Optimizing production agents with Amazon Bedrock AgentCore Observability

This article demonstrates how to use Amazon Bedrock AgentCore Observability and CloudWatch to identify and resolve performance bottlenecks and memory issues in production AI agents.

• Identify high-latency requests using CloudWatch queries to find agents exceeding performance budgets
• Analyze request timelines to locate bottlenecks in tool execution, memory retrieval, and token generation
• Optimize slow tools through caching, connection pooling, and parallel execution of independent operations
• Manage unbounded memory growth in long-running sessions using consolidation strategies and namespace organization
• Implement production best practices including comprehensive instrumentation, CloudWatch alarms, and operational dashboards
• Use AgentCore Evaluators and Insights for continuous automated assessment of agent behavior and quality

Systematic observability practices enable teams to catch performance degradation early, resolve issues quickly, and build production agents that maintain user trust and cost efficiency.