Automated agent evaluation with Amazon Bedrock AgentCore and GitHub Actions
Wire Amazon Bedrock AgentCore Evaluations into a GitHub Actions pipeline: deploy an AI agent and an OAuth-protected MCP server to AgentCore runtime, invoke the agent with test prompts, score the responses, and automatically block pull requests when agent behavior regresses.
The article outlines a method for implementing automated quality gates for AI agents using Amazon Bedrock AgentCore and GitHub Actions. The process involves deploying the agent and MCP server to AgentCore runtime using CDK, enabling role-based access control on the MCP tools through a three-layer authentication pattern, and invoking OAuth-protected runtimes from CI using the M2M client_credentials flow.
The article details the on-demand evaluation of the agent with built-in evaluators and enforces a quality gate that prevents merges when evaluation scores drop. It also explains how CI pipelines authenticate without user context in an OAuth-protected environment. The key concepts covered include AgentCore runtime, AgentCore Evaluations, MCP, OIDC federation, quality gates, and different evaluation modes such as on-demand, online, and batch evaluation.
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