Evaluating AI Agents: A production blueprint with Strands and AgentCore
AWS and Motorway cut AI agent error rates from 1-in-8 to 1-in-50 with a production eval pipeline.
“The agent gives a confident-sounding response, but how do you prove it works reliably with real money on the line?”
Motorway partnered with AWS to build an end-to-end evaluation pipeline for their AI dealer stock search agent, reducing query error rates from 12.5% to 2% and shrinking issue detection time from hours to minutes. The blueprint combines Strands Agents SDK with Amazon Bedrock AgentCore and introduces a three-layer evaluation framework (tool usage, reasoning, output quality) plus a pass^k consistency metric. The companion open-source repository makes this a reusable production template for any organization deploying AI agents at scale.