As your AI agents move from prototype to production, the challenges shift from getting them to work to keeping them fast and efficient. In Part 1 of this series, we walked through debugging two common agent failures: infinite loops and tool invocation errors. Those scenarios dealt with agents that were broken. In this post, we tackle a different challenge: agents that work correctly but perform poorly. Slow response times and unbounded memory growth are the most common operational issues that surface after you resolve the initial debugging problems. They don’t trigger error alerts, but they erode user trust and increase costs over time.

Using AgentCore Observability, a capability of Amazon Bedrock AgentCore, and Amazon CloudWatch, you will learn how to identify performance bottlenecks across your agent’s execution path and diagnose memory issues in long-running sessions. You will also implement monitoring practices that catch degradation before users notice it. For additional information and best practices, review AgentCore Evaluations, a capability of Amazon Bedrock AgentCore, and AgentCore Insights.

Prerequisites

You need an AWS account with Amazon Bedrock AgentCore access, CloudWatch Transaction Search enabled, and a deployed agent. See Part 1 for full setup details.