Amazon Bedrock AgentCore: Detection of Hidden AI Agent Failures

Amazon Bedrock AgentCore optimization addresses a common problem in operating AI agents at scale: behavioral failures that complete without errors but produce incorrect outcomes, such as an order modification never executed or a product reported as in stock after an API timeout. Traditional dashboards show green metrics despite these issues, which surface through customer escalations. AgentCore analyzes session traces to detect 11 failure categories, including hallucination, incorrect actions, and orchestration errors, grouping them into clusters ranked by affected sessions to distinguish systematic issues from edge cases. It also provides user intent analysis via request clustering and execution summaries showing how agents respond versus intended design. Users can set up recurring or one-time reports via the AgentCore console, selecting failure analysis, user intent analysis, or execution summary, and connect agent endpoints or CloudWatch log groups. In a demo with a market trends agent, failure analysis identified a hallucination case where the agent fabricated financial data without tool invocation; user intent analysis showed 50% of traffic was profile and portfolio retrieval; execution summaries highlighted strategy divergence. This shifts observability from reactive trace inspection to proactive pattern detection.