Building trust in AI agents: A production-ready evaluation framework with Amazon Bedrock AgentCore
How-To How to actually use this
What changed: Amazon Bedrock AgentCore now includes a nine-stage evaluation framework to detect silent quality regressions in AI agents across development and production.
How to use it:
- Integrate the framework into your agent development pipeline using Amazon Bedrock AgentCore.
- Define quality benchmarks for each stage, from code commit to deployment.
- Run automated evaluations after every code change to monitor answer accuracy.
- Track latency and response quality side-by-side to catch silent degradation.
- Set alerts for quality drops even when latency remains stable.
- Use framework outputs to roll back or fix agents before user impact.
- Repeat evaluations continuously in production to maintain trust.
Good for: teams deploying AI agents who need to prevent silent quality failures.
Autonomous AI agents fail softly: latency stays flat while answer quality quietly erodes. This post shares a nine-stage evaluation framework, built on Amazon Bedrock AgentCore, that catches silent quality regressions across the full agent lifecycle, from first commit to live production.
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