Amazon Bedrock AgentCore Optimization: Full-Fledged Agent Optimization Loop Now in Preview

Amazon Bedrock AgentCore Optimization is now in preview, providing a comprehensive system to automate the continuous quality improvement of AI agents. This moves away from traditional manual improvement processes and enables a complete performance optimization loop based on production trace data, recommendation generation, batch evaluation, and A/B testing.

Limitations of Traditional Manual Improvement Processes

AI agents continuously degrade in quality in production environments due to model evolution, changes in user behavior, and prompt reuse, causing them to gradually deviate from their original design intent.

Traditional improvement processes were entirely manual, involving developers reading traces, hypothesizing, rewriting prompts, testing in a few cases, and deploying to production, only to cause new issues for other users. This cycle was often based on intuition and non-systematic approaches.

Even with dedicated science teams or large-scale benchmarks, the weekly or monthly cycle of operation did not provide timely solutions for agents degrading daily.

(Reference: Amazon Bedrock AgentCore Optimization)

Mechanism of the New Optimization Loop

AgentCore Optimization provides a complete loop of observation, evaluation, and improvement, with three major components working together.

Recommendations analyzes production traces and evaluation outputs to generate suggestions for optimizing system prompts or tool descriptions for specified evaluators. Batch evaluation tests these recommendations against pre-defined test datasets and reports aggregated scores to detect regressions in important cases. If manually created scenarios are insufficient, the Simulation feature can generate datasets where LLM-based actors play the role of end-users.

A/B testing performs controlled comparisons between agent versions through the AgentCore Gateway, splitting live production traffic at set ratios and reporting results, including confidence intervals and statistical significance.

(Reference: Amazon Bedrock AgentCore Optimization)

Actual Operational Patterns

A real operational example is shown in the model upgrade scenario, but the pattern applies to any changes, such as prompt refactoring or tool setting changes.

Recommendations propose changes, batch evaluation and A/B testing verify them, replacing manual cycles of reading traces, making speculative corrections, and blind deployments. According to Yoshiharu Okuda of NTT DATA, “The manual prompt adjustment process, which used to take several weeks, has evolved into a rapid and iterative cycle.”

By deriving improvement recommendations from production trace data and verifying their impact through A/B testing, organizations can ensure accuracy and effectiveness while optimizing performance on a large scale.

(Reference: Amazon Bedrock AgentCore Optimization)

Getting Started

Each feature of AgentCore Optimization is detailed in the AWS documentation, including setup procedures and API references for Recommendations, Batch evaluation, Simulation, and A/B testing.

Developers already using Amazon Bedrock AgentCore can access these optimization features without additional setup. New users need to start by setting up the AgentCore Gateway.

(Reference: Amazon Bedrock AgentCore Optimization)

Summary

  • Use AgentCore Optimization’s preview feature to automatically detect and correct production agent quality degradation — Move away from manual trace analysis to data-driven improvement cycles.
  • Use the Recommendations API to automatically generate optimization proposals from production traces — Achieve prompt improvement based on actual usage patterns, not developer speculation.
  • Combine batch evaluation and A/B testing to minimize regression risk — Validate new settings against known important cases and actual traffic splits before production deployment.
  • Obtain statistically significant A/B test results through the AgentCore Gateway — Quantitatively measure improvement effects with confidence intervals and make data-driven decisions.