Optimizing agent system prompts with Amazon Bedrock AgentCore | Amazon Web Services
In a previous launch post, we introduced AgentCore optimization, a capability of Amazon Bedrock AgentCore that can help you improve the quality of your agents. Improving a low-scoring agent has traditionally been a manual process. You review long traces to find where the agent goes wrong, tune individual components such as prompts, tool descriptions, and skills, and rerun evaluations to check for improvement.
With AgentCore optimization, you can use production traces to propose configuration changes, validate them through offline batch evaluation and online A/B testing on live traffic, and promote the winners. AgentCore Observability, a capability of Amazon Bedrock AgentCore, provides visibility into agent behavior, and evaluations provide signals about agent quality. Together, recommendations, configuration bundles, and validation through A/B testing provide a workflow for improving an agent.
The system prompt optimizer in AgentCore uses agent traces recorded in AgentCore Observability together with a reward signal to...
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