Amazon Bedrock AgentCore provides insights to detect silent agent failures that pass system health checks but lead to customer complaints. These failures often go unnoticed as dashboards show green indicators, with 99% completion rates and zero error spikes. However, issues like incorrect outcomes or skipped approval steps surface through customer escalations, often weeks after affecting users at scale. The tool helps prioritize and address these behavioral failures without relying on error signals alone.
AgentCore transforms existing trace data into actionable behavioral intelligence, revealing broader patterns affecting agent performance beyond individual session failures. It clusters session attributes to identify ranked failure patterns with root cause analysis, helping developers distinguish systematic issues from edge cases. The tool also offers user intent analysis, showing how users interact with agents across different scenarios, and execution insights, revealing how agents respond to various requests. These capabilities provide a comprehensive behavioral map of deployed AI agents.
The source explains that Amazon Bedrock AgentCore analyzes session traces against a structured taxonomy of behavioral failure types, detecting 11 categories including hallucination and task instruction violations. It reasons about policy compliance and behavioral correctness, identifying silent failures by grouping descriptions into clusters. The system traces backward through the execution graph to pinpoint root causes, offering fix recommendations and prioritizing failures based on their impact on sessions.
Source: awsml
