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TutorialsAWS Machine Learning BlogFrom the source· Rui Li·· 6 hours agoPicks

Building trust in AI agents: A production-ready evaluation framework with Amazon Bedrock AgentCore

Who
AWS Sales, Marketing, and Global Services Operations team
What
shared a nine-stage evaluation framework for production AI agents built with Amazon Bedrock AgentCore
Quote
In this post, we show how we built a continuous quality loop for production-ready agentic solutions at scale using Amazon Bedrock AgentCore.

What happened

AWS explains how its SMGS Operations team built a nine-stage evaluation framework with Amazon Bedrock AgentCore for an internal sales intelligence system. The framework combines domain-specific evaluators, versioned golden datasets, CI/CD gates, continuous monitoring, and trace-driven improvements across tools, agents, and multi-agent workflows. AWS reports that automated evaluation reduced quality regression detection from days or weeks to under four hours.

Why it matters

The framework offers a transferable way to connect domain-specific failure categories with release gates and production monitoring across tools, individual agents, and multi-agent workflows.

The story so far

AWS’s Sales, Marketing, and Global Services Operations team shared a nine-stage evaluation framework built with Amazon Bedrock AgentCore for an internal sales intelligence system, reporting that automated evaluation cut quality regression detection time from days or weeks to under four hours. The framework combines domain-specific evaluators, versioned reference datasets, checks in the software delivery pipeline, continuous monitoring, and improvements guided by execution traces. It evaluates tools, individual agents, and multi-agent workflows as part of a continuous quality improvement process. The dataset evaluation capability used in the framework is in public preview.

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