Debugging production agents with Amazon Bedrock AgentCore Observability

Source Domain: aws.amazon.com

Problem Statement: Production AI agents can fail silently without triggering error alerts, making debugging issues difficult since standard logs and metrics often do not capture decision-making processes.
Amazon Bedrock AgentCore Observability: This feature provides visibility into execution across metrics, traces, and structured logs, enabling more effective debugging by tracing reasoning steps, tool invocations, and pinpointing execution issues.
Types of Failure Patterns: Production AI agent failures can fall into three main categories: quality failures (incorrect answers), reliability issues (uncompleted workflows), and efficiency problems (high latency or excessive cost).
Monitoring Tools: Use CloudWatch dashboards to monitor performance metrics like session volume, latency, token usage, and error rates. CloudWatch Logs Insights allows real-time analysis of structured log data to identify and diagnose issues.
Troubleshooting Workflows: Steps for diagnosing and resolving common issues such as infinite loops and tool invocation failures include analyzing logs for patterns, tweaking prompts, ensuring proper tool selection, and configuring role permissions.