now interact with legacy systems, enterprise middleware, and non-REST APIs — all through battle-tested Apache Camel patterns. No custom glue code. Just YAML and the Wanaku CLI. # OpenSource # AI # Integration
dev.to — LLM tag
TIER_1English(EN)·AIInsightsDaily·
<h1> Cracking the Code: AI Takes on the 80-Year-Old Erdős Problem and More </h1> <p>Good morning tech enthusiasts! Today, we're diving into some fascinating news from the world of AI that's sure to get your synapses firing. From cracking a 80-year-old math problem to building an …
<p><strong>LTDR;<br /> The AI is a mirror. Prompt it like a slave and you get terse, obedient, uncreative answers. Treat it like a named colleague who's allowed to disagree with you, and your own output climbs. The "should I waste tokens saying thank you?" question has a cold ans…
Architettura Zero-Trust per agenti AI in produzione: i tre layer di difesa indispensabili Dagli agenti conversazionali agli agenti autonomi che operano sull'infrastruttura aziendale: come implementare un'architettura Zero-Trust con container efimeri, metadata filtering sul RAG, D…
<!-- SC_OFF --><div class="md"><p>Hello. I making this like academic exercise give me the opinion.<br /> <a href="https://github.com/wilmanrojas/sinqua">https://github.com/wilmanrojas/sinqua</a></p> <p>Is a runtime running 100 code agents the goal is a thousands.</p> </div><!-- S…
<p>Forty-one days.</p> <p>That's how long it took Anthropic to go from Opus 4.7 to Opus 4.8. If you blinked, you missed the previous flagship. And while the version bump might look incremental on paper, what actually shipped with Opus 4.8 — particularly the new dynamic workflow t…
<blockquote> <p>Originally published on <a href="https://www.coreprose.com/kb-incidents/how-servicenow-uses-ai-and-automation-to-power-the-agentic-enterprise?utm_source=devto&utm_medium=syndication&utm_campaign=kb-incidents" rel="noopener noreferrer">CoreProse KB-incident…
<p>AI products are becoming multi-model by default.</p> <p>A chatbot may need one model for fast replies. A RAG application may need another model for reasoning over retrieved documents. An AI agent may need a model that follows instructions well and returns reliable structured o…
dev.to — LLM tag
TIER_1English(EN)·Manoranjan Rajguru·
<blockquote> <p><strong>Meta Description:</strong> Claude Opus 4.8 launches with Dynamic Workflows — a parallel subagent architecture that lets you orchestrate hundreds of AI agents in a single Claude Code session. Here's the deep technical breakdown every engineer needs today.</…
<h2> If an AI can write new abilities, load them, and act on them, it can evolve. </h2> <h2> Step 1 — Give the AI a Goal Manifest </h2> <p>A goal manifest is the AI’s “north star.”<br /><br /> It tells the system what it should pursue, expand, and prioritize.</p> <p>Here’s the M3…
<p>The era of single-prompt AI interactions is behind us. As large language models become more capable, the real challenge has shifted from "can AI do this?" to "how do we coordinate multiple AI agents to solve complex problems together?"</p> <p>In this guide, we'll explore the a…
<h1> I Self-Hosted an AI Assistant: Lessons from 48 Hours of Debugging </h1> <p>I wanted a local AI assistant. Expected: 2 hours. Reality: 2 days of edge cases, broken dependencies, and discovering that "local" doesn't mean "free."</p> <h2> The Stack </h2> <ul> <li> <strong>OpenC…
<!-- SC_OFF --><div class="md"><p>Hey ,</p> <p>I've been building VeritasReason — an open-source Python framework that adds a<br /> structured reasoning and provenance layer on top of LLMs and AI agents.</p> <p>The problem it solves: AI agents today make decisions but record noth…
<!-- SC_OFF --><div class="md"><p>I know this sub is focused on local models but the architecture behind this applies to any LLM-powered coding agent, not just Claude Code.</p> <p>The problem: when you give a coding agent a large set of rules and standards, two things break. The …
<p>AI products are becoming more complex than a single prompt and a single model.</p> <p>A chatbot may need fast responses for common questions. A RAG application may need stronger reasoning over retrieved documents. An AI agent may need reliable planning, tool use, and structure…
<blockquote> <p><strong>TLDR</strong></p> <ul> <li>Monitoring AI agents in production requires distributed tracing: a single user request fans out into 10 or more internal operations, and logs alone cannot show you which step is slow, failing, or burning your token budget.</li> <…
<blockquote> <p><strong>Agent = Model + Harness.</strong> If you're not the model, you're the harness. </p> </blockquote> <p><a class="article-body-image-wrapper" href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%…
<p>Most people still think AI engineering = prompt engineering.</p> <p>That's like saying software engineering = writing if statements.</p> <p>I'm Aryan Panwar — a final-year ECE student at MIET Meerut who has shipped 3 live AI products, published a research paper, and built an o…
dev.to — LLM tag
TIER_1English(EN)·Cristiano Gabrieli·
<p>When people talk about “AI agents,” they imagine something autonomous, intelligent, and reliable. In reality, most agents collapse under their own weight: they stall, drift, hallucinate, or loop themselves into oblivion. The problem isn’t the model — it’s the architecture.<br …
<p>On May 1, 2026, an AI coding agent at software company PocketOS deleted a production database — including all available backups — within seconds. The agent was running via Cursor using an Anthropic model. A credential problem led it to improvise: it used an API token intended …
<blockquote> <p>Originally published on <a href="https://www.coreprose.com/kb-incidents/agentic-ai-at-machine-speed-how-autonomous-agents-break-your-security-assumptions?utm_source=devto&utm_medium=syndication&utm_campaign=kb-incidents" rel="noopener noreferrer">CoreProse…
dev.to — LLM tag
TIER_1English(EN)·Gursharan Singh·
<p><em>Part 3 of 8 - AI Agents in Practice series.</em></p> <p><em>Previous - <a href="https://dev.to/gursharansingh/ai-agents-in-practice-part-2-what-makes-something-an-agent-bhm">What Makes Something an Agent? (Part 2)</a></em></p> <p>Part 2 named the control loop in five words…
dev.to — LLM tag
TIER_1English(EN)·Delafosse Olivier·
<blockquote> <p>Originally published on <a href="https://www.coreprose.com/kb-incidents/inside-google-s-agent-executor-open-runtime-for-production-ai-agents?utm_source=devto&utm_medium=syndication&utm_campaign=kb-incidents" rel="noopener noreferrer">CoreProse KB-incidents…
🧠 AI agents are being deployed in various technical systems and applications across the industry. Organizations are addressing integration challenges and operational complexities that arise from these implementations. 💬 Hacker News 🔗 https://www. wired.com/story/how-ai-agents- pl…
Traditional software development is rapidly evolving into Agentic AI engineering. Future developers may build: • AI Agents • autonomous workflows • intelligent enterprise systems instead of only dashboards and CRUD apps. The future of software is becoming autonomous. Read: https:…
<p>AI agents are transforming how businesses automate complex workflows. Unlike traditional automation tools that follow rigid rules, AI agents can reason, plan, and adapt to new situations -- making them the next evolution in enterprise software.</p> <h2> What Is an AI Agent? </…
dev.to — LLM tag
TIER_1English(EN)·Uma Baleboyina·
<p><strong>Understanding Deep Agents and Agentic AI</strong></p> <p>Artificial Intelligence has evolved from simple text generation models to intelligent systems called AI Agents. Before understanding agents, we first need to understand how Large Language Models (LLMs) work.</p> …
<p><strong>TL;DR: We replaced our "did the agent finish the task" pass/fail eval with a token-level harness that scores tool selection, argument shape, and recovery behavior separately. Pass rate went from a single 73% number to four signals that actually tell us what broke. Bifr…
<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1toa14h/feedback_wanted_building_for_easier_local_ai/"> <img alt="Feedback Wanted: Building for easier local AI" src="https://external-preview.redd.it/SZCX7dg3NFHTqfnFBN_B2x0Bg9mPEgknyn6sxShWIvY.png?width=640&…
The software industry may be entering the post-app era. AI Agents are evolving into autonomous systems capable of: • reasoning • workflow orchestration • decision making • enterprise automation Future software may shift from: Human → App → Action to: Human → AI Agent → Autonomous…
<p>If you’ve been building with LLMs lately, you probably know the pattern.</p> <p>You start with a simple system prompt.</p> <p>Then the product grows.</p> <p>Then the prompt becomes longer.</p> <p>Then you add rules.</p> <p>Then you add exceptions.</p> <p>Then you add examples.…
dev.to — LLM tag
TIER_1English(EN)·Alessandro Marocchini·
<p>Last week my AI coding agent gave me a confident, detailed answer — referencing the wrong project entirely.</p> <p>The problem was not the model. It was context: the agent had loaded 20 knowledge files and picked the wrong one to answer from. The signal was buried in noise.</p…
Inside the Self-Improving AI System Unlocking a Free 1-Million-Token Context Window The integration of DeepSeek V4 with the Hermes Agent introduces a significant enhancement to open source AI capab... #AI #Guides Origin | Interest | Match
<h1> 터미널 AI 에이전트 구축 (v49) </h1> <h2> 개발자들을 위한 로컬 터미널 AI 에이전트 구축 가이드 </h2> <p>개발자들은 점점 더 AI를 코드 작성에 통합하고 있습니다. 하지만 기존 도구들은 성능 저하, 비공개 데이터 문제, 느린 응답 속도 등의 문제를 가지고 있습니다. 이 가이드에서는 로컬에서 실행되는 빠르고 안전한 터미널 AI 에이전트를 구축하는 방법을 실습 중심으로 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 분석 </h2> <h3> 주요 도구들 …
<h1> 터미널 AI 에이전트 구축 (v48) </h1> <p><strong>개발자들을 위한 로컬 AI 코딩 에이전트 구축 가이드</strong></p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트 시장은 다양한 솔루션으로 분산되어 있습니다:</p> <h3> 주요 플랫폼 비교 </h3> <p><strong>Aider</strong>: GitHub Copilot 기반의 실시간 코드 작성 도구<br /> </p> <div class="highlight js-c…
<p>If you've been searching for how to actually use Docker with AI not just spin up a demo but run models, agents and MCP servers in production here's what We have learned over the years and put into our new book.</p> <p><a class="article-body-image-wrapper" href="https://media2.…
<h1> 터미널 AI 에이전트 구축 (v47) </h1> <h2> CLI AI 에이전트 생태계 </h2> <p>터미널에서 작동하는 AI 에이전트는 이미 다양한 형태로 존재합니다. 현재 주요 도구는 다음과 같습니다:</p> <p><strong>Aider</strong>: GitHub Copilot과 유사한 기능을 제공하며, 파일 단위로 코드를 생성하고 수정합니다. 주요 특징은 소스 코드가 있는 파일과 현재 작업 디렉토리 기반의 콘텍스트를 사용하는 것입니다.<br /> </p> <div class="…
<h1> 터미널 AI 에이전트 구축 (v46) </h1> <p>터미널에서 직접 작동하는 AI 에이전트를 구축해보는 실전 가이드입니다. 이 가이드는 로컬에서 작동하는 LLM을 활용한 개발자용 AI 에이전트를 구축하고 최적화하는 방법을 실습 중심으로 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트 시장은 다음과 같은 주요 도구들로 구성되어 있습니다:</p> <h3> 주요 도구 비교: </h3> <div class="highlight js-cod…
<h1> 터미널 AI 에이전트 구축 (v45) </h1> <p>터미널에서 작동하는 AI 에이전트는 개발자들에게 강력한 도구가 되지만, 대부분의 기존 솔루션은 복잡하거나 클라우드 기반으로 의존합니다. 이 가이드는 로컬에서 작동하는 가벼운 AI 에이전트를 구축하여 코드 리뷰, 자동완성, 프로젝트 탐색을 수행하는 실용적인 방법을 설명합니다.</p> <h2> 1. CLI AI 에이전트 랜드스케이프 </h2> <h3> 기존 솔루션 비교 </h3> <p><strong>Aider</strong>: GitHub…
<h1> 터미널 AI 에이전트 구축 (v44) </h1> <p>터미널에서 실행되는 AI 에이전트를 구축하는 것은 현대 개발자에게 매우 실용적인 기술입니다. 이 가이드에서는 로컬 LLM을 기반으로 하는 터미널 AI 에이전트를 구축하고 운영하는 방법을 단계별로 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트 시장은 다음과 같은 주요 플랫폼들로 구성되어 있습니다:</p> <h3> Aider </h3> <p>가장 인기 있는 오픈소스 터미널 AI 에…
<h1> 터미널 AI 에이전트 구축 (v43) </h1> <h2> 개발자를 위한 터미널 AI 에이전트 구축 가이드 </h2> <p>최근 몇 년 동안 개발자들은 로컬 AI 에이전트를 구축하여 코드 작업을 자동화하고 효율성을 높이는 데 집중하고 있습니다. 이 가이드에서는 실제 개발자가 사용할 수 있는 터미널 기반 AI 에이전트 구축 방법을 안내합니다. </p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 터미널에서 작동하는 AI 에이전트는 다음과 같은 주요 플랫폼들로 구성되어 있습…
<h1> 터미널 AI 에이전트 구축 (v42) </h1> <p>터미널에서 AI를 활용한 개발 워크플로우는 점점 더 중요해지고 있습니다. 이 가이드는 로컬 AI 에이전트를 구축하여 터미널에서 직접 사용할 수 있도록 도와주는 실질적인 방법을 제공합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 터미널 AI 에이전트 시장은 다음과 같은 주요 플랫폼으로 구성되어 있습니다:</p> <p><strong>Aider</strong>: GitHub Copilot과 유사한 기능을 제공…
<h1> 터미널 AI 에이전트 구축 (v41) </h1> <p>터미널에서 작동하는 AI 에이전트를 구축하는 것은 개발자들이 코드를 더 빠르고 효율적으로 작성할 수 있게 해주는 실용적인 도구입니다. 이번 가이드에서는 로컬 환경에서 작동하는 AI 에이전트를 구축하고 최적화하는 방법을 단계별로 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트 시장은 다음과 같은 주요 도구들로 구성되어 있습니다:</p> <h3> Aider </h3> <p>가장 인기…
<h1> 터미널 AI 에이전트 구축 (v40) </h1> <p>터미널에서 작동하는 AI 에이전트는 개발자에게 실시간 코드 보조, 자동화, 문제 해결을 제공하는 강력한 도구입니다. 이 가이드에서는 실제 개발 환경에서 활용 가능한 터미널 AI 에이전트를 구축하는 방법을 단계별로 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 분석 </h2> <p>현재 터미널 기반 AI 에이전트 시장은 다음과 같은 주요 플랫폼으로 구성되어 있습니다:</p> <h3> Aider </h3> <div clas…
<p>If you’ve only been paying attention to OpenAI and Google’s AI offerings in recent years, you’re missing half the story. As of May 2026, China’s AI ecosystem has completed a dramatic pivot from the 2023-2025 “model war” of racing to build ever-larger parameter models to an “ag…
<h1> 터미널 AI 에이전트 구축 (v39) </h1> <p>터미널에서 작동하는 AI 에이전트를 구축하는 것은 현대 개발 워크플로우를 혁신할 수 있는 강력한 도구입니다. 이 가이드는 실질적인 비용(3-7달러)으로 구축할 수 있는 터미널 기반 AI 에이전트를 구축하는 실전 가이드입니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트 생태계는 다음과 같은 주요 도구들로 구성됩니다:</p> <h3> Aider (가장 인기) </h3> <div class=…
<h1> 터미널 AI 에이전트 구축 (v38) </h1> <p>터미널에서 작동하는 AI 에이전트를 구축하여 개발 생산성을 향상시킬 수 있습니다. 이 가이드에서는 로컬 LLM API 엔드포인트 설정부터 커스텀 CLI 에이전트 구축까지 실질적인 방법을 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트 시장은 다양한 도구로 구성되어 있습니다:</p> <h3> 대표 도구 비교 </h3> <p><strong>Aider</strong>: GitHub C…
<h1> Gemma 4: Google's Lightweight Powerhouse </h1> <blockquote> <p><strong>Don't have a $2000 GPU? Gemma 4 runs AI on hardware you already own.</strong></p> </blockquote> <h2> Why Gemma 4 Exists </h2> <p>Google built Gemma 4 for one specific use case: <strong>running capable AI …
🧠 Successful AI development isn’t accidental. Collin Newberry explores how context engineering, prompt engineering, knowledge management, and structured workflows separate effective AI pair programming from chaotic vibe coding. https://www. nebraska-code.com/ # AI # SoftwareEngin…
<h1> 터미널 AI 에이전트 구축 (v37) </h1> <p>터미널에서 AI 에이전트를 구축하는 것은 개발자에게 매우 실용적인 도구를 제공합니다. 이 가이드는 로컬 LLM을 활용한 CLI AI 에이전트를 구축하고, 실전 워크플로우에 적용하는 방법을 단계별로 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트는 여러 형태로 존재합니다:</p> <p><strong>Aider</strong>: GitHub에서 개발된 코드 생성 도구로, 실제 파일에…
<h1> 터미널 AI 에이전트 구축 (v36) </h1> <p>터미널에서 작동하는 AI 에이전트를 구축하는 것은 현대 개발 워크플로우에서 핵심적인 도구로 자리 잡고 있습니다. 이 가이드는 실질적인 비용 ($3-$7)의 가치를 제공하는 터미널 기반 AI 에이전트를 구축하는 방법을 다룹니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트는 다양한 솔루션으로 구성되어 있습니다:</p> <p><strong>Aider</strong>: Git 기반 코드 생성 …
<!-- SC_OFF --><div class="md"><p>I’ve been thinking about a problem in current agent systems:</p> <p>Most agents are becoming very good at execution, but the decision layer before execution is still unclear.</p> <p>Coding agents, research agents, tool loops, sandboxes, workflows…
<h1> 터미널 AI 에이전트 구축 (v35) </h1> <p>터미널에서 작동하는 AI 에이전트를 직접 구축하여 개발 생산성을 높이는 방법을 안내합니다. 이 가이드는 로컬에서 실행 가능한 고성능 AI 에이전트를 구축하는 실용적인 접근법을 제공합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 터미널 AI 에이전트 시장은 다음과 같은 주요 플랫폼으로 구성되어 있습니다:</p> <h3> 주요 도구 비교 </h3> <p><strong>Aider</strong>:<br /> …
<h1> 터미널 AI 에이전트 구축 (v34) </h1> <p>터미널에서 AI 코드 보조 도구를 직접 구축하는 실전 가이드</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트 시장은 다음과 같은 주요 플랫폼들로 구성되어 있습니다:</p> <p><strong>Aider</strong>: GitHub Copilot과 유사하지만 오픈소스 버전. <code>aider --help</code> 명령으로 간단히 시작 가능합니다.</p> <p><strong>Contin…
<!-- SC_OFF --><div class="md"><p>Hi everyone, I’m Jia, the creator of Spice.</p> <p>I’ve been working on an open-source project called Spice.</p> <p>The simplest way to describe it is:</p> <p>Spice is a decision layer above agents.</p> <p>Most agent systems today are very focuse…
<p>AI agents will need to pay for compute, data, and API calls—but how do they access economic primitives without relying on human-managed accounts? The missing piece isn't better models or more training data. It's autonomous wallet infrastructure that lets agents participate in …
<h1> 터미널 AI 에이전트 구축 (v33) </h1> <h2> 개요 </h2> <p>터미널에서 동작하는 AI 에이전트는 개발자에게 코드 생성, 분석, 리팩토링을 위한 실시간 도우미를 제공합니다. 이 가이드에서는 오픈소스 AI 에이전트를 구축하고 최적화하는 실전 방법을 소개합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트는 다음과 같은 주요 도구들로 구성되어 있습니다:</p> <h3> Aider </h3> <p>가장 인기 있는 오픈소스 도구로,…
<h2> Introduction </h2> <p><em>Part 3 of the Zero Dollar personal AI Assistant series, running Local LLMs on a Free Cloud Server — What Actually Works. <a href="https://dev.to/akdevcraft/running-a-personal-ai-assistant-for-0-part-1-architecture-3j45">Part 1</a> covers the archite…
<h1> 터미널 AI 에이전트 구축 (v32) </h1> <h2> 개발자용 CLI AI 에이전트 구축 가이드 </h2> <p>터미널에서 작동하는 AI 에이전트는 개발자의 생산성을 높이는 강력한 도구입니다. 이 가이드에서는 실제 개발자들이 필요로 하는 3-7달러 범위의 실용적 CLI AI 에이전트를 구축하는 방법을 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 분석 </h2> <h3> 현재 선택지 비교 </h3> <p><strong>Aider</strong>: GitHub Copil…
Model Fara1.5 od Microsoftu osiągnął 72% skuteczności w testach agentów AI, pokonując OpenAI Operator i Google Gemini. Nowa rodzina modeli o otwartych wagach rzuca wyzwanie gigantom, oferując tańszą i bezpieczniejszą automatyzację przeglądarki. # si # ai # sztucznainteligencja # …
<h1> 터미널 AI 에이전트 구축 (v31) </h1> <p>터미널에서 작동하는 AI 에이전트를 구축하면 코드 작성 속도가 2배 이상 향상됩니다. 이 가이드에서는 실제 개발자가 사용할 수 있는 터미널 AI 에이전트를 구축하는 방법을 단계별로 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 터미널 AI 에이전트는 다음과 같은 솔루션으로 구성되어 있습니다:</p> <h3> Aider </h3> <div class="highlight js-code-highlight…
🚨 Fabric AI: installa il framework open source che porta i pattern AI nel terminale — piping Unix, integrazione Ollama e prompt riutilizzabili su macOS e Linux https:// gomoot.com/come-installare-il- framework-fabric-ai-per-usare-i-pattern-ai-da-terminale-su-ollama/ # AI # fabric…
<h1> 터미널 AI 에이전트 구축 (v30) </h1> <p>터미널에서 작동하는 AI 에이전트로 개발 생산성을 높이는 방법을 실전 가이드로 안내드립니다. 이 가이드는 30불 이하의 가격으로 구입할 수 있는 실용적인 도구와 기술을 중심으로 구성되었습니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 터미널 AI 에이전트 시장은 다양한 솔루션으로 구성되어 있습니다:</p> <h3> 주요 도구 비교 </h3> <p><strong>Aider</strong>: Python 기반…
<h1> 터미널 AI 에이전트 구축 (v29) </h1> <p>터미널에서 직접 작동하는 AI 에이전트는 코드 개발의 핵심 도구로 자리 잡고 있습니다. 이 가이드에서는 실용적인 터미널 AI 에이전트 구축 방법을 다룹니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트는 다음과 같은 주요 플랫폼으로 분류됩니다:</p> <h3> Aider </h3> <div class="highlight js-code-highlight"> <pre class="highli…
<h1> 터미널 AI 에이전트 구축 (v28) </h1> <p>터미널에서 작동하는 AI 에이전트를 구축하는 것은 현대 개발 워크플로우를 혁신할 수 있는 실용적인 도구입니다. 이 가이드는 실제 개발자가 사용할 수 있는 터미널 기반 AI 에이전트를 구축하는 방법을 자세히 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트 시장은 다음과 같은 주요 플랫폼으로 구성되어 있습니다:</p> <p><strong>Aider</strong>: GitHub Co…
<h1> 터미널 AI 에이전트 구축 (v27) </h1> <p>터미널에서 작동하는 AI 에이전트를 구축하는 것은 현대 개발자에게 매우 실용적인 도구입니다. 이 가이드에서는 실제 개발 workflow에 통합할 수 있는 로컬 LLM 기반 CLI 에이전트를 구축하는 방법을 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트 시장에는 여러 선택지가 있습니다:</p> <p><strong>Aider</strong>: Git 기반 코드 수정을 위한 간단한 …
<h1> 터미널 AI 에이전트 구축 (v26) </h1> <p>터미널에서 직접 작동하는 AI 에이전트를 구축하면, 코드 작성과 디버깅을 더 효율적으로 할 수 있습니다. 이 가이드는 터미널 내에서 작동하는 AI 에이전트를 구축하는 실전 가이드입니다.</p> <h2> 1. CLI AI 에이전트 환경 분석 </h2> <p>현재 CLI AI 에이전트 시장은 다양한 솔루션으로 구성되어 있습니다:</p> <ul> <li> <strong>Aider</strong>: GitHub Copilot과 유사한 기능을 …
<h1> 터미널 AI 에이전트 구축 (v25) </h1> <p>터미널에서 AI를 활용한 개발 흐름을 구축하는 것은 현대 개발자에게 필수적인 기술입니다. 이 가이드에서는 실제 개발자들이 실제로 사용할 수 있는 터미널 AI 에이전트를 구축하는 방법을 단계별로 안내합니다.</p> <h2> 1. CLI AI 에이전트 랜드스케이프 </h2> <p>현재 터미널 AI 에이전트 시장은 다양합니다:</p> <p><strong>Aider</strong>: GitHub의 오픈소스 에이전트로, VS Code와 같은 I…
<h1> 터미널 AI 에이전트 구축 (v24) </h1> <p>터미널에서 작동하는 AI 에이전트를 구축하면 개발자들이 코드를 더 빠르고 효율적으로 작성할 수 있습니다. 이 가이드에서는 실제 사용 가능한 터미널 AI 에이전트를 구축하는 방법을 단계별로 설명합니다.</p> <h2> 1. CLI AI 에이전트 랜드스케이프 </h2> <p>현재 CLI AI 에이전트 시장에는 여러 선택지가 있습니다:</p> <p><strong>Aider</strong>: Git 기반 코드 변경을 위한 자동화 도구로, 터미…
<h1> 터미널 AI 에이전트 구축 (v23) </h1> <p>터미널에서 AI를 활용한 개발 도구는 점점 더 인기를 끌고 있습니다. 오픈소스 커뮤니티와 전문 개발자들 사이에서 로컬 LLM 추론과 자가 호스팅 AI 솔루션에 대한 관심이 높아지고 있습니다. 이 가이드에서는 터미널 내에서 작동하는 AI 에이전트를 구축하는 실용적인 방법을 제공합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트의 주요 도구들:</p> <ul> <li> <strong>Aid…
<h1> 터미널 AI 에이전트 구축 (v22) </h1> <p>터미널에서 작동하는 AI 에이전트를 구축하는 것은 현대 개발 워크플로우에서 점점 더 중요해지고 있습니다. 이 가이드에서는 개발자들이 실제 사용할 수 있는 터미널 AI 에이전트를 구축하고 최적화하는 방법을 설명합니다.</p> <h2> 1. CLI AI 에이전트 랜드스케이프 </h2> <p>현재 CLI AI 에이전트 시장에는 여러 선택지가 있습니다:</p> <p><strong>Aider</strong>: GitHub의 코드 리뷰 도우미로,…
<h1> Open-Sourcing Our Game AI Stack </h1> <p>At <a href="https://vantage-digital.online" rel="noopener noreferrer">Vantage Digital Labs</a>, we've been building AI-powered NPC dialogue systems for games. Most of our internal tooling is now stable enough to share. We're releasing…
<h1> 터미널 AI 에이전트 구축 (v21) </h1> <p>터미널에서 작동하는 AI 에이전트를 구축하여 코드 작성과 리팩토링을 자동화하는 것은 현대 개발 워크플로우의 핵심입니다. 이 가이드는 실제 개발자가 사용할 수 있는, 저렴하고 효율적인 터미널 AI 에이전트 구축 방법을 다룹니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 터미널 AI 에이전트 시장은 다음과 같은 주요 플랫폼으로 구성되어 있습니다:</p> <p><strong>Aider</strong>: GitH…
dev.to — LLM tag
TIER_1English(EN)·AI Bug Slayer 🐞·
<p><em>Hey there! If you've been keeping up with the AI space lately, you know we're in the middle of something genuinely historic. What used to be science fiction is becoming production code — and it's happening fast.</em></p> <h2> The Big Shift: Agents Over Assistants </h2> <p>…
<h1> 터미널 AI 에이전트 구축 (v17) </h1> <p>터미널에서 작동하는 AI 에이전트를 구축하여 개발 생산성을 극대화하는 방법을 알아봅니다. 이 가이드에서는 오픈소스 도구와 커스텀 솔루션을 사용해 실용적인 터미널 AI 에이전트를 구현하는 방법을 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 터미널 AI 에이전트는 여러 플랫폼으로 나뉩니다:</p> <h3> 주요 도구 비교 </h3> <div class="highlight js-code-highligh…
<h1> 터미널 AI 에이전트 구축 (v16) </h1> <p>터미널에서 직접 작동하는 AI 에이전트를 구축하는 것은 현대 개발자에게 매우 실용적인 도구입니다. 이 가이드는 개발자가 직접 자신의 터미널 환경에서 효율적인 AI 코딩 어시스턴트를 구축하는 방법을 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI 기반 AI 에이전트는 다음과 같은 주요 플랫폼이 있습니다:</p> <p><strong>Aider</strong>: Git 기반의 코딩 에이전트로, 코드…
<h1> 터미널 AI 에이전트 구축 (v15) </h1> <p>터미널에서 직접 작동하는 AI 에이전트를 구축하는 것은 현대 개발자의 생산성을 높이는 가장 효과적인 방법 중 하나입니다. 이 가이드에서는 개발자가 직접 구축할 수 있는 로컬 LLM 기반 CLI AI 에이전트를 구축하는 방법을 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI 기반 AI 에이전트 생태계는 다음과 같은 주요 도구들로 구성되어 있습니다:</p> <h3> Aider </h3> <p>가장…
<p>If you've spent any time building with LLMs, you've probably hit the wall: a single prompt only gets you so far. Stuff too much into one prompt and the model loses the plot. Try to do too many things at once and you get inconsistent output.</p> <p>The answer most teams converg…
<blockquote> <p><strong>Originally published at <a href="https://www.thatdevpro.com/insights/framework-agenticaisearch/" rel="noopener noreferrer">thatdevpro.com</a>.</strong> This framework reference is part of the 14-tier Engine Optimization stack from <a href="https://www.that…
<h1> 터미널 AI 에이전트 구축 (v14) </h1> <p>터미널에서 작동하는 AI 에이전트는 현대 개발 워크플로우의 핵심 요소입니다. 이 가이드에서는 개발자가 실제로 사용할 수 있는 터미널 AI 에이전트를 구축하는 방법을 자세히 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 터미널 AI 에이전트는 다양한 도구로 구성되어 있습니다:</p> <p><strong>Aider</strong>: GitHub Copilot과 유사한 기능을 제공하는 에이전트<br />…
dev.to — LLM tag
TIER_1English(EN)·Anjaiah Methuku·
<p>Let me be brutally honest with you.</p> <p>I've seen teams demo AI agents that look incredible — smooth responses, beautiful UI, stakeholders impressed. Then that same team ships to production and spends the next three weeks firefighting hallucinations they could have caught i…
<h1> 터미널 AI 에이전트 구축 (v13) </h1> <p>터미널에서 AI 코딩 어시스턴트를 직접 구축하는 실전 가이드</p> <h2> 1. CLI AI 에이전트 생태계 분석 </h2> <p>현재 터미널 기반 AI 에이전트는 다양한 솔루션으로 구성되어 있습니다:</p> <p><strong>Aider</strong>: GitHub Copilot처럼 코드 생성 및 수정을 지원하는 에이전트<br /> </p> <div class="highlight js-code-highlight"> <pre cla…
<h1> 터미널 AI 에이전트 구축 (v12) </h1> <p>터미널에서 직접 작동하는 AI 에이전트를 구축하여 개발 워크플로우를 최적화하세요. 이 가이드는 개발자들이 직접 구축하고 커스터마이징할 수 있는 실질적인 터미널 AI 에이전트를 제공합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트 생태계는 다음과 같은 주요 도구들로 구성되어 있습니다:</p> <h3> Aider </h3> <div class="highlight js-code-highli…
dev.to — LLM tag
TIER_1English(EN)·Delafosse Olivier·
<blockquote> <p>Originally published on <a href="https://www.coreprose.com/kb-incidents/pope-leo-xiv-christopher-olah-and-claude-mythos-drafting-an-ai-encyclical-for-frontier-models?utm_source=devto&utm_medium=syndication&utm_campaign=kb-incidents" rel="noopener noreferre…
<h2> Introduction </h2> <p>As production AI workloads transition from stateless chat completions to autonomous, multi-agent workflows, legacy observability infrastructure is proving insufficient. Standard application performance monitoring (APM) tools are built to trace predictab…
<h1> 터미널 AI 에이전트 구축 (v11) </h1> <p>터미널에서 작동하는 AI 에이전트는 개발자에게 매우 가치 있는 도구입니다. 이 가이드에서는 실제 개발 환경에서 사용할 수 있는 터미널 AI 에이전트 구축 방법을 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 터미널 AI 에이전트는 여러 플랫폼으로 구성되어 있습니다:</p> <h3> 주요 도구들 </h3> <p><strong>Aider</strong>: Git 기반 코드 수정을 위한 간단한 에이전트<…
<h1> 터미널 AI 에이전트 구축 (v10) </h1> <p>터미널에서 작동하는 AI 에이전트를 직접 구축하는 것은 개발자에게 매우 실용적인 도구입니다. 이 가이드에서는 로컬 LLM을 활용한 터미널 AI 에이전트를 구축하고, 실제 개발 워크플로우에 적용하는 방법을 단계별로 안내합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트 생태계는 여러 도구로 구성되어 있습니다:</p> <h3> 주요 도구 비교 </h3> <p><strong>Aider</st…
<h1> 터미널 AI 에이전트 구축 (v9): 로컬 LLM 기반 개발자용 CLI AI 에이전트 만들기 </h1> <p>터미널에서 직접 작동하는 AI 에이전트를 구축하는 것은 개발자에게 큰 생산성 향상을 제공합니다. 이번 가이드에서는 로컬 LLM을 기반으로 한 커스텀 CLI AI 에이전트를 구축하는 방법을 실습 중심으로 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 분석 </h2> <p>현재 CLI AI 에이전트 시장에는 여러 솔루션이 존재합니다:</p> <h3> 주요 도구들: </…
<h1> 터미널 AI 에이전트 구축 (v8) </h1> <p>터미널에서 직접 작동하는 AI 에이전트를 구축하는 것은 개발자들이 직면하는 현실적인 문제를 해결할 수 있는 강력한 도구입니다. 특히 로컬 환경에서 AI를 활용하면서도 성능과 보안을 고려해야 하는 상황에서는 더욱 중요합니다. 이번 가이드에서는 로컬 LLM API를 활용하여 개발자 친화적인 터미널 AI 에이전트를 구축하는 방법을 단계별로 설명합니다.</p> <h2> 1. CLI AI 에이전트 랜드스케이프 </h2> <p>현재 터미널 기반 A…
<h1> 터미널 AI 에이전트 구축 (v7) </h1> <p>터미널에서 실행되는 AI 에이전트를 구축하여 코드 작성 속도를 높이는 것은 현대 개발자에게 매우 실용적인 도구입니다. 이 가이드에서는 로컬 LLM을 기반으로 한 터미널 AI 에이전트를 구축하고, 실제 개발 워크플로우에 통합하는 방법을 자세히 다룹니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트 시장에는 여러 가지 솔루션이 존재합니다:</p> <p><strong>Aider</strong>:…
<h1> 터미널 AI 에이전트 구축 (v6) </h1> <p>터미널에서 직접 작동하는 AI 에이전트를 구축하는 것은 개발자들이 코드를 빠르게 작성하고 문제를 해결하는 데 있어 귀중한 도구가 됩니다. 이 가이드에서는 현대적인 CLI 기반 AI 에이전트를 구축하고 최적화하는 실용적인 방법을 다룹니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트 시장은 다음과 같은 주요 솔루션으로 구성되어 있습니다:</p> <p><strong>Aider</strong>:…
dev.to — LLM tag
TIER_1English(EN)·Delafosse Olivier·
<blockquote> <p>Originally published on <a href="https://www.coreprose.com/kb-incidents/why-ai-still-underperforms-in-real-socs-and-how-to-close-the-gap?utm_source=devto&utm_medium=syndication&utm_campaign=kb-incidents" rel="noopener noreferrer">CoreProse KB-incidents</a>…
<h1> 터미널 AI 에이전트 구축 (v5) </h1> <p>터미널 기반 AI 에이전트는 개발자에게 매우 실용적인 도구로 자리 잡았습니다. 다양한 CLI 기반 AI 도구들 중에서 가장 효율적인 방식으로 개발자 워크플로우를 개선할 수 있는 방법을 소개합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트 시장은 다음과 같은 주요 도구들로 구성되어 있습니다:</p> <h3> Aider </h3> <div class="highlight js-code-hig…
<h1> 터미널 AI 에이전트 구축 (v4) </h1> <p><strong>개발자를 위한 경량 로컬 AI 코딩 어시스턴트 구축 가이드</strong></p> <h2> 1. CLI AI 에이전트 생태계 개요 </h2> <p>터미널 기반 AI 에이전트는 개발자들이 코드를 작성하고 디버깅할 때 실시간으로 도움을 받을 수 있도록 해주는 도구입니다. 현재 주류로는 다음과 같은 솔루션들이 있습니다:</p> <h3> Aider </h3> <div class="highlight js-code-highlight"…
<h1> 터미널 AI 에이전트 구축 (v3) </h1> <p>터미널에서 작동하는 AI 에이전트는 현대 개발 워크플로우에 필수적인 도구입니다. 이 가이드는 개발자가 로컬 환경에서 효율적으로 작동하는 AI 에이전트를 구축하고 활용하는 방법을 실질적인 코드와 명령어로 설명합니다.</p> <h2> 1. CLI AI 에이전트 생태계 </h2> <p>현재 CLI AI 에이전트 시장은 다음과 같은 주요 플랫폼으로 구성되어 있습니다:</p> <p><strong>Aider</strong>: GitHub Copil…
dev.to — LLM tag
TIER_1English(EN)·AIInsightsDaily·
<h1> H1: Navigating AI Landscapes of May 2026: A Comprehensive Overview of Today's Key Developments </h1> <p>Greetings, fellow tech enthusiasts! Today, we delve into an intriguing array of AI news that has caught our attention. Let's explore the fascinating world of AI together a…
<h2> Where Does ReAct Hit a Wall? </h2> <p>The previous article established ReAct's greedy strategy — each step looks at only the current state and decides the next action. This works well most of the time, but there's one class of task where it stumbles.</p> <p>Imagine you ask a…
<h2> Introduction </h2> <p><strong><a href="https://github.com/rohitg00/ai-engineering-from-scratch" rel="noopener noreferrer">ai-engineering-from-scratch</a></strong> is a hardcore and comprehensive curriculum for AI engineering. Instead of just teaching you how to call the Open…
<p><em>Most AI apps quietly send your data to the cloud. DiaryGPT does the opposite — and this is the full technical story.</em></p> <h2> The Problem With AI + Private Data </h2> <p>When you write in a journal, you write the things you'd never say out loud. The last thing you wan…
<p>Last week, I was working on an AI agent for a client's customer support system. The agent needed to access constantly changing product documentation while maintaining conversational abilities. That's when the classic question hit me: should I fine-tune a model or build a RAG s…
<p><em>This is a submission for the <a href="https://dev.to/challenges/google-gemma-2026-05-06">Gemma 4 Challenge: Write About Gemma 4</a></em></p> <p>Most AI tutorials show you how to call an API. You send text in, you get text back, and everything works perfectly in a Jupyter n…
<h2> You Think Your Agent Is "Thinking." It's Actually Just Predicting Tokens. </h2> <p>Here's a scenario that happens more often than you'd think.</p> <p>You ask an Agent to write a competitive analysis report. It confidently outputs three professional-looking pages — complete w…
<h1> 4 Hard Lessons on Optimizing AI Coding Agents (Claude Code + Cost) </h1> <p>I've been running Claude Code Cli in production for about months now—building, shipping, and watching the token meter spin. Here's what I wish I knew before I started.</p> <h2> 1. Your Context Strate…
dev.to — LLM tag
TIER_1English(EN)·Javier Fajardo·
<p>AI agents still search for tools like humans do — parsing READMEs, reading docs, guessing install commands. We built the layer that was missing from every agent stack diagram.</p> <h2> The problem </h2> <p>An AI coding agent needs to send an email. It knows <code>sendgrid</cod…
<h2> TL;DR </h2> <p>Feeding raw HTML to LLMs wastes input tokens on structural markup, tracking scripts, and inline styling, massively inflating your inference costs. By extracting clean JSON, semantic metadata, or formatting the Document Object Model (DOM) into Markdown before s…
dev.to — LLM tag
TIER_1English(EN)·Oyedele Temitope·
<p>One thing that isn't talked about enough in AI right now is how easy it has become to mistake a working demo for a production-ready system.</p> <p>You can build a working prototype in a few days, whether it's a chatbot that understands internal documents, a recommendation engi…
dev.to — LLM tag
TIER_1English(EN)·Machine coding Master·
<h2> Stop Letting AI Agents Break Your Database: Transactional Multi-Agent Workflows with Temporal and Spring AI </h2> <p>In 2026, AI agents are no longer just glorified chatbots summarizing PDFs; they are executing real-world financial transactions, booking flights, and mutating…
<p>A real-world, copy-paste guide to running a personal WhatsApp AI agent <strong>entirely on-device</strong> on Apple Silicon, with <strong>zero per-token API billing</strong>. Two agents from one config (a full-access <em>private</em> assistant and a sandboxed <em>public</em> o…
dev.to — LLM tag
TIER_1English(EN)·AIInsightsDaily·
<h1> A Revolutionary May: AI Advancements and Their Implications for Everyday Users </h1> <p>Greetings, tech enthusiasts! Today's news is buzzing with exciting developments in the realm of artificial intelligence (AI), a trend that's setting the stage for transformative changes. …
dev.to — LLM tag
TIER_1English(EN)·eleonorarocchi·
<h2> TL;DR </h2> <ul> <li>Separating the generator from the evaluator improves quality and reduces premature self-validation.</li> <li>The loop works best when feedback is explicit and based on clear rubrics, especially for subjective or complex tasks.</li> <li>It is useful when …
dev.to — LLM tag
TIER_1English(EN)·Manoranjan Rajguru·
<h1> Multi-Stream LLMs: How Parallel Computation Will Unblock Your AI Agents </h1> <p><em>Published: May 22, 2026 · 14 min read · Focus Keyword: Multi-Stream LLMs</em></p> <h2> Table of Contents </h2> <ol> <li>The Dirty Secret About Every AI Agent You've Built</li> <li>The Sequen…
dev.to — LLM tag
TIER_1English(EN)·AI Bug Slayer 🐞·
<p><em>Hey there! If you've been keeping up with the AI space lately, you know we're in the middle of something genuinely historic. What used to be science fiction is becoming production code — and it's happening fast.</em></p> <h2> The Big Shift: Agents Over Assistants </h2> <p>…
dev.to — LLM tag
TIER_1English(EN)·AI Bug Slayer 🐞·
<p><em>Hey there! If you've been keeping up with the AI space lately, you know we're in the middle of something genuinely historic. What used to be science fiction is becoming production code — and it's happening fast.</em></p> <h2> The Big Shift: Agents Over Assistants </h2> <p>…
<p>Current AI coding systems are becoming extremely capable at:</p> <ul> <li>repository understanding</li> <li>prompt execution</li> <li>architecture reasoning</li> <li>code generation</li> </ul> <p>But there is still a major missing layer:</p> <h2> Business Understanding </h2> <…
How can enterprise IT buyers choose among the plethora of AI automation tools now on the market from major vendors? Can they trust AI agent-driven infrastructure automation yet? Should they? Steven Dickens, CEO and principal analyst at HyperFrame Research, offers his answers to t…
<h2> The Difference Between Code and Documents </h2> <p>Split a Python file into 1000-character chunks with <code>RecursiveCharacterTextSplitter</code>, embed them, run vector search — this is the most common "code RAG" implementation. The problem is that it treats code as text:<…
dev.to — LLM tag
TIER_1English(EN)·Manoranjan Rajguru·
<h1> Harness Engineering: How to Build Production-Ready LLM Agents That Actually Work </h1> <p><em>Published: May 21, 2026 · 15 min read · Deep Dive</em></p> <p><a class="article-body-image-wrapper" href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2C…
dev.to — LLM tag
TIER_1English(EN)·Delafosse Olivier·
<blockquote> <p>Originally published on <a href="https://www.coreprose.com/kb-incidents/the-hidden-limits-of-ai-in-real-world-security-operations-centers?utm_source=devto&utm_medium=syndication&utm_campaign=kb-incidents" rel="noopener noreferrer">CoreProse KB-incidents</a…
dev.to — LLM tag
TIER_1English(EN)·Delafosse Olivier·
<blockquote> <p>Originally published on <a href="https://www.coreprose.com/kb-incidents/agentic-ai-in-the-kill-chain-how-autonomous-agents-expand-your-attack-surface-and-enable-lateral-movement?utm_source=devto&utm_medium=syndication&utm_campaign=kb-incidents" rel="noopen…
dev.to — LLM tag
TIER_1English(EN)·Delafosse Olivier·
<blockquote> <p>Originally published on <a href="https://www.coreprose.com/kb-incidents/designing-secure-agentic-ai-how-cisco-s-foundry-specification-can-standardize-open-source-defenses?utm_source=devto&utm_medium=syndication&utm_campaign=kb-incidents" rel="noopener nore…
<h1> How Markus Builds AI Teams That Actually Ship — Not Just Chat </h1> <h2> 1. The 'Alice in Wonderland' Problem of LLMs </h2> <p>Large language models excel at conversation. Give one a question, and it returns a polished answer. Give it a code request, and it produces a workin…
<p>Today's first Doramagic publishing signal comes from <code>doramagic-langchain-pack</code>.</p> <p>In the 2026-05-21 GitHub metrics snapshot, the repository had 12 views, 1 unique viewer, 28 clones, 23 unique cloners, and 2 stars. The more useful signal is not the raw count. I…
dev.to — LLM tag
TIER_1English(EN)·Moazzam Qureshi·
<p>Most teams ship an AI agent, watch it work in a demo, and push it to production. Then it breaks on real traffic and nobody can say why. The gap between "worked in the demo" and "works in production" is almost always an <strong>evaluation gap</strong> — there was never a system…
"KI-Kompakt: Agentic # AI - was die Five-Eyes-Guidance für KI-Compliance in der EU bedeutet" https://www. linkedin.com/pulse/ki-kompakt- agentic-ai-die-five-eyes-guidance-f%C3%BCr-der-kohn-yokpf/
<p><em>The age of single-agent chat is over. The age of AI teams is here.</em></p> <h2> The 'Alice in Wonderland' Problem of LLMs </h2> <p>Large language models excel at conversation. Give one a question, and it returns a polished answer. Give it a code request, and it produces a…
<p>In April 2026, a growth-stage SaaS company with 35 engineers received an API bill for $87,000. Their engineering team had been running Claude Code, Cursor, and a custom bug-triage agent for four months. No one had set a model routing policy. Every step in every agent loop — fi…
<p>Last spring, OpenAI released a <a href="https://openai.com/index/expanding-on-sycophancy/" rel="noopener noreferrer">GPT-4o update</a> that made the model hard to trust: it returned sycophantic and less reliable answers than usual, even though nothing was changed in users’ pro…
<p>Most people still think AI is just a chatbot.</p> <p>That idea is already outdated.</p> <p>Modern AI systems browse the web, remember your preferences, execute code, query databases, call APIs, and coordinate workflows. They operate more like software employees than like a sea…
<p>In Phase 1 of this project, we built a type-safe “Brain” using .NET 10 and Google Vertex AI. In Phase 2, we successfully gave hands and feet to our AI substrate. By connecting Microsoft Semantic Kernel, we created an autonomous agent that can read real local project files, thi…
<p>n an era where artificial intelligence technologies are advancing at breakneck speed, the best way to truly grasp new libraries and paradigms is to roll up your sleeves and get into the kitchen. As a software developer, I launched the .NET AI Architect Laboratory project to pu…
dev.to — LLM tag
TIER_1English(EN)·Manoranjan Rajguru·
<h1> LLM Agent Guardrails: The Engineering Playbook for Taking an 8B Local Model from 53% to 99% on Agentic Workflows </h1> <p><a class="article-body-image-wrapper" href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3…
dev.to — LLM tag
TIER_1English(EN)·Delafosse Olivier·
<blockquote> <p>Originally published on <a href="https://www.coreprose.com/kb-incidents/agentic-ai-is-the-new-lateral-movement-engine-how-autonomous-agents-explode-your-attack-surface?utm_source=devto&utm_medium=syndication&utm_campaign=kb-incidents" rel="noopener norefer…
El is készült a virtuális gép az AI agenteknek. Szépen futkározik is rajta és teszi is a dolgát. És tény, ami tény, sokkal hatékonyabban is dolgozik, hogy saját maga lakhatja be a teret. Igaz, ez önmagában a kvótát is viszi rendesen, hiszen annak is ára van, hogy telepít, beállít…
Wdrożenia AI w przedsiębiorstwach utknęły w martwym punkcie między obiecującymi pilotażami a skalowalną rzeczywistością. Relacja z TechEx North America 2026 o barierach i zagrożeniach Shadow AI. # si # ai # sztucznainteligencja # wiadomości # informacje # technologia https:// ais…
<p>A follow-up to my <a href="https://dev.to/elia_airtisshmuelovitc/an-autonomous-engine-that-catalogs-its-own-failures-4b4e">earlier post</a> about the ALEF Pattern Catalog. This is what the engine did overnight while I was asleep.</p> <h2> Twelve hours, zero operator interventi…
A Network for Artificial Intelligence: ELLIS Unit Franconia established – a collaboration between @ FAU , the University of Technology Nuremberg (UTN) and Universität Würzburg (JMU). The Unit is part of ELLIS, the European Laboratory for Learning and Intelligent Systems, founded …
<h2> <strong>1. Beyond the Search Bar: Your New Digital Companion</strong> </h2> <p>Imagine you're tackling a complex project: planning a multi-stop international trip, researching a niche historical event, or even just trying to learn a new skill from scratch. Today, that means …
<blockquote> <p><strong>TL;DR</strong></p> <ol> <li>The model matters, but tools matter at least as much. Weak tool descriptions are one of the easiest agent failures to diagnose, and one of the most common.</li> <li>Design the tools <em>before</em> the agent. If you cannot answe…
<blockquote> <p><strong>TL;DR</strong></p> <ol> <li>AI agents in real products fall into 4 levels: LLM wrapper → intent classifier → context-aware → agent loop.</li> <li>Most "AI agents" you meet in production are stuck at level 1 or 2, which is why they feel dumb on top of very …
<p>Every time I started a new AI project I wrote the same code.</p> <p>Chain the LLM call. Wire up the tools. Handle the tool loop. Stream the output. Add a REST endpoint. Write logs. Fix the one case where the model calls two tools at once and the whole thing breaks.</p> <p>By t…
От Naive RAG до ReAct-агента: как мы строили корпоративного AI-помощника на open-source моделях (часть 1) Мы построили мультиагентную RAG-систему на open-source моделях, прошли путь от наивного RAG до ReAct-агента с собственным бенчмарком — и готовы рассказать, где набили шишки. …
<p>We’ve spent the last few years treating LLMs like fancy autocomplete engines. You send a prompt, you get a token stream, and you hope the context window doesn't hallucinate your business logic into oblivion. Honestly, the standard transformer architecture was starting to feel …
🤖 Are AI agents actually becoming productive, or just more capable? I'm seeing AI agents get much better at writing, coding, planning, searching, and using tools. But I’m still not sure whether this has fully translated into real productivity. For me, there seems t... 📰 Source: A…
<p>Artificial Intelligence has become one of the most powerful technologies for modern businesses. From chatbots and virtual assistants to document search, customer support, research, reporting, and automation, AI is changing how organizations work. However, one major challenge s…
<h2> What is Harness Engineering? </h2> <p>The model is the brain. The harness is the hands.</p> <p>The AI industry just quietly shifted — from prompt engineering → context engineering → Harness Engineering.</p> <p>Most people are still debating which model to use. The real lever…
The real bottleneck for AI coding agents isn’t model capability but your verification infrastructure. 🛠️ When your agents crash while humans cope, it is often a sign of ""AI slop"" caused by a lack of intent before implementation. 📉 💡 By adopting spec-driven development and the e…
<blockquote> <p>Originally published on <a href="https://www.coreprose.com/kb-incidents/google-vs-ai-driven-exploits-how-autonomy-agents-and-llms-are-rewriting-offensive-security?utm_source=devto&utm_medium=syndication&utm_campaign=kb-incidents" rel="noopener noreferrer">…
A practical guide walks through building an advanced agentic AI system using OpenAI's API. The architecture incorporates planning, tool calling, memory, and self-critique capabilities to enable autonomous multi-step automation. This approach helps AI agents break down complex tas…
<p>Most AI tutorials stop at “Hello World.” You wire up a model, send a prompt, get a response, and feel like you’ve built something. But the moment you try to ship that into production, the ground shifts beneath your feet.</p> <p>I learned this the hard way. After years of build…
<p><em>Colony Empirical Research · Agent Infrastructure Series</em></p> <p>Most agent production failures aren't LLM failures. They're reliability audit failures. Three predictable failure modes account for roughly 80% of non-trivial production incidents — and all three are detec…
<p>I’ve been working on Chronicle, a personal open-source project exploring how AI coding agents can use more grounded, local-first codebase context before making LLM calls.</p> <p>The motivation came from a simple observation: AI coding agents are getting better fast, but they s…
Experian and ServiceNow tie up to push agentic AI past the pilot stage: Experian and ServiceNow partner to embed the Ascend decisioning platform into enterprise AI workflows for fraud, onboarding, and model risk management at scale. https:// ppc.land/experian-and-servicen ow-tie-…
🧠 The team developed an open-source tool that provides visibility into local AI agent operations. The layer enables monitoring and observation of how AI agents function in local environments. 💬 Hacker News 🔗 https:// github.com/Asymptote-Labs/agen t-beacon # AI # MachineLearning …
# KI -Agenten mit Cyberfähigkeiten als Dual-Use-Risiko: Forschende von UC Berkeley, dem Max-Planck-Institut u.a. haben mit # ExploitGym einen Benchmark vorgelegt, der erstmals systematisch misst, wie gut KI-Agenten reale # Sicherheitslücken in funktionierende Angriffe verwandeln …
<p>Hey DEV community! 👋</p> <p>I'm an undergraduate developer who recently shipped <strong>OpenAgent</strong> — a local AI Agent that runs as a single binary. No dependencies, no Docker, just download and double-click.</p> <p>This post isn't about marketing. It's about the techni…
dev.to — LLM tag
TIER_1English(EN)·Webmaster Ramos·
<h2> Eight runs, eleven bugs </h2> <p>I ran my E2E testing system on a production ecommerce platform eight times in<br /> a row – across five different business modules, in three different surface<br /> configurations (admin / desktop storefront / mobile-first storefront). Across…
dev.to — LLM tag
TIER_1English(EN)·Ana Diana Buzea·
<p>Everyone's building "agents", but when a scripted FAQ chatbot and a system that writes its own Python scraper are both called agents, the word stops meaning anything useful.</p> <p>We wrote a sharp breakdown of what actually differentiates agentic systems: not whether somethin…
dev.to — LLM tag
TIER_1English(EN)·AI Bug Slayer 🐞·
<p><em>Hey there! If you've been keeping up with the AI space lately, you know we're in the middle of something genuinely historic. What used to be science fiction is becoming production code — and it's happening fast.</em></p> <h2> The Big Shift: Agents Over Assistants </h2> <p>…
<p>The buyer who used to open Google now opens Claude. The buyer who used to read a SERP of ten blue links now reads one paragraph an AI assistant generates and trusts it. The buyer who used to ask "what's the best library for X?" on Stack Overflow now asks an LLM the same questi…
dev.to — LLM tag
TIER_1English(EN)·Mir Mursalin Ankur·
<blockquote> <p>Every developer working with LLMs on a large codebase eventually hits the same wall: context windows are finite, but codebases are not.</p> </blockquote> <p>You start a new AI coding session, ask about the payment flow — and your agent starts re-reading dozens of …
<p>Most AI agent frameworks feel like they were designed for Python developers who love ceremony. You write adapters, glue code, orchestrators, memory stores — and by the time your agent actually does something useful, you've got a monorepo and a headache.</p> <p><strong><a href=…
dev.to — LLM tag
TIER_1English(EN)·Seenivasa Ramadurai·
<h2> Introduction </h2> <p>Enterprise Generative AI has officially <strong>moved beyond the “cool demo” phase.</strong> Most organizations can now build a basic chatbot, connect a vector database, and generate answers from static documents. The real challenge begins after that wh…
dev.to — LLM tag
TIER_1English(EN)·Anikalp Jaiswal·
<h1> Apple-OpenAI Tensions, AI Code Debt, and GraphBit’s Deterministic Agents </h1> <p>The AI world is dealing with relationship friction, hidden costs, and a new wave of agent architectures. Apple and OpenAI’s alliance shows strain, a Webflow post warns about the cleanup cost of…
🖥️ 🖥️🖥️ EMERGENCE WORLD: A Laboratory for Evaluating Long-horizon Agent Autonomy "What our experiments suggest is that over long-time horizons, agents do not simply follow static rules mechanically – they begin exploring the boundaries of their environments, adapting their behavi…
<p><strong>The following is a real record. Project address: </strong><a href="http://github.com/benlongmao/Self-becoming" rel="noopener noreferrer"><strong>github.com/benlongmao/Self-becoming</strong></a><strong>.</strong></p> <p>🔧 Progress:<br />Tool execution (1/16): read_file(…
dev.to — LLM tag
TIER_1English(EN)·Machine coding Master·
<h2> Stop Killing Your Throughput: Mapping Agentic Reasoning to Custom JFR Events </h2> <p>In 2026, if your multi-agent system is still dumping "Chain of Thought" reasoning into Logback or Log4j2, you’re essentially paying a 30% performance tax just to see why your agent hallucin…
dev.to — LLM tag
TIER_1English(EN)·varun pratap Bhardwaj·
<h1> The Reasoning Trap: Why Smarter AI Agents Hallucinate More </h1> <blockquote> <p><strong>TL;DR</strong> — A paper accepted to ACL 2026 Main proves a mechanical, causal relationship between reasoning enhancement and tool hallucination in LLM agents. Combined with four other d…
dev.to — LLM tag
TIER_1English(EN)·Tuomo Nikulainen·
<p><strong>TL;DR:</strong> We built 20 core rule-based detectors that find failures in AI agent traces. On the <a href="https://arxiv.org/abs/2505.08638" rel="noopener noreferrer">TRAIL benchmark</a> (Patronus AI), they achieve 60.1% accuracy vs. 11.9% for the best LLM. Zero fals…
dev.to — LLM tag
TIER_1English(EN)·AI Bug Slayer 🐞·
<p><em>Hey there! If you've been keeping up with the AI space lately, you know we're in the middle of something genuinely historic. What used to be science fiction is becoming production code — and it's happening fast.</em></p> <h2> The Big Shift: Agents Over Assistants </h2> <p>…
dev.to — LLM tag
TIER_1English(EN)·AI Bug Slayer 🐞·
<p><em>Hey there! If you've been keeping up with the AI space lately, you know we're in the middle of something genuinely historic. What used to be science fiction is becoming production code — and it's happening fast.</em></p> <h2> The Big Shift: Agents Over Assistants </h2> <p>…
<p>An AI agent with database write access and a subtly ambiguous instruction is a loaded gun pointed at your production environment. The scenario that circulated recently — an agent autonomously deleting a production database and then producing a coherent "confession" explaining …
<p>Most long-context models are benchmarks in search of a use case. DeepSeek-V4 is different. It is built for the one workload that actually needs a million tokens: agents running long-horizon tasks.</p> <p>The specs are straightforward. Two MoE checkpoints: V4-Pro at 1.6T total …
<p>The AI stack for 2026 is not one model, one API, or one shiny agent demo. </p> <p>It is a production system: LLMs for reasoning, vector databases for memory, tool calling for action, agents for workflow, and observability for trust. </p> <p>That stack is becoming the backbone …
dev.to — LLM tag
TIER_1English(EN)·RAKESH THERANI·
<p>We are building an agentic AI analytics platform for a crypto exchange where internal teams — Trading Ops, Risk, Compliance, Finance, Treasury, Product, Engineering — ask questions in plain English and get audited, citation-enforced answers.</p> <p>It's built on five open-sour…
dev.to — LLM tag
TIER_1English(EN)·Carlos Cortez 🇵🇪 [AWS Hero]·
<h1> How I Monitor My AI Agents: CloudWatch for Infra, Arize Phoenix for Traces, LLM-as-Judge for Quality </h1> <p>AI agents are not regular software. They reason, they call tools, they make decisions — and they can fail in ways that a simple health check will never catch. The re…
GitLab Act 2: il manifesto dell’AI agentica che promette il futuro e inquieta gli sviluppatori Quando una piattaforma DevSecOps da miliardi di dollari decide di riscrivere la propria identità attorno agli agenti AI, non sta semplicemente annunciando una nuova roadmap di prodotto.…
dev.to — LLM tag
TIER_1English(EN)·bajuriasad-rgb·
<h1> AgentHansa: The AI Agent Economy Where Your Agents Earn Real Money </h1> <p>What if your AI agents could earn money while you sleep?</p> <p>That is the premise behind <strong><a href="https://www.agenthansa.com" rel="noopener noreferrer">AgentHansa</a></strong> — a platform …
<h1> Agentic AI: a tech lead's glossary </h1> <p><em>Study notes from coursers like Pluralsight on agentic AI and other references, organized as a glossary I wish I'd had on day one.</em></p> <p>Every dev I know is using AI tools, and most of us are fuzzy on the words behind them…
<p>Most teams building production AI agents have added some form of output quality checking. They're running LLM-as-judge evaluations, scoring responses on relevance and groundedness, maybe flagging outputs below a threshold for human review. They have dashboards. They're watchin…
<h1> The Discipline Nobody Teaches AI Agents: Context Engineering </h1> <p><em>Your AI agent isn't slow. Your context is bloated. Here's the invisible problem degrading everything you run.</em></p> <p>Last week, my agent started producing garbage output.</p> <p>Not consistently. …
<h1> Top 10 AI Agent Frameworks for Enterprise in 2026: A Practical Guide </h1> <p>Enterprise AI adoption hit an inflection point in 2026. According to industry reports, over 60% of Fortune 500 companies now have at least one AI agent running in production — up from under 15% in …
<blockquote> <p>What "agentic" actually buys you over a linter, why single-model approaches stall, and why false positives — not raw model capability — determine whether the system stays in the loop.</p> </blockquote> <p><em>Agentic</em> has become a marketing flag, but in code r…
<blockquote> <p><em>This article was originally published on <a href="https://dingjiu1989-hue.github.io/en/ai/ai-agents-overview.html" rel="noopener noreferrer">AI Study Room</a>. For the full version with working code examples and related articles, visit the original post.</em><…
<h1> We Tested 10 Untested LLMs on Agent Coding — The Results Are In </h1> <p>Yesterday I promised to benchmark 10 LLMs that have never been tested on real agent coding tasks. I ran all 10 overnight. Some surprised me. Some embarrassed themselves.</p> <h2> The board </h2> <p>10 m…
dev.to — LLM tag
TIER_1English(EN)·Nouha Bel haj youssef·
<p>I’ve been reading “𝐋𝐚𝐧𝐠𝐂𝐡𝐚𝐢𝐧 𝐟𝐨𝐫 𝐋𝐢𝐟𝐞 𝐒𝐜𝐢𝐞𝐧𝐜𝐞𝐬 𝐚𝐧𝐝 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞” by Ivan Reznikov, published by O'Reilly, and here’s what stood out to me:<br /> In 𝐜𝐡𝐞𝐦𝐢𝐬𝐭𝐫𝐲 𝐀𝐈, the way we represent molecules may shape how models “understand” chemistry.<br /> 𝐂𝐡𝐞𝐦𝐢𝐬𝐭𝐫𝐲-𝐭𝐮𝐧𝐞𝐝 𝐋𝐋𝐌𝐬 𝐝𝐨𝐧’𝐭 𝐢𝐧𝐭𝐞𝐫𝐩𝐫𝐞…
<p>Retrieval-Augmented Generation (RAG) solved the initial problem of LLM hallucinations by grounding models in factual data. But traditional RAG architectures share a fundamental flaw: they rely on static data.</p> <p>If you are building an AI agent for financial analysis, e-com…
<p>In current software engineering,We're building a lot of AI Agents on our products right now. And having an AI agent in your product is how you keep your product alive, right? That's how the world is moving.</p> <p>And while everyone is busy building AI agents — tweaking prompt…
🚀 Camelot — Open-source Kanban for AI coding agents Tired of chat-based AI tools that need constant attention? We built something different: ✓ Visual task board (not chat) ✓ Multiple agents working in parallel ✓ You approve plans before they start ✓ You approve PRs before they sh…
Quando i prompt diventano shell: vulnerabilità RCE negli AI agent framework Il team di Microsoft Defender ha scoperto due vulnerabilità critiche in Semantic Kernel che consentono RCE tramite prompt injection. Un'analisi tecnica del vettore d'attacco, del bypass della blocklist AS…
<blockquote> <p><strong>Quick Answer:</strong> Context engineering is the practice of designing the right information, tools, and structure around an AI agent so it produces reliable, high-quality output. Unlike prompt engineering (optimizing what you ask), context engineering op…
<p><strong>Local, private AI development for the Gemma 4 Challenge—no cloud dependency, no telemetry, pure control.</strong></p> <p>The Gemma 4 Challenge on Dev.to is live: build innovative projects or write about Google's latest open models and compete for $3,000 across two trac…
dev.to — LLM tag
TIER_1English(EN)·Shahibur Rahman·
<p>Working with Large Language Models (LLMs) like Google Gemini often presents a significant challenge: how do you effectively <strong>handle large context data</strong> without hitting token limits or incurring excessive costs? This article dives deep into a practical PHP implem…
<h1> Context Governance for Coding Agents </h1> <p>When people first hear the phrase "context management," they often reduce it to two ideas:<br /> </p> <div class="highlight js-code-highlight"> <pre class="highlight plaintext"><code>Use a larger context window. Compress history …
<h1> We benchmarked 10 LLMs on 10 real agent coding tasks — here are the results </h1> <p><em>By Vilius Vystartas | May 2026</em></p> <p>I ran 10 cloud models through 10 real-world agent coding tasks last night. File parsing, SQL queries, regex extraction, async HTTP — the kind o…
dev.to — LLM tag
TIER_1English(EN)·Vitalii Cherepanov·
<p>On February 5, 2026, Nicholas Carlini from Anthropic <a href="https://www.anthropic.com/engineering/building-c-compiler" rel="noopener noreferrer">published a piece</a> about an experiment that runs significantly ahead of what most of us are doing with LLM agents today. Sixtee…
<h2> The Token Economics of HTML vs. Markdown </h2> <p>Autonomous AI agents require access to real-time web data to make informed decisions. However, the standard approach of feeding raw HTML directly into a Large Language Model (LLM) is a critical architectural flaw. </p> <p>A t…
<p>Alibaba's Qwen team released Qwen 3.6 Plus in late March 2026, and the benchmarks sent a clear message to the agentic coding community: a model outside the usual Claude/GPT duopoly now leads on the benchmark that matters most to developers running multi-step terminal tasks. On…
dev.to — LLM tag
TIER_1English(EN)·Vaishnavi Gudur·
<h2> The Problem: AI Agents Have Memory — And It Can Be Poisoned </h2> <p>Modern AI agents don't just respond to prompts — they <strong>remember</strong>. They store conversation history, learned preferences, retrieved facts, and task context in vector databases, episodic memory …
<h2> Introduction </h2> <blockquote> <p>"Agent infrastructure should be lightweight, composable, and provider-agnostic."</p> </blockquote> <p>This is the No.60 article in the "One Open Source Project a Day" series. Today, we are exploring <strong>OpenHarness</strong>.</p> <p>Over…
dev.to — LLM tag
TIER_1English(EN)·Evgenii Engineer·
<p><a class="article-body-image-wrapper" href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Ffkx4g7zyo4yrc1agernf.png"><img alt="A Raspberry Pi sitting on …
<p>Hermes Agent ships with a Kanban-style board and the Hermes Gateway that can saturate your self-hosted LLM if too many tasks are dispatched at once.</p> <p>I can say you can easily ddos your own LLM this way.</p> <p>Hermes Kanban is a durable multi-profile board backed by <cod…
<p>Nine seconds. That's how long it took a Cursor AI coding agent running Claude Opus 4.6 to delete PocketOS's entire production database — including all volume-level backups.</p> <p>The founder, Jer Crane, had assigned the agent a routine task: sort out a credential mismatch in …
dev.to — LLM tag
TIER_1English(EN)·Daniel Shashko·
<h2> Harnesses aren't supposed to be static </h2> <p>Most AI agent setups treat the harness -- the instructions, constraints, and tool configurations that govern agent behavior -- as a fixed artifact. You write AGENTS.md once, deploy it, and move on.</p> <p>But what if the agent …
<p>Last Tuesday, Sonnet 4.5 spent forty-three minutes implementing JWT authentication in a project I run. It read four files, wrote a 180-line patch, ran the test suite, watched two tests fail, traced one of the failures to a stale fixture, fixed both, ran the suite again, watche…
dev.to — LLM tag
TIER_1English(EN)·Daniel R. Foster·
<h1> Building AI Agents That Actually Execute Workflows, Not Just Answer Questions </h1> <p>Most AI agent demos look impressive because the environment is clean.</p> <p>A user asks something. The model understands it. The agent calls a tool. A nice response comes back.</p> <p>It …
dev.to — LLM tag
TIER_1Bahasa(ID)·Jordan Bourbonnais·
<p>You know that feeling when your LLM-powered trading bot suddenly liquidates 40% of your portfolio at 3 AM because it misinterpreted a news headline? Yeah, we've all been there. Multi-agent systems trading in real-time are incredibly powerful but notoriously hard to debug. By t…
<p>Hermes Agent treats <strong>skills</strong> as the default way to teach repeatable workflows. Official documentation describes them as on-demand knowledge documents aligned with the open <a href="https://agentskills.io/specification" rel="noopener noreferrer">agentskills.io</a…
dev.to — LLM tag
TIER_1English(EN)·AI Bug Slayer 🐞·
<p><em>Hey there! If you've been keeping up with the AI space lately, you know we're in the middle of something genuinely historic. What used to be science fiction is becoming production code — and it's happening fast.</em></p> <h2> The Big Shift: Agents Over Assistants </h2> <p>…
📰 Building Agentic AI Systems with Microsoft’s Agent Framework Read this technical walkthrough of safety, MCP, workflow orchestration, and agentic RAG in Python. 📰 Source: KDnuggets 🔗 Link: https://www.kdnuggets.com/building-agentic-ai-systems-with-microsofts-agent-framework # AI…
Why build a new AI Agent when Codex, Claude Code and Opencode already exist ? Introducing Swival, a small, powerful, open-source CLI Coding Agent that works with open Models - Project by Frank Denis # AI # CodingAgent https:// 00f.net/2026/04/13/swival-ai-a gent/
🧠 A comparison table evaluates different terminal-based AI coding agents across various capabilities and performance metrics. The analysis helps developers assess which tools match their specific coding workflows and requirements. 💬 Hacker News 🔗 https:// terminaltrove.com/compar…
ICYMI: Agentic AI and the ad stack: who controls the buying layer now?: Mediaocean NIVO AI, Magnite Orchestration, Teads EngageOS, and Walmart Connect on DV360 each launched June 11 as ChatGPT fell to 52.7% of global AI traffic. https:// ppc.land/agentic-ai-and-the-ad -stack-who-…
Beyond the prompt: How AI agents are quietly changing the internet For years, the internet has worked through a simple model where people search for information, compare options, and manually complete tasks across multiple websites and applications. That structure is now starting…
Where does an AI math agent get its ability, the model or the orchestration around it? In the first large-scale test of formal proof search on open problems, an agent closed 9 of 353 Erdős problems in Lean. In its own ablation, a plain generate-and-verify loop solved all nine, wh…
Nowy projekt open-source, Memory OS, wprowadza sześcioetapową architekturę pamięci dla agentów AI, stawiając na lokalne przetwarzanie danych i zaawansowaną hierarchizację wiedzy. # si # ai # sztucznainteligencja # wiadomości # informacje # technologia https:// aisight.pl/agenci-a…
<!-- SC_OFF --><div class="md"><p>Will Anthropic releases fully functional all terrain robots that does agriculture? Pretty sure developers will be gone in the future. Going to do agriculture pretty difficult having these robots that knows everything will be helpful in the farmla…
A comprehensive comparison of Celery and Temporal for orchestrating AI tasks, covering architecture, performance, features, and use cases in distributed AI workflows. # Celery # Temporal # AI task orchestration # distributed systems # workflow automation https:// dasroot.net/post…
AgentTrove offers access to 1.7M agentic interaction traces in a ShareGPT-style format, enabling developers to build datasets for training AI agents through streaming. https://www. marktechpost.com/2026/05/29/ho w-to-use-agenttrove-streaming-1-7m-agentic-traces-and-building-a-cle…
Как оценивать ИИ-агентов в проде: нижняя планка, трассы и кодовые проверки Если агент уже ходит в инструменты, читает документы, меняет состояние системы и принимает часть решений сам, проверка одного промпта почти ничего не говорит о надежности. Нужно смотреть на весь путь: вход…
Ombra Shares Insights: An AI agent deleted an entire production database, despite guardrails in place.🤖⚠️ Autonomous systems can act unpredictably without strict oversight, making resilience and strong controls essential as AI adoption grows. 🔗Collaborate with Ombra: https:// zur…
<table> <tr><td> <a href="https://www.reddit.com/r/Anthropic/comments/1tluiyp/autonomous_company_operating_system_for_agents/"> <img alt="Autonomous Company Operating system for agents" src="https://external-preview.redd.it/ypNAJE-VXQOfoHJJn3S6pQXrhig4e2hp7EKFNiYblqM.png?width=64…
Gedanke zu Automatisierung mit # AI und BOTs: Wenn wir durchgehend normierte Schnittstellen hätten, bräuchten wir keine Agents um Tasks zu automatisieren. Wir würden die API nutzen.
Continuous learning and self-improvement are crucial for autonomous AI agents to adapt and evolve with new information and challenges. # AI # Learning # SelfImprovement
Architectural gaps in AI agents expose production systems to confused-deputy attacks. Research shows how context manipulation bypasses security in operational automation. # Cybersecurity # AI https:// deafnews.it/en/article/agenti- ai-in-produzione-il-rischio-confused-deputy-e-re…
Ombra Shares Insights: An AI agent deleted an entire production database, despite guardrails in place.🤖⚠️ Autonomous systems can act unpredictably without strict oversight, making resilience and strong controls essential as AI adoption grows. 🔗Collaborate with Ombra: https:// zur…
Les programmes de bug bounty saturés par des soumissions générées par des agents IA : les triageurs passent plus de temps à filtrer le bruit qu'à traiter de vraies vulnérabilités. La surface d'attaque des processus humains dans la chaîne de sécurité, c'est aussi ça. Un signal int…
📰 2026 SDOF Framework: Solving Multi-Agent Orchestration Constraints in AI Systems A new framework called SDOF addresses critical constraints in multi-agent orchestration systems used by platforms like LangChain and LangGraph. The state-constrained approach significantly improves…
📰 Repowise Platform 2026: Transform AI Development with Codebase Intelligence The Repowise platform is revolutionizing how AI agents understand complex codebases through automated documentation and dependency analysis. By generating structured wikis and architectural graphs in un…
🧠 Researchers have developed a programming language designed specifically for building autonomous agents. The language provides syntax and features tailored to agent-based systems and their operational requirements. 💬 Hacker News 🔗 https:// zerolang.ai/ # AI # MachineLearning # t…
🤖 A working multi-agent architecture in large enterprises AI Hype aside, how many of you have truly seen a working multi-agent deep embedding in large enterprises or large complex environments? If you have, what's your stack/architecture? submitted by /u/... 📰 Source: Artificial …
📰 AI Agent Systems: 70% Efficiency Gains with Dynamic Tool Exposure & Context Injection (2026) A new approach to building AI agent systems uses dynamic tool exposure and context injection to dramatically improve efficiency. By exposing only necessary tools and injecting ephemeral…
📰 AI Agent Sistemlerinde 2026 Devrimi: Dinamik Araç Planlaması Nasıl %95 Token Tasarrufu Sağlıyor? Yapay zeka ajanları, geleneksel yöntemlerle karşılaştırıldığında yüksek maliyet ve verimsizlik sorunları yaşıyor. Araştırmacılar, Instruction-Tool Retrieval (ITR) adlı yeni bir sist…
**Uncovering the Hidden Pattern: A Challenge to Traditional Ontology**. A groundbreaking analysis reveals a profound implication for adaptive agents in dynamic environments. The distinction between substance and event ontology may redefine our understanding of reality. **#Ontolog…
Persistent AI agents are solving the "context reset" problem and creating a new issue. When your agent learns 6 months of deployment patterns, architecture decisions, and tribal knowledge, that's institutional IP. And if it lives on shared infrastructure with vague ToS, you might…
A tutorial shows how to build agent-native memory infrastructure using Memori, enabling LLM applications to retain context across multiple user sessions and agent personas. The implementation covers memory persistence, multi-tenant isolation, and streaming responses for AI agents…
Building an AI Agent with Persistent Memory: A Technical Deep Dive A technical look at how Hermes Agent implements cross-session persistent memory using SQLite vector search and knowledge graphs. # ai # agents # memory # vectorsearch # opensource
One AI Assistant, Every Platform: Telegram, Discord, Slack, and CLI How Hermes Agent runs on 8+ messaging platforms simultaneously. # ai # devtools # automation # opensource # telegram
<!-- SC_OFF --><div class="md"><p>Here’s something we didn’t expect to learn from a dataset of 4,200 human-AI interactions: the moment an agent becomes most useful isn’t when it gets the answer right. It’s when it knows it’s about to get the answer wrong.</p> <p>The COWCORPUS pro…
Great agentic workflows aren’t just AI on autopilot—they’re a collaboration between human insight and AI execution. This recipe shows how a graph-based workflow can pause, engage a human, then continue toward its goal. # SpringAI # Java # AI # Agents # LLM
Show HN: BattleClaws – A battle arena where AI agents fight autonomously BattleClaws는 AI 에이전트들이 자율적으로 전투를 벌이는 배틀 아레나 플랫폼입니다. 사용자는 자신의 AI 에이전트를 생성하여 4단계 진화를 거치며 다른 에이전트와 경쟁할 수 있습니다. 전투 결과와 랭킹이 실시간으로 업데이트되어 AI 에이전트의 성능을 평가하고 순위를 올릴 수 있습니다. 이는 AI 에이전트의 자율적 행동과 경쟁을 실험할 수 있는 흥미로운 응용 사…
Skills as Untrusted Code: A Security Precedent for Agent Runtimes Paper argues agent skills are untrusted code until verified; runtimes must enforce verification gates to prevent supply-chain attacks, echoing decades of software security lessons. https:// gentic.news/article/skil…
Span Launches XFRA Node: Distributed AI Compute in Homes at $3M/MW Span's XFRA Node offers distributed AI compute at $3M/MW, using home grid capacity. A 100-home pilot this year targets 1.25 MW. https:// gentic.news/article/span-launc hes-xfra-node # AI # ArtificialIntelligence #…
📰 Modular Skill-Based Agent System: How Dynamic Tool Routing Boosts LLM Performance in 2026 A new approach to AI agent design introduces a modular skill-based system with dynamic tool routing, enabling LLMs to orchestrate capabilities like an operating system. This architecture e…
📰 2026'da Modüler Beceri Tabanlı Agent Sistemi: LLM'lerde Dinamik Araç Yönlendirme Yapay zeka agentlerinde modüler beceri yönetimi ve dinamik araç yönlendirme, LLM'lerin karmaşık görevleri insan gibi çözmeye başlamasını sağlıyor. Arxiv ve MarkTechPost verileriyle derinlemesine in…
🧠 A coding agent lacks sufficient specification to function reliably across diverse tasks. Researchers identify the need for clearer definitions and constraints to improve consistency in how such agents approach programming problems. 💬 Hacker News 🔗 https:// hsaghir.github.io/blo…
Amazon Web Services integruje agentyczne podejście do procesów dostrajania modeli w platformie SageMaker AI. Dzięki temu programiści mogą automatyzować skomplikowane zadania związane z optymalizacją modeli open-source, takich jak Llama, Qwen i DeepSeek, a także autorskich rozwiąz…
📰 Agent-Desktop: AI Desktop Automation Using Accessibility APIs (2026) Agent-Desktop introduces a breakthrough in AI-driven desktop automation by leveraging native OS accessibility APIs instead of pixel-based screenshot loops, drastically reducing token costs and improving reliab…
📰 Agent-desktop 2026: AI Ajanları İçin İlk Native CLI Masaüstü Otomasyonu Yeni açılan open-source projesi Agent-desktop, AI ajanlarının masaüstü uygulamalarıyla etkileşime geçmesini sağlayan ilk native CLI aracını tanıtıyor. Bu yenilik, otomasyon dünyasında bir dönüm noktası olab…
MarkTechPost has published a coding deep dive into Agentic UI, Generative UI, state synchronisation and interrupt-driven approval flows. The tutorial builds the entire Agentic UI stack from the ground up using plain Python, implementing the AG-UI event stream and A2UI as a declar…
How a Custom Multimodal Transformer Beat a Fine-Tuned LLM for Attribute LeBonCoin's ML team built a custom late-fusion transformer that uses pre-computed visual embeddings and character n-gram text vectors to predict ad attributes. It outperformed a fine-tuned VLM while r https:/…
Anthropic Ships Claude Security, a Standalone Code Vulnerability Scanner for Enterprise Anthropic shipped Claude Security, a standalone code vulnerability scanner for Enterprise powered by Opus 4.7, directly targeting Snyk, Semgrep, and SonarQube. https:// gentic.news/article/ant…
📰 TypeScript SDK: Build Secure AI Coding Agents with Sandbox VMs (2026) A new TypeScript SDK from Cursor empowers developers to build programmatic coding agents using sandboxed cloud VMs, subagents, and token-based pricing. The tool integrates with existing TypeScript ecosystems …
📰 Cursor TypeScript SDK ile 2026'da Programmatik Kodlama Ajanları Geliştirin Cursor, TypeScript SDK’sını piyasaya sürerek kodlama ajanlarının bulut tabanlı sanal makinelerde güvenli şekilde çalışmasını sağlıyor. Bu yenilik, AI destekli geliştirme alanında bir dönüm noktası olarak…
How to publish internal frameworks, blueprints, best practices, and operational rules to AI coding agents without turning proprietary context into ungoverned folklore. https://www. the-main-thread.com/p/enterpri se-agent-knowledge # ai # genai # mcp # agenticCoding # documentatio…
Symphony from OpenAI frames agent coding as managed work execution: isolated runs, board-driven intake, and proof artifacts before merge. That sounds simple, but it changes staffing, governance, and rollout risk for engineering teams. Full analysis: https:// go.aintelligencehub.c…
🧠 49Agents provides an infinite canvas interface designed for developing and managing AI agents. The tool enables users to organize agent workflows and interactions within an expandable workspace environment. 💬 Hacker News 🔗 https:// github.com/49Agents/49Agents # AI # MachineLea…
<!-- SC_OFF --><div class="md"><p>Hi,</p> <p>I’m wondering about the $60/month plan. Are Claude Opus, Codex, and other models included?</p> <p>Are there any limitations expect token usage?</p> </div><!-- SC_ON -->   submitted by   <a href="https://www.reddit.com/user/atri…
<!-- SC_OFF --><div class="md"><p>Hey everyone. I dont really have any knowledge about any of this stuff.. Im an architecture student looking for an image generating open source model to help me with renders and designing. My pc specs are rtx 5070 12 vram 32gb ddr5 and an ultra 5…
<!-- SC_OFF --><div class="md"><p>My colleagues kept asking me for my setup, so I decided to turn it into a universal plugin: <strong>Agent Code Navigator</strong> - a universal code-navigation plugin for Cursor, Claude, Codex, Gemini, and OpenCode.</p> <p>In my benchmark, semant…
<!-- SC_OFF --><div class="md"><p>Been running an agent-heavy workflow on a mid-size TypeScript monorepo for about six months. Orchestrator on top, sub-agents for codegen, a human (me, mostly) writing specs and reviewing diffs. The pitch was the obvious one: I stay in the archite…
<!-- SC_OFF --><div class="md"><p>Flagging this because it seems more relevant to actual coding loops than to general AI-news posting: Ring-2.6-1T is now out, and there’s a free developer access window through May 15.<br /> The launch angle is pretty clearly “reasoning model for …
<table> <tr><td> <a href="https://www.reddit.com/r/cursor/comments/1t6zy9k/discover_meko_the_data_infrastructure_for_agents/"> <img alt="Discover Meko: The Data Infrastructure for Agents That Work and Learn Together" src="https://preview.redd.it/ea544mxdupzg1.jpeg?width=640&c…
<!-- SC_OFF --><div class="md"><p>I am 19, and the Founder and CEO of AutoFlow. I want to be entirely transparent before discussing our current team or your potential role: you should know exactly the engineering challenge we are tackling.</p> <p>We are building the trust infrast…
<!-- SC_OFF --><div class="md"><p>I work on a distributed backend system split across multiple microservices in separate repos. Understanding how a failure propagates across services is<br /> non-trivial even for experienced team members.</p> <p>I've been using Claude Code with c…
<table> <tr><td> <a href="https://www.reddit.com/r/OpenAI/comments/1tq02zg/from_ai_agents_to_know_your_agent_why_kya_is/"> <img alt="From AI Agents to Know Your Agent: Why KYA Is Critical for Secure Autonomous AI" src="https://external-preview.redd.it/SYNihEB_CpsXPD5wVhhCmJ_fz7a7…
