← 研踪 PaperTrailarXiv:2605.13625 · v2
How to Interpret Agent Behavior
首次提交 2026-05-13 22:52
版本更新 2026-11-09 10:22
雷达观测 2026-11-10 13:18
摘要
Autonomous agents such as Claude Code and Codex now operate for hours or even days. Understanding their runtime behavior has become critical for downstream tasks such as diagnosing inefficiencies, fixing bugs, and ensuring better oversight. A primary way to gain this understanding is analyzing the reasoning trajectories and execution traces these agents generate. Yet such data remains in unstructured natural-language form, making it difficult for humans to interpret at scale. We introduce Act*ONOMY, a taxonomy for describing and analyzing agent behavior at runtime. Act*ONOMY has two components: (1) the taxonomy itself, developed through Grounded Theory and structured as a three-level hierarchy of 10 actions, 46 subactions, and 120 leaf categories; and (2) an open repository that hosts the living taxonomy, provides an automated analysis pipeline that applies it to agent trajectory analysis, and defines an extension protocol for customization and growth. Our experiments show that Act*ONOMY can be used to compare behavioral profiles across agents and characterize a single agent's behavior across diverse trajectories, surfacing patterns indicative of failure modes. By providing a shared vocabulary, Act*ONOMY aims to help agent researchers, designers, and deployment engineers interpret agent behavior more effectively and consistently, supporting further study of oversight and control.
作者备注：The Fortieth Annual Conference on Neural Information Processing Systems (NeurIPS2026)
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v2 · 2026-11-09 10:22 · 最新观测版本
How to Interpret Agent Behavior
Autonomous agents such as Claude Code and Codex now operate for hours or even days. Understanding their runtime behavior has become critical for downstream tasks such as diagnosing inefficiencies, fixing bugs, and ensuring better oversight. A primary way to gain this understanding is analyzing the reasoning trajectories and execution traces these agents generate. Yet such data remains in unstructured natural-language form, making it difficult for humans to interpret at scale. We introduce Act*ONOMY, a taxonomy for describing and analyzing agent behavior at runtime. Act*ONOMY has two components: (1) the taxonomy itself, developed through Grounded Theory and structured as a three-level hierarchy of 10 actions, 46 subactions, and 120 leaf categories; and (2) an open repository that hosts the living taxonomy, provides an automated analysis pipeline that applies it to agent trajectory analysis, and defines an extension protocol for customization and growth. Our experiments show that Act*ONOMY can be used to compare behavioral profiles across agents and characterize a single agent's behavior across diverse trajectories, surfacing patterns indicative of failure modes. By providing a shared vocabulary, Act*ONOMY aims to help agent researchers, designers, and deployment engineers interpret agent behavior more effectively and consistently, supporting further study of oversight and control.
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