AI Agent Failure Detection and Root Cause Analysis with Strands Evals

This article explains how to use Strands Evals detectors to automatically identify AI agent failures and their root causes, reducing diagnosis time from hours to minutes.

• Detectors automatically scan execution traces against nine failure categories including hallucination, incorrect actions, and orchestration errors
• Root cause analysis traces causal chains between failures, distinguishing primary causes from downstream symptoms
• Structured output includes failure categories, confidence scores, causal classifications, and fix recommendations categorized by type
• Integrate detectors into evaluation pipelines with DiagnosisConfig for automated diagnosis on every test run
• CloudWatchProvider fetches production traces from Amazon CloudWatch Logs for offline analysis
• Best practices include starting with MEDIUM confidence threshold and fixing PRIMARY failures first

Detectors automate the manual trace inspection workflow, enabling teams to quickly identify what failed and where to apply fixes in system prompts or tool definitions.