Optimizing agent system prompts with Amazon Bedrock AgentCore
What Changed
Amazon Bedrock AgentCore now includes a system prompt optimizer that uses production traces and reward signals to propose improved system prompts for agents. The optimizer works by having a reflector agent analyze evaluated traces stored in a filesystem, generate configuration edits, and then guardrails review the changes before promotion. The Single Agent Reflector processes all traces in one pass, while the Sub-Agent Reflector uses multiple agents to explore trace subsets for higher quality results.
Why It Matters
Enterprise architects can reduce manual tuning of agent prompts, lowering operational costs and speeding deployment cycles. The guardrail and A/B testing workflow ensures changes are validated, improving reliability and governance of AI services. However, the approach requires sufficient trace data and may increase storage and compute overhead.
The Limitation
The optimizer’s effectiveness depends on the volume and quality of traces; sparse or noisy data may yield suboptimal recommendations.
What You Can Do
Implement AgentCore optimization by collecting production traces, running the Single Agent Reflector for an initial epoch, and reviewing the recommended system prompt before A/B testing.