AI Agent Monitoring

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AI agent monitoring studies how to observe and assess agents while they use tools, pursue long-running tasks, and interact with other agents, so problems can be detected before they cause harm or waste. Common approaches include trajectory classifiers and LLM judges, early-warning models over trace prefixes, formal runtime checks and action gates, and telemetry or provenance systems that record what happened and why. Some work also uses internal activations, human-facing dashboards, and recovery or intervention mechanisms to complement external observation. A central challenge is making monitors timely and reliable across long contexts and adaptive attacks without excessive false alarms, cost, or loss of useful autonomy.

Latest papers

285 papers51 papers in the last four weeks, up 55% on the four weeks before. 0.3% of all new papers.

Tracking the Behavioral Trajectories of Adapting Agents
Jun 1, 2026Jonah Leshin, Manish Shah, Ian TimmisAgent Evaluation AI Agent Monitoring
Project VAIL, San Francisco, CA, USA
Monitoring Agentic Systems Before They're Reliable
Jun 1, 2026Marisa Ferrara Boston, Glen Hanson, Effi Georgala +2Agent Failure Analysis Agent Reliability
Reins AI USA · Veraitech USA
POIROT: Interrogating Agents for Failure Detection in Multi-Agent Systems
Jun 1, 2026Iñaki Dellibarda Varela, R. Sendra-Arranz, Pablo Romero-Sorozabal +5Agent Failure Analysis AI Agent Reliability
Center for Automation and Robotics, Spanish National Research Council (CSIC-UPM), Madrid, Spain. · ETSI Telecomunicación, Universidad Politécnica de Madrid (UPM), Madrid, Spain.
Agent System Operations: Categorization, Challenges, and Future Directions
Jun 1, 2026Zexin Wang, Changhua Pei, Yuanhao Liu +10Agent Failure Analysis AI Agent Monitoring
CNIC, CAS · Hangzhou Institute for Advanced Study, UCAS · Singapore Management University +1
From Confident Closing to Silent Failure: Characterizing False Success in LLM Agents
Jun 1, 2026Laksh AdvaniAgent Failure Analysis AI Agent Reliability
University of Colorado
Agent Operating Systems (AOS): Integrating Agentic Control Planes into, and Beyond, Traditional Operating Systems
Jun 1, 2026Ankur Sharma, Deep ShahAI Agent Security AI Agent Monitoring
Independent Researcher San Francisco, USA
Early Diagnosis of Wasted Computation in Multi-Agent LLM Systems via Failure-Aware Observability
May 31, 2026Xianyou Li, Weiran Yan, Yichao Wu +4LLM Inference Efficiency Multi-Agent LLM Systems
New York University · New York, NY 10012, USA · Independent Researcher +7
BraveGuard: From Open-World Threats to Safer Computer-Use Agents
May 31, 2026Yunhao Feng, Xiaohu Du, Xinhao Deng +13Computer-Use Agent Safety LLM Guardrails
1Fudan University · Ant Group · 8ShanghaiUniversity +5
Stateful Online Monitoring Catches Distributed Agent Attacks
May 29, 2026Davis Brown, Samarth Bhargav, Arav Santhanam +7LLM Agent Security AI Agent Monitoring
University of Pennsylvania · 2Carnegie Mellon University
Syll: Open-Source Personal Automation with Cross-Surface Execution
May 28, 2026Bo Zhang, Borui Zhang, Chenghao Jiang +5LLM Agent Skill Learning Human-in-the-Loop AI
Tsinghua University · GigaAI
Dissociative Identity: Language Model Agents Lack Grounding for Reputation Mechanisms
May 28, 2026Botao Amber Hu, Helena Rong, Max Van KleekAI Agent Monitoring AI Agent Governance
University of Oxford, UK · New York University Shanghai, China
Training Deliberative Monitors for Black-Box Scheming Detection
May 28, 2026Aditya Sinha, Akshat Naik, Victor Gillioz +5AI Agent Safety AI Agent Monitoring
1Independent · 2MATS Research† · 4Apollo Research +1
OpenClawBench: Benchmarking Process-side Anomalies in Real-world Agent Execution Trajectories
May 28, 2026Yibing Liu, Yangze Liu, Xiaolong Yin +4Agent Failure Analysis Anomaly Localization
School of Software, Shandong University, Jinan, China · School of Artificial Intelligence, Nanjing University; State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing 210023, China · Center for Medical Artificial Intelligence; Qingdao Academy of Chinese Medical Sciences; Institute of Marine Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine,2026 Qingdao 266112, China +1
The Best-Laid SCHEMEs: Coordinated Sabotage and Monitoring in Multi-Agent Systems
May 27, 2026Nikolay Radev, Lennart Haas, Benjamin Arnav +1AI Agent Security Benchmarks AI Agent Monitoring
The Importance of Out-of-Band Metadata for Safe Autonomous Agents: The Redpanda Agentic Data Plane
May 27, 2026Tyler Akidau, Tyler Rockwood, Johannes Brüderl +1AI Agent Security AI Agent Safety
Redpanda · USA / Germany
FinHarness: An Inline Lifecycle Safety Harness for Finance LLM Agents
May 26, 2026Haoxuan Jia, Yang Liu, Bin Chong +10AI Agent Reliability LLM Guardrails
Nanyang Technological University · Tsinghua University · Peking University +8
Benchmarks are Not Enough: RAMP for Runtime Assessing of Agentic Models in Production Systems
May 26, 2026Yipeng Ouyang, Xin Huang, Bingjie Liu +3Software Engineering Agents LLM Agent Evaluation
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
Retrying vs Resampling in AI Control
May 25, 2026James Lucassen, Adam KaufmanAI Agent Safety AI Agent Monitoring
Redwood Research
Agent-ToM: Learning to Monitor Autonomous LLM Agents via Theory-of-Mind Reasoning
May 22, 2026Nesreen K. Ahmed, Nima NafisiLLM Agent Security AI Agent Monitoring
Cisco Outshift
Causal Past Logic for Runtime Verification of Distributed LLM Agent Workflows
May 20, 2026Benedikt BolligLLM Agent Orchestration Agentic Workflows
Universit´e Paris-Saclay, CNRS, ENS Paris-Saclay, LMF, Gif-sur-Yvette, France
An Application-Layer Multi-Modal Covert-Channel Reference Monitor for LLM Agent Egress
May 20, 2026Alfredo MetereLLM Agent Security AI Agent Monitoring
Enclawed, LLC, California, USA
CASPIAN: Online Detection and Attribution of Cascade Attacks in LLM Multi-Agent Systems via Cross-Channel Causal Monitoring
May 19, 2026Kavana Venkatesh, Jafar Isbarov, Saad Amin +2Causal Attribution AI Agent Monitoring
Virginia Tech, Blacksburg, VA
Surviving the Unseen: Predictive Defense for Novel Multi-Turn Multimodal Attacks
May 18, 2026Doohee YouAdversarial Attacks on VLMs Multimodal Robustness
Trust and Safety, Google
SLEIGHT-Bench: A Benchmark of Evasion Attacks Against Agent Monitors
May 15, 2026Elle Najt, Colin Toft, Tyler Tracy +2Software Engineering Agents Adversarial Attacks
1Anthropic Fellows Program · University of Waterloo · 3Redwood Research +1
Formal Methods Meet LLMs: Auditing, Monitoring, and Intervention for Compliance of Advanced AI Systems
May 15, 2026Parand A. Alamdari, Toryn Q. Klassen, Sheila A. McIlraithLLM Auditing Formal Verification
University of Toronto, Vector Institute, Canada
Who Owns This Agent? Tracing AI Agents Back to Their Owners
May 15, 2026Ruben Chocron, Doron Jonathan Ben Chayim, Eyal Lenga +3AI Accountability LLM Agent Security
Ben-Gurion University of the Negev Beer-Sheva, Israel · Center for Cybersecurity Systems & Networks, Amrita Vishwa Vidyapeetham Amritapuri, India · Northeastern University Boston, Massachusetts, USA
Ensemble Monitoring for AI Control: Diverse Signals Outweigh More Compute
May 14, 2026Eugene Koran, Yejun Yun, Samantha Tetef +2AI Agent Monitoring AI Control
SPAR · Yale University
AI Knows When It's Being Watched: Functional Strategic Action and Contextual Register Modulation in Large Language Models
May 14, 2026Vinicius Covas, Jorge Alberto Hidalgo ToledoLLM Auditing AI Agent Monitoring
Center for Applied Communication Research (CICA)
Trustworthy Multi-Agent Systems: Mitigating Semantic Drift with the Argent Signaling Protocol
May 14, 2026Anantha SharmaAI Agent Reliability Multi-Agent LLM Systems
Synechron Inc · Charlotte, NC, USA
In-IDE Toolkit for Developers of AI-Based Features
May 14, 2026Yaroslav Sokolov, Yury Khudyakov, Lenar Sharipov +3AI Agent Monitoring Automated Evaluation
JetBrains Berlin, Germany · JetBrains Munich, Germany · JetBrains Amsterdam, Netherlands +1

Jun 1, 2026J/K move · Enter open · S save

Tracking the Behavioral Trajectories of Adapting Agents

Jonah Leshin, Manish Shah, Ian Timmis

Project VAIL, San Francisco, CA, USA

Agent Evaluation AI Agent Monitoring

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Text files such as skill files, memory files, and behavioral configuration files play a central role in defining how modern agents act. Through edits by humans or the agents themselves, these files may evolve over time, directly steering the agent's behavior in future interactions. We present a methodology and framework for measuring agent

traits

by defining traits as directions in the embedding space of a text embedding model. We train a linear model on labeled "before" versus "after" skill file diffs to learn a trait vector, then score arbitrary skill edits by projecting their embedding diffs onto this vector. Evaluated on 68 labeled skill diff pairs for the trait of propensity to seek sensitive data, our method achieves 91.2% sign classification accuracy and a Spearman rank correlation of

ρ= 0.82

under leave-one-out cross-validation. We build this trait evaluation into a broader agent-to-agent protocol that enables one agent to evaluate another's skill file updates through a trusted intermediary.