Introducing the agent performance loop: AgentCore Optimization now in preview
Amazon AWS AI Blog / 5/5/2026
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Key Points
- Amazon Bedrock AgentCore is adding an agent performance loop capability that targets quality drift over time by using production traces and systematic validation.
- The platform will generate optimization recommendations by analyzing production traces and evaluation outputs, focusing on improving system prompts or tool descriptions for a specified evaluator.
- Teams can validate recommendations using batch evaluation against a predefined dataset to catch regressions on known-important cases.
- AgentCore also supports two additional validation paths: simulating a dataset with an LLM-based actor and running A/B testing for controlled comparisons between agent versions.
- The preview is positioned as a way to reduce reliance on manual, intuition-driven trace debugging and to enable faster, data-backed iteration for agent teams.
Generate recommendations from production traces, validate them with batch evaluation and A/B testing, and ship with confidence. AI agents that perform well at launch don’t stay that way. As models evolve, user behavior shifts, and prompts get reused in new contexts they were never designed for. Agent quality quietly degrades. In most teams, the improvement […]
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