AI Agent 犯错后，终于有人做了个“事后复盘”工具
AI Agent 犯错时，你看到的是最后一步崩了，但真正的问题可能藏在前面好几步。现有工具只能回放，没法帮你找到根因。这篇开源了一个调试框架，核心是让 AI 自己当侦探：先全局看一遍整个执行轨迹，再按结构引导去怀疑关键步骤，最后交叉验证。在测试中，它找错归因的准确率比单次扫描高 7 个百分点；修复能力上，一次重跑就能修好 13/73 个失败任务，而其他方法只能修 4-6 个。它不是你明天就能直接用的，但如果你在搭复杂 Agent 流程、被“幽灵错误”折磨过，这个方向值得关注。
📄 原文摘要(英文)
LLM agent failures are difficult to debug because the step where an error surfaces is often not the one that caused it. Existing observability tools replay execution traces but provide little support for identifying the root cause or translating diagnosis into recovery. We present AgentDebugX, an open-source debugging framework that organizes debugging as a closed loop of Detect, Attribute, Recover, and Rerun. At its core, DeepDebug performs multi-turn root-cause diagnosis through global trajectory understanding, structure-guided investigation, and cross-examination. On the Who and When benchmark, DeepDebug achieves the best strict attribution accuracy among the evaluated methods on both tested open-weight backbones, reaching 28.8 percent exact agent-and-step accuracy on qwen3.5-9b versus 21.7 percent for the strongest single-pass baseline. On GAIA, DeepDebug repairs 13 of 73 failed tasks in a single rerun, compared with 4 to 6 for three decoupled self-correction baselines, improving overall accuracy from 55.8 percent to 63.6 percent. AgentDebugX exposes this workflow through a Python library, CLI, web console, and installable agentic skill, and provides an opt-in Error Hub for sharing scrubbed failure-diagnosis-repair bundles and reusing them as debugging memory.