AWS shows automated AI agent quality gates with AgentCore and GitHub Actions

AWS's guide addresses a growing pain point in agent development: how to ensure quality as code changes frequently. Without automated evaluation, regressions go unnoticed until users complain. The proposed solution integrates evaluation directly into the development workflow, catching issues before they reach production.

The architecture uses several AWS services: AgentCore runtime hosts the agent and MCP server, Cognito provides authentication, and GitHub Actions runs the pipeline. The key challenge is OAuth for headless CI pipelines, which AWS solves by using M2M tokens that bypass role checks, though this means role enforcement is not tested in CI.

The guide includes a practical example with a deliberately bad system prompt, showing how the pipeline catches the regression with scores dropping to 0.0, then passes when fixed. The success of this approach hinges on setting an appropriate threshold, as LLM-as-judge scores have inherent variance.