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Amazon Bedrock AgentCore introduces system-prompt optimizer to automate agent tuning

Amazon’s Bedrock AgentCore now includes a system prompt optimizer that analyzes production agent traces and a reward signal to propose and validate configuration edits. The technical post describes a reflector-based workflow (Single Agent Reflector and an experimental open-source Sub‑Agent Reflector), integration with AgentCore Observability, offline batch evaluation and online A/B testing, guardrails for promotion, and evaluation results on GEPA and MIPROv2 benchmarks.

KEY POINTS

  1. Amazon’s Bedrock AgentCore now includes a system prompt optimizer that analyzes production agent traces and a reward signal to propose and validate configuration edits.
  2. The technical post describes a reflector-based workflow (Single Agent Reflector and an experimental open-source Sub‑Agent Reflector), integration with AgentCore Observability, offline batch evaluation and online A/B testing, guardrails for promotion, and evaluation results on GEPA and MIPROv2 benchmarks.
  3. Automating prompt and configuration tuning from production traces can speed up improving agent quality and make iterative testing (including A/B on live traffic) more systematic while retaining reviewable guardrails.

WHY IT MATTERS

Automating prompt and configuration tuning from production traces can speed up improving agent quality and make iterative testing (including A/B on live traffic) more systematic while retaining reviewable guardrails.

SOURCES & TIMELINE

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