Amazon Bedrock AgentCore: Detecting Hidden AI Agent Failures

Amazon Bedrock introduced AgentCore optimization, designed to detect behavioral failures in AI agents that are invisible to standard monitoring: the system shows 99% success, zero latency, and no errors, but customers complain about incorrect results (e.g., an unexecuted order change or a missed approval step). AgentCore analyzes session traces collected via CloudWatch and structures failures according to a taxonomy of 11 categories (hallucinations, incorrect actions, instruction violations, orchestration errors, etc.), identifying even those that do not generate error signals. The system clusters failures across hundreds of sessions, determines the root cause (e.g., fabricating financial data without calling tools), and ranks clusters by the proportion of affected sessions, so developers can immediately see which problem affects 30% of traffic and which affects only three sessions. AgentCore also clusters user queries (showing how much traffic falls into intended, partially supported, and unexpected scenarios) and extracts executable summaries for each session, highlighting discrepancies between expected and actual agent behavior. Configuration includes selecting analysis types (failures, intents, execution), specifying the trace source (agent from AgentCore or directly from CloudWatch), and scheduling (one-time or recurring). Example: a market trends agent in 1 out of 10 sessions hallucinated financial data without using tools; the fix was to strengthen the system prompt. The insights shift the observability model from reactive trace viewing to proactive pattern detection, allowing the most critical issues to be fixed first.