Amazon Bedrock AgentCore launches capabilities for optimizing agent performance in preview

Amazon Web Services has launched new optimization capabilities for its Bedrock AgentCore platform in preview, introducing automated recommendations, batch evaluations, and A/B testing features designed to systematically improve AI agent performance over time. The new functionality addresses a critical gap in AI agent deployment where performance degradation occurs gradually as models evolve and user behavior shifts, requiring manual developer intervention to identify and fix issues. The recommendations capability analyzes production traces and evaluation outputs to generate optimized system prompts and tool descriptions tailored to specific workloads. Developers can then validate these recommendations through batch evaluations against predefined test cases and conduct controlled A/B tests using either test sets or live production traffic, with statistical significance reporting built into the platform. All recommendations require explicit developer approval before implementation, maintaining human oversight while automating the performance improvement cycle. The optimization features are now available in all AWS regions where AgentCore Evaluations is supported, completing what AWS describes as the "observe, evaluate, improve loop" for production AI agents. This systematic approach replaces the previous reliance on developer intuition and manual processes for maintaining agent quality in production environments.