Automated agent evaluation with Amazon Bedrock AgentCore and GitHub Actions

This article demonstrates how to build a CI/CD quality gate that automatically evaluates AI agents deployed on Amazon Bedrock AgentCore, using GitHub Actions to block pull requests when agent performance regresses.

• Deploy agents and OAuth-protected MCP servers to AgentCore runtime using CDK infrastructure-as-code
• Implement three-layer authentication pattern supporting both machine-to-machine and user-scoped OAuth flows
• Use AgentCore Evaluations API with built-in evaluators (GoalSuccessRate, Correctness, ToolSelectionAccuracy, ToolParameterAccuracy) to score agent behavior
• Invoke OAuth-protected runtimes from CI pipelines using client_credentials grant without user context
• Enforce quality gates that automatically block merges when evaluation scores fall below defined thresholds
• Compare three evaluation approaches: stored traces, service accounts with pre-authorized consent, and M2M authentication
• Handle OpenTelemetry trace propagation delays (30-90 seconds) and runtime startup timing in CI workflows

Automated agent evaluation in CI/CD catches performance regressions before production, replacing subjective manual testing with objective LLM-as-judge scoring on representative prompts.