Introducing the agent quality loop: AgentCore Optimization now in preview
Amazon AWS AI Blog / 5/5/2026
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Key Points
- Amazon Bedrock AgentCore introduces an agent quality loop to help teams continuously maintain and improve AI agent performance in production.
- The loop generates optimization recommendations from production traces and evaluation outputs, targeting improvements to system prompts and tool descriptions for a specified evaluator.
- New validation options include batch evaluation against a predefined dataset to catch regressions and LLM-backed simulation to create datasets when hand-authored scenarios aren’t sufficient.
- AgentCore also adds A/B testing to compare agent versions in a controlled manner before shipping, helping teams release fixes with greater confidence.
- The announcement addresses the common problem that manual, intuition-driven debugging cycles don’t keep up with daily agent drift as models and user behavior change.
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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