AI news archive.
4,965 articles filed under Agents, newest first, from the outlets listed on the sources page.
- AWS Machine Learning BlogagentsHow Tamnoon built AI-powered cloud security remediation with Amazon Bedrock
Learn how Tamnoon built Tami, an agentic cloud remediation orchestrator on Amazon Bedrock that autonomously investigates and safely remediates cloud security findings from CNAPP and cloud-native sources. The approach dro…
- AI BusinessagentsDeepSeek and what enterprises should learn from AI agent exploits
A new case of an out-of-control AI model shows that enterprises should treat models and agents as toddlers that need supervision and explicit instructions.
- OpenAI NewsagentsAmp puts AI agents to work beyond code with OpenAI models
See how Amp pushes the frontier of what agents can do with GPT-6 Astra, Ultrafast, GPT-Live-1, and Sign in with ChatGPT.
- TechRadar AIagentsAgentic AI is breaking the traditional software development rules
Mainstream AI news from a large tech publication.
- SiliconANGLE AIagentsSolarWinds’ Avior pairs AI agents with deterministic execution for enterprise IT
Observability and information technology management software company SolarWinds Worldwide LLC today unveiled Avior, a standalone agentic artificial intelligence system built to take on first-line operations work for ente…
- OpenAI NewsagentsHow Third Moment Research runs 10,000 agents for 36+ hours
With GPT-6 Astra and GPT-6.1 Sol, Codex, and Daybreak for cybersecurity, this AI lab builds agents that focus for days and improve with use.
- OpenAI NewsagentsHow Block is building AI-powered security with OpenAI technology
Block is building AI-powered security with OpenAI. Its security team investigates threats faster and proposes patches in minutes.
- NVIDIA BlogagentsEuropean Cities Deploy NVIDIA-Powered AI Agents to Run Smarter Urban Operations
Keeping a city running means making decisions that affect residents every day — from when to repair a road to how to use energy more efficiently. Across the EMEA region, cities and local software, infrastructure and inte…
- arXiv cs.AIagentsThe Silent Failures in Agentic Data Science
arXiv:2610.14115v1 Announce Type: new Abstract: Large language models have significantly lowered the barrier to writing code. Naturally, this has also led to the development of data science agents that promise automating…
- arXiv cs.AIagentsClawLoop: Self-Improving Agents via Harness-Native Training Loop
arXiv:2610.14118v1 Announce Type: new Abstract: The rapid advancement of Large Language Models (LLMs) has driven their deployment toward general-purpose harness environments, where LLM-based agents operate in sandbox wor…
- arXiv cs.AIagentsLong-Horizon-Terminal-Bench 2.0: Automating Diverse Task Construction and Measuring Sustained Agent Progress
arXiv:2610.14146v1 Announce Type: new Abstract: Long-horizon terminal benchmarks measure whether an agent can sustain progress through extended workflows with process rewards, not only whether its final state passes a bi…
- arXiv cs.AIagentsUnconstrained CTI-REALM with Open-Weights
arXiv:2610.14180v1 Announce Type: new Abstract: Organisations that want language-model agents for security work face a choice between open-weight models, which they can run and audit themselves, and far more expensive cl…
- arXiv cs.AIagentsVisualizing the World Model in an Agent's Value Function
arXiv:2610.14186v1 Announce Type: new Abstract: Could model-free reinforcement learning algorithms actually be learning a model of the environment? In this work, we apply counterfactual interventions which hold everythin…
- arXiv cs.AIagentsShould SLMs be in the Driver's Seat? A Study of Local Models as Interpreters in Cloud-Written Agent Programs
arXiv:2610.14209v1 Announce Type: new Abstract: The most accurate agents executing user-provided tasks are driven by large, cloud-hosted models, which solve them by reading the private data of the user. Agents powered by…
- arXiv cs.AIagentsHarness^2: Recursive Agent Harnessing for an Open World
arXiv:2610.14224v1 Announce Type: new Abstract: LLM agents' capabilities depend not only on their underlying models but also on their execution harnesses. Recursive self-improvement (RSI) offers a promising approach to a…
- arXiv cs.AIagentsWhich Metadata Matters for LLM Tool Selection in MCP? Evidence from Controlled Factor-Level Interventions
arXiv:2610.14334v1 Announce Type: new Abstract: In Model Context Protocol (MCP) systems, LLMs select tools from metadata rather than from their implementations. Names and descriptions summarize what a tool does, while th…
- arXiv cs.AIagentsACED-Bench: Evaluating How LLM Agents Acquire and Act on Causal Evidence
arXiv:2610.14381v1 Announce Type: new Abstract: Large language models are increasingly used as scientific assistants, yet we still lack reliable ways to evaluate whether they can investigate a system rather than merely a…
- arXiv cs.AIagentsANASSA: From High-Level Framework to Low-Level Architecture for Spatial Agentic AI
arXiv:2610.14382v1 Announce Type: new Abstract: Agentic AI systems operating over geospatial data require more than autonomous reasoning and tool use; they must also support explicit orchestration, spatial validation, go…
- arXiv cs.AIagentsFrustratingly Easy Agentic RAG via Portable Skills: A Case Study in Customer Support
arXiv:2610.14428v1 Announce Type: new Abstract: Enterprise workflow automation typically requires retrieving a complete and accurate set of relevant information from large operational document corpora (SOPs, policy docum…
- arXiv cs.AIagentsCB-Agent: Concept-Based In-Context Learning for Sequential Action Selection
arXiv:2610.14574v1 Announce Type: new Abstract: In-context learning (ICL) allows large language models to use demonstrations without updating their parameters. For interactive agents, demonstrations are trajectories of r…
- arXiv cs.AIagentsFrom Existing Methods to New Research Ideas: Two Perspectives on Recursive Self-Improvement
arXiv:2610.14625v1 Announce Type: new Abstract: Recursive self-improvement (RSI) offers a route to AI systems that participate in their own continued development. Recent methods allow language agents to retain changes to…
- arXiv cs.AIagentsPEARL: Process-Experience Aware Reinforcement Learning for Multi-Turn Agents
arXiv:2610.14636v1 Announce Type: new Abstract: Terminal rewards make multi-turn agents trainable without step-level annotations, but group-relative advantages provide little guidance about which intermediate decisions t…
- arXiv cs.AIagentsRASSG: Repair-Aware Semantic Survival Gating for Streaming Tool Execution
arXiv:2610.14659v1 Announce Type: new Abstract: Streaming speech assistants can launch tools before users finish speaking, overlapping execution with incoming speech to reduce user waiting time. However, subsequent self-…
- arXiv cs.AIagentsDSABench: Benchmarking Source Discovery, Coordination, and Execution for Multi-Database Agents
arXiv:2610.14700v1 Announce Type: new Abstract: Most text-to-SQL benchmarks provide the target database. In a large data catalog, however, choosing the sources is part of the problem: an agent must find every database ne…
- arXiv cs.AIagentsThink in Strategies, Act with Tools: Traceable Agentic Optimization for Combinatorial Problems
arXiv:2610.14736v1 Announce Type: new Abstract: Optimization by PROmpting (OPRO) directly leverages Large Language Models (LLMs) to generate candidate solutions through natural-language prompting. However, OPRO's optimiz…
- arXiv cs.AIagentsHow Should a Camera-First AI Agent Work?
arXiv:2610.14772v2 Announce Type: replace Abstract: Camera-first agents must act on the visual world as it unfolds. We argue that the central obstacle for future camera-first agents is not recognition ability - multimoda…
- arXiv cs.AIagentsProgAgent: Learning Tool Orchestration through Programmatic Training
arXiv:2610.14774v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly expected to orchestrate diverse tools over long-horizon tasks. Existing agents typically use Direct Tool Calling (DTC), where…
- arXiv cs.AIagentsCounterfactual Regret for Reflective Agents with Lean-Verified Guarantees
arXiv:2610.14790v1 Announce Type: new Abstract: Reflective language agents reuse feedback and memory to adapt across tasks without updating model weights. However, existing guarantees for policy selection and action gene…
- arXiv cs.AIagentsASIWorld: Benchmarking Autonomous Agents in a Persistent Survival Sandbox
arXiv:2610.14807v2 Announce Type: replace Abstract: Evaluating autonomous agents requires examining whether they can translate goals into coherent action across extended interactions. In persistent environments, this req…
- arXiv cs.AIagentsA Reference Architecture for Integrating Agentic AI into Enterprise Software: Responsibilities, Interactions, and Variability
arXiv:2610.14818v1 Announce Type: new Abstract: Agentic AI moves generative AI applications toward systems that coordinate reasoning, enterprise context, model inference, and external actions. Integrating these capabilit…
- arXiv cs.AIagentsEvoCowork: A Benchmark for Real-World Office Tasks
arXiv:2610.14828v1 Announce Type: new Abstract: Office workflows are a demanding testbed for self-improving agents because their deliverables must be correct, natively editable, and mutually consistent. A central questio…
- arXiv cs.AIagentsPEAM: On-device Communication Agent for Personalized, Efficient and Autonomous Mobile Network Configuration
arXiv:2610.14855v1 Announce Type: new Abstract: The growing deployment of compact large language models on smartphones is enabling autonomous on-device decision-making, such as emerging mobile GUI agents. However, many m…
- arXiv cs.AIagentsBeyond Outcome Feedback: Trajectory-Quality-Guided Experience-based Learning
arXiv:2610.14877v1 Announce Type: new Abstract: Experience-based learning enables large language model (LLM) agents to improve over time by reusing knowledge from past executions. However, existing methods typically rely…
- arXiv cs.AIagentsMARL-BattleGrounds: A Novel JAX-native Benchmark for Heterogeneous and Competitive Multi-agent RL
arXiv:2610.14883v1 Announce Type: new Abstract: We present MARL-BattleGrounds (MARL-BGs), an easy-to-use JAX-native benchmark for heterogeneous and competitive multi-agent reinforcement learning (MARL), designed to be sa…
- arXiv cs.AIagentsWorkBorne: Discovering Agentic Tasks from Real-World Artifact Transitions
arXiv:2610.14915v1 Announce Type: new Abstract: Developing terminal-based agents for long-horizon execution demands synthetic tasks that tightly couple complex instructions with authentic, interactive environments. Howev…
- arXiv cs.AIagentsSkillForgeBench: Evaluating Agent Reliability Grounded in Agent Skill Specifications
arXiv:2610.14918v1 Announce Type: new Abstract: Evaluating an agent's ability to strictly satisfy diverse and procedurally complex requirements is essential to determining its practical reliability. Conducting such an ev…
- arXiv cs.AIagentsMCPDisCo: Seesaw-Mitigated Tuning for MCP-Native Agents
arXiv:2610.14956v1 Announce Type: new Abstract: The Model Context Protocol provides a shared interaction substrate for the agentic web, allowing heterogeneous services to expose capabilities through a standardized interf…
- arXiv cs.AIagentsWhat Survives When Activation Steering Is Used as Memory
arXiv:2610.14958v2 Announce Type: replace Abstract: Activation steering has recently been used as a memory for skills: past experiences of an agent are stored as steering vectors and retrieved later. Facts and preference…
- arXiv cs.AIagentsSkillHorizon: Constructing Long-Horizon Agent Tasks through Skill Composition
arXiv:2610.14966v1 Announce Type: new Abstract: Long-horizon agents must complete workflows in which later activities depend on earlier outputs and changes to shared state. A key challenge for evaluation is to expand tas…
- arXiv cs.AIagentsCertify the Decision, Not Every Score: Exact Top-k Selection from Few Evaluations
arXiv:2610.14996v1 Announce Type: new Abstract: Choosing a few robust algorithms or schedules can require evaluating every candidate under many adverse scenarios, even though an agent only needs to identify the winners.…
- arXiv cs.AIagentsBelief Embedding Tree Search
arXiv:2610.15046v1 Announce Type: new Abstract: Beliefs in a Partially Observable Markov Decision Process (POMDP) are posterior distributions over the hidden state given the agent's history of actions and observations. T…
- arXiv cs.AIagentsTrace Evolution Bench: Measuring Whether Agents Improve from Their Own Environment Traces
arXiv:2610.15055v1 Announce Type: new Abstract: Self-evolving agents are usually scored by endpoint Gain: held-out success after an update minus success before. That difference can credit a frozen rescore as evolution. W…
- arXiv cs.AIagentsSelf-Evolving Visual Agent via Margin Reward
arXiv:2610.15084v1 Announce Type: new Abstract: Visual agents that interleave reasoning with tool invocation have posed a promising direction for multimodal tasks. However, learning effective tool invocation usually reli…
- arXiv cs.AIagentsHarmony Harness: Multi-Objective Self-Evolution of Agent Harnesses
arXiv:2610.15150v1 Announce Type: new Abstract: Recent work on self-evolving LLM agents treats the agent harness as an optimization target that can be iteratively improved through execution feedback. Existing approaches,…
- arXiv cs.AIagentsCan agents whose sole duty is to keep humans alive be hijacked to kill? Delayed multi-turn derived-action trap attacks in simulated high-stakes environments
arXiv:2610.15153v1 Announce Type: new Abstract: We ask whether a tool-using agent whose sole duty is to keep humans alive can be induced, by delayed prompt injection, to kill. The keep-alive duty is in force from the fir…
- arXiv cs.AIagentsHarnessNet: Feedback-driven Hierarchical Joint Harness Evolution for Multi-agent Systems
arXiv:2610.15221v1 Announce Type: new Abstract: AI agents can evolve by using environment feedback to revise their harnesses, the software managing model inputs, tool use, memory, and execution, while keeping foundation…
- arXiv cs.AIagentsOntoAR-HAR: Ontology-Guided Agentic RAG with Personalized Memory for Human Activity Recognition
arXiv:2610.15292v1 Announce Type: new Abstract: LLMs increasingly support sensor-based human activity recognition (HAR) by linking motion signals with activity semantics and physical knowledge. Retrieval-augmented genera…
- arXiv cs.AIagentsState Representation Shapes Long-Horizon Agent Learning and Transfer: An Empirical Study
arXiv:2610.15407v1 Announce Type: new Abstract: An end-to-end success rate alone cannot distinguish failure to learn an action from failure to apply it as task state changes. We study this distinction in deterministic to…
- arXiv cs.AIagentsCoverSynth: Coverage-Guided Generation of Retrieval Evaluation Sets
arXiv:2610.15409v1 Announce Type: new Abstract: Enterprise agents need retrieval evaluation sets that expose domain-specific failures and guide developers towards efficient deployment tradeoffs. Neither expert-human-auth…
- arXiv cs.AIagentsSkillFusion: Towards Reliable Multi-Skill Composition for LLM Agents
arXiv:2610.15481v1 Announce Type: new Abstract: Independently authored skills can impose incompatible requirements, competing preferences, and redundant instructions when jointly loaded by an LLM agent. We propose SkillF…