Motorway and AWS Publish Production Blueprint for Evaluating AI Agents
Key Takeaways
- ▸Error rates dropped from 1 in 8 to 1 in 50 queries through systematic evaluation
- ▸Issue detection time reduced from hours to minutes via automated monitoring
- ▸Three-layer evaluation framework assesses tool selection, reasoning, and output quality
Summary
Motorway, a UK online car marketplace, collaborated with AWS and Strands to develop a production-ready AI agent evaluation pipeline. The solution powers a conversational dealer stock search agent that replaces hours of manual CSV filtering with natural language queries, handling up to 1,500 concurrent users during peak hours.
The evaluation pipeline combines Strands Agents SDK with Amazon Bedrock AgentCore, AWS's fully managed service for deploying AI agents at scale. The pipeline reduced incorrect search results from 1 in 8 queries to 1 in 50 and cut issue detection time from hours to minutes—critical improvements for a platform where dealers make real purchasing decisions.
AWS is releasing a deployable blueprint implementing a three-layer evaluation framework (tool usage, reasoning, output quality) and a five-stage deployment pipeline with automated quality gates. The approach combines build-time testing via the open-source strands-agents-evals library with production monitoring through Bedrock AgentCore Evaluations, using the pass^k metric to account for non-deterministic agent outputs.
- Five-stage deployment pipeline with quality gates prevents releases when metrics degrade
- Deployable blueprint available; core principles are system-agnostic, not AWS-specific
- Strands Agents SDK paired with Amazon Bedrock AgentCore for production-scale operations
Editorial Opinion
As AI agents move into high-stakes domains like financial transactions, systematic evaluation is no longer optional—it's essential. This production blueprint fills a critical gap: most agent deployments lack rigorous evaluation pipelines, leaving teams to discover failures in production. The separation of build-time testing and production monitoring, combined with automated quality gates that block risky releases, establishes a gold standard that should influence industry practice across agent development.