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Signals: Continuous Diagnostics of AI Agents
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arXiv AI
1d ago
Signals: Continuous Diagnostics of AI Agents
The paper "Signals: Continuous Diagnostics of AI Agents" introduces a method for diagnosing problems in AI agents by analyzing agentic traces. It defines 45 problem types across eight families, categorizes findings into engineering issues called Signals, and assesses their severity. The method was evaluated on public agent executions, achieving a 91.43% F1 score in matching Signals to reference issues, outperforming a one-pass LLM control by about 18 percentage points.
arXiv AI
Sep 10
Explaining AI Agents Through Execution Traces
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arXiv AI
Jun 2
POIROT: Interrogating Agents for Failure Detection in Multi-Agent Systems
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arXiv AI
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Skynet: Workflow-Level Anomaly Detection for Agentic AI via Semantic and Structural Modeling
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arXiv AI
Sep 1
AgentRx: Diagnosing AI Agent Failures from Execution Trajectories
arXiv:2602.02475v2 Announce Type: replace Abstract: AI agents often fail in ways that are difficult to localize because executions are probabilistic, long-horizon, multi-agent, and mediated by noisy...
arXiv AI
Jun 8
TRACE: Trajectory Reasoning through Adaptive Cross-Step Evidence Aggregation for LLM Agents
arXiv:2606. 07054v1 Announce Type: cross Abstract: Autonomous LLM agents can pursue hidden malicious objectives through sequences of individually benign actions, making sabotage difficult to detect using standard trajectory-level monitoring.