用熵值分析AI智能体行为内部状态，揭示探索与决策多样性。
Entropy-Based Observability for AI Agent Behavior
- 通过熵值量化智能体行为的探索程度与动作选择多样性
- 能捕捉工具使用集中度和不确定性降低过程等内在特征
- 适合研究智能体稳定性与可解释性的研究人员
AI智能体通常通过任务成功率、奖励、延迟和成本等结果导向指标进行监控。尽管这些指标在运营上重要，但对智能体行为的内部结构（如探索程度、动作选择的僵化或多样性、工具使用的集中度、运行过程中不确定性的减少以及重复执行下的行为稳定性）缺乏可见性。本文提出基于熵的智能体行为可观测性框架（EOA），一种从智能体轨迹中提取行为遥测数据的轻量级方法。
原文摘要 · Abstract (English)
AI agents are typically instrumented through outcome-oriented indicators such as task success, reward, latency, and cost.Although these indicators are operationally important, they provide limited visibility into the internal structure of agent behavior such as the degree of exploration, the rigidity or diversity of action selection, the concentration of tool use, the reduction of uncertainty across a run, and the stability of behavior across repeated executions.This paper proposes Entropy-Based Observability for AI Agents (EOA), a lightweight framework for deriving behavioral telemetry from agent traces.
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