Evaluating AI Agents: A production blueprint with Strands and AgentCore
AWS Machine Learning Amit Deol
Motorway and AWS built an evaluation pipeline for an AI agent that helps car dealers search vehicle inventory using natural language queries, reducing incorrect results from 1 in 8 to 1 in 50. The pipeline combines the Strands Agents SDK with Amazon Bedrock AgentCore and uses a three-layer framework assessing tool usage (>95% pass threshold), reasoning (>85%), and output quality (>90%), plus a five-stage deployment process with quality gates. Organizations can now catch agent failures before production and during runtime, with test case management and multi-trial evaluation metrics ensuring consistent behavior across repeated interactions.
Why it matters
Together, Motorway and AWS built an end-to-end evaluation pipeline that reduced incorrect results from 1 in 8 queries to 1 in 50 and cut issue detection time from few hours to few minutes. The pipeline combines the Strands Agents SDK with Amazon Bedrock AgentCore, a fully managed service for deploying and operating AI agents at scale. In this post, you will learn how to build this pipeline for your own agents.
