AI Agent Evals实战FAQ：先发现失败、再谈评估，700+工程师踩坑后的完整方法论
For developers working on AI agents, this is a practical guide that challenges the standard evaluation workflow, offering a more grounded approach based on real-world failure analysis.
The article argues that the common approach to AI agent evaluation—building infrastructure, using LLM-as-a-Judge, and monitoring dashboards—is backwards. The authors, based on teaching 700+ engineers, propose a different path: first manually analyze 100 real trace logs to identify failure patterns, then build an evaluation system. This method suggests evaluation metrics should emerge from real failures rather than being pre-designed.
AI Agent Evals 实战 FAQ：先发现失败、再谈评估，700+ 工程师踩坑后的完整方法论 当前行业的主流叙事是：搭评估基础设施、接 LLM-as-a-Judge、看仪表盘分数。两位作者 @HamelHusain @sh_reya 基于大量一线教学与实践，认为这条路径恰恰是错的，或者说是本末倒置的。 他们的核心方法论可以压缩成一句话： 先做错误分析，再谈评估体系。 评估指标是从真实失败案例中“生长”出来的，不应该是预先设计出来的。 meng shao @shao__meng AI Agent 评估，多数团队从搭平台、接 LLM judge、看分数开始 @HamelHusain 与 @sh_reya 在教了 700+ 工程师之后给出的答案是反的：先手动读 100 条真实 trace，找到失败模式，评估器才会长出来 评估的本质是理解系统在哪里影响了用户，这一步没有自动化捷径 x.com/i/article/2101… 🔗 View Quoted Tweet 💬 1 🔄 0 ❤️ 0 👀 588 📊 2 ⚡
