Automated agent evaluation with Amazon Bedrock AgentCore and GitHub Actions
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This article outlines a method for implementing automated agent evaluation within Amazon Bedrock AgentCore using GitHub Actions to establish continuous integration and delivery quality gates. The proposed solution deploys AI agents with role-based Model Context Protocol (MCP) tools and evaluates their performance automatically before merging code changes into production. A key challenge addressed is authenticating headless CI pipelines against OAuth-protected MCP servers that typically require user context, which the authors solve by implementing a dual-token authentication pattern. This approach allows CI to use machine-to-machine credentials to bypass role checks while preserving strict access controls for interactive users. The system utilizes AgentCore Evaluations to score agent responses based on OpenTelemetry traces, ensuring metrics like helpfulness and correctness meet predefined thresholds. If the evaluation scores drop below the set bar, the pipeline blocks the pull request, preventing regressions from reaching live environments. The guide details the necessary infrastructure components, including AWS CDK for deployment and Cognito for managing distinct authentication flows. Ultimately, this framework enables developers to maintain high-quality standards for AI agents through rigorous, automated testing integrated directly into their development workflows.
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