Artificial Intelligence · 31.07.2026, 15:49 UTC
Optimizing production agents with Amazon Bedrock AgentCore Observability
| Schweregrad | info |
|---|---|
| Kategorie | Artificial Intelligence |
| Quelle | AWS Machine Learning ↗ |
| Veröffentlicht | 31.07.2026 UTC |
Sicherheitsmeldung mit Schweregrad noch nicht bewertet. Technische Details im Tab „Originaltext“; empfohlene Schritte in der Checkliste.
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. Scenario 3: Performance bottlenecks Agents experience performance bottlenecks when they work correctly but respond too slowly. You expect sub-second responses but experience multi-second delays. Agents complete tasks successfully, but the latency makes them impractical for interactive use cases. This …