LangChain 分享长时 AI Agent 评估技巧：拆解任务简化评估
做长时 AI Agent 评估的开发者终于有了实用技巧——拆解任务后评估效率大幅提升，建议直接参考这个流程优化你的评估策略。
LangChain 的 Palash Shah 分享了一种针对长时运行 AI Agent 的评估方法。核心思路是将复杂的评估任务拆解成更小、更易处理的子任务，这样不仅便于人类理解，也更容易让 LLM 自身进行评估。他举例说明，对于运行超过 30 分钟的 Agent，通过从追踪中提取推理过程，找出特定行为的根本原因，然后重建简化版的评估场景。这种方法可以快速测试提示词调整的效果，而无需每次都运行完整的长时间评估。
Breaking down the eval task into easy to process chunks not only makes it easy for us humans to understand, but also easier for LLMs to eval: Palash Shah @palashshah you can condense long horizon evals with agents into smaller subsets that still let you test intended behavior. i'm currently evaluating an agent that runs for 30+ minutes, and analyzes thousands of traces at a time. here's my process: if you're evaluating whether X impacts Y agent output, oftentimes a lot of the information that exists in X isn't relevant to the decision of Y. i extract the reasoning out of trace, and then figure out what is the cause of a specific behavior. then, i know what situation i need to re-create when setting up my eval. and as a result, i can create a much smaller/simpler version of the long horizon eval that i can quickly use to figure out what i need to change in my prompts to get the behavior i want. 🔗 View Quoted Tweet 💬 3 🔄 1 ❤️ 7 👀 961 📊 3 ⚡