AWS Bedrock AgentCore spots hidden AI agent failures customers never report

Amazon Bedrock AgentCore optimization introduces a new detection layer for AI agent failures that pass infrastructure health checks but still deliver incorrect outcomes to customers. The core problem it solves is that behavioral failures—an order modification never executed, a product wrongly reported as in stock when an API timed out, an approval step skipped—complete successfully from the system's perspective. They generate no error spikes, pass health checks, and only surface through customer escalations, often weeks after beginning to affect users at scale. Even when failures do produce error signals, distinguishing a systematic issue affecting 30% of traffic from an edge case affecting three sessions requires manually reviewing hundreds of traces.

AgentCore optimization shifts observability from reactive trace inspection to proactive pattern detection. The system analyzes each session trace against a structured taxonomy of 11 behavioral failure categories, including hallucination, incorrect actions, task instruction violations, orchestration errors, and context handling issues. For each detected failure, the system performs root cause analysis by tracing backward through the execution graph—a sequence of inference calls, tool executions, and sub-agent invocations—and pruning branches unrelated to the failure. This makes root cause analysis tractable even on long workflows; a 50-step session is narrowed to the specific execution path that caused the failure. The output identifies a root cause location (a span ID in logs), a causality classification, and a fix recommendation (system prompt change, tool description update, or infrastructure work). Failures are then clustered and ranked by the number of affected sessions relative to total sessions analyzed. A top-level cluster might show "Agent Bypasses Information Gathering" affecting 116 sessions, with 114 of those traced to a single sub-pattern ("Skipped Prerequisite Information Retrieval") and only 2 unrelated edge cases—immediately identifying the single fix that addresses the largest share of failures.

Beyond failure detection, AgentCore surfaces two additional behavioral views. User intent analysis embeds and clusters the actual prompts users send, revealing the distribution of use cases in production. A team might discover that 40% of traffic matches their intended design, 30% is partially supported, and the remaining 30% is unanticipated. Execution summary extracts the approach the agent took and the outcome it reached for each session using hierarchical summarization to handle varying session lengths, then clusters these summaries to reveal common strategies, how they correlate with success or failure, and where actual behavior diverges from intended design. Together, these provide a behavioral map of the agent in production that goes beyond error detection into understanding how the system is actually being used and how it responds.

Setup occurs through the AgentCore console under Optimizations > Insights > Create Insights. Users select which analysis types to run (failure analysis, user intent analysis, execution summary, or any combination), choose whether to analyze an agent deployed through AgentCore runtime (which auto-selects the associated CloudWatch log group) or point directly to a CloudWatch log group for agents running elsewhere, and configure a schedule (recurring on daily, weekly, or monthly cadence, or one-time for a specific time range). Optional filters and sampling allow scoping analysis to specific session criteria or traffic subsets. In a real example, a market trends agent analyzing 10 sessions identified one failure category—"Hallucinated Financial Data Without Tool Invocation"—affecting 1 out of 10 sessions. The agent had generated specific financial data points and market rates as facts without invoking available data retrieval tools, violating the system prompt directive to retrieve actual data before making claims. The root cause analysis traced this to insufficient enforcement mechanisms in the system prompt, producing a clear fix: strengthen the prompt to mandate tool invocation before numerical claims. This failure produced no error and the session completed successfully; only insights revealed the behavioral violation.

AI agents in production often exhibit failures that defy traditional observability: the system completes the request, health metrics show green, error rates remain nominal, and yet customers report wrong outcomes. These behavioral failures — an order never executed, inventory wrongly reported, approval steps skipped — look indistinguishable from success to standard monitoring. The challenge deepens at scale: when an agent serving thousands of daily sessions accumulates hundreds of errors, individual trace inspection reveals single-session facts but cannot answer whether a pattern affects 30% of traffic or only three edge cases.

AgentCore optimization addresses this gap by operating one layer above existing observability tools. Rather than relying on error signals or spot-checking traces, it consumes trace data already being collected and transforms it into behavioral intelligence. The system analyzes each session against a structured taxonomy of 11 failure types, including hallucination and orchestration errors, and then performs root cause analysis by reasoning backward through the execution graph of each session—pruning branches unrelated to failures to make causality tractable. The result is ranked failure clusters that surface how many sessions a given problem affects, paired with specific root cause explanations (system prompt change, tool description update, or infrastructure work) and natural language descriptions of what went wrong.

Beyond failure detection, the tool also surfaces user intent distribution—showing teams what users actually ask their agent to do, which may diverge sharply from intended use—and execution summaries that reveal the strategies the agent employs and where real behavior diverges from design. Teams can configure insights to run daily, weekly, or monthly on either an AgentCore-deployed agent or any CloudWatch log group, either on a recurring schedule or one-time for post-deployment review or complaint investigation.

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