AI Agent Failure Detection and Root Cause Analysis with Strands Evals | Amazon Web Services
When your AI agent fails in production, knowing that it failed is only the beginning. The harder question is why it failed and what to fix. Traditional evaluation tells you “this agent scored 60 percent on goal completion,” but leaves you manually reviewing execution traces to understand what went wrong. For teams operating agents at scale, this manual diagnosis becomes the bottleneck between detecting a problem and shipping a fix. Detectors in the Strands Evals SDK remove this bottleneck by automatically identifying failures in agent execution traces and performing root cause analysis, so you can reduce diagnosis time from hours to minutes.
In this post, we walk you through calling the detector functions to diagnose real agent failures. You learn how to interpret their structured output: categorized failures with confidence scores, causal chains linking root causes to downstream symptoms, and fix recommendations specifying whether a change belongs in your system...
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