AWS built a production eval framework on Bedrock AgentCore: agents fail silently
AWS ML Blog · rss · 2026-10-17
AWS's SMGS Operations team shares how it built a full-lifecycle evaluation framework on Amazon Bedrock AgentCore Evaluations for a multi-agent system serving AWS field sales worldwide (Performance Insights, Customer Insights, and text-to-SQL Data Insights agents, with role-scoped security).
The core problem: agents fail silently — latency and error rates stay green while answers quietly go wrong. Pre-launch evals caught real errors: $410.08 of revenue reported as "$410.08 Million," mislabeled revenue metrics, a hallucinated ARR estimate, and model calculations passed off as sourced facts.
Coordinated releases add risk: when data, tool, and prompt changes land out of sync, one pre-production test's pass rate dropped from 78% to 38%.
Key patterns: CI/CD regression gates, canary scoring, drift detection, automated feedback loops; evals span five dimensions (safety, quality, performance, cost, operations), backed by Bedrock Guardrails, CloudWatch, S3/Redshift, Amazon Quick, and Lambda-based custom evaluators.
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