📄 中文摘要
使用基于 CLI 的 AI 代理（如 Claude Code、Aider 或 AutoGPT）时，用户常常会面临终端日志的困扰。运行命令后，终端会滚动显示大量文本，其中包含代理做出的决策。这些决策可能是合理的，也可能是导致 API 费用浪费的递归循环。传统的日志记录方式适用于线性软件，但自主代理的行为是非线性的，具有分支、回溯和幻觉特性。通过线性日志来理解这些复杂的逻辑路径就像通过条形码来理解三维地图。因此，开发者需要从可视化转向可观察性，以更好地理解和调试 AI 代理的工作流。
为什么你的 AI 代理日志在欺骗你：调试 LLM 工作流的更好方法
📄 English Summary
Why Your AI Agent's Log is Lying to You: A Better Way to Debug LLM Workflows
Using CLI-based AI agents like Claude Code, Aider, or AutoGPT often leads users to struggle with terminal logs. After running a command, the terminal scrolls with extensive text, containing decisions made by the agent. These decisions can either be sound or lead to recursive loops that waste API credits. Traditional logging methods are designed for linear software, while autonomous agents exhibit non-linear behaviors, including branching, backtracking, and hallucination. Attempting to understand complex logic paths through linear logs is akin to interpreting a 3D map via a barcode. Therefore, developers need to pivot from visualization to observability to better comprehend and debug AI agent workflows.