Optimizing agent system prompts with Amazon Bedrock AgentCore
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Amazon Bedrock AgentCore introduces a new optimization capability that automates the improvement of agent system prompts using production traces and reward signals. This workflow allows developers to propose configuration changes, validate them through offline batch evaluation, and test them via online A/B testing before promotion. The system features two reflector designs: the Single Agent Reflector, which offers high efficiency and speed, and the experimental Sub-Agent Reflector, which uses a swarm of agents to achieve higher quality by analyzing traces independently. Both methods are protected by guardrails that prevent prompt bloat, safety violations, and overfitting to trace data. Evaluation results on AppWorld and WebShop benchmarks show that the Single Agent Reflector provides the best quality-per-cost trade-off, while the Sub-Agent Reflector achieves the highest success rates. AWS recommends starting with small trace sets and incorporating diverse feedback signals like free-text rationale for better reflection. These tools aim to streamline the manual process of tuning agent components such as prompts, tool descriptions, and skills.
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