Motorway cuts AI agent errors 6× with AWS evaluation pipeline

Motorway, working with AWS, constructed an end-to-end evaluation pipeline designed to improve the reliability of AI agents in production. The pipeline combines two key components: the Strands Agents SDK, which provides agent-building capabilities, and Amazon Bedrock AgentCore, a fully managed service purpose-built for deploying and operating AI agents at scale. The results speak directly to the pain points of agent deployment: before the pipeline, 1 in every 8 queries returned an incorrect result, and when issues arose, it took several hours to detect them. After implementation, incorrect results dropped to 1 in 50—a six-fold improvement—and issue detection time collapsed from several hours to just a few minutes. AWS published this work as a production blueprint, offering the broader community a detailed guide on how to replicate the same evaluation framework for their own agents.

The challenge of evaluating AI agents in production is that failures are often hard to predict and slow to detect. Motorway's partnership with AWS demonstrates that automating this evaluation process—rather than relying on manual monitoring—can deliver dramatic improvements both in accuracy and operational speed. The 6× reduction in error rate (from 1 in 8 to 1 in 50) and the shift from hour-long detection cycles to minute-long ones reflect a fundamental shift from reactive troubleshooting to proactive quality assurance. By open-sourcing this blueprint via the blog post, AWS is positioning Bedrock AgentCore and the Strands Agents SDK as production-ready tools for teams that need to scale agent deployments with confidence.

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