Automated Artificial Intelligence Agent Evaluation with Amazon Bedrock AgentCore and GitHub Actions

Amazon Web Services has announced a continuous integration and continuous delivery quality gate model that automatically evaluates the performance of artificial intelligence agents using Amazon Bedrock AgentCore and GitHub Actions, preventing performance drops before reaching the production environment.

Continuous Integration and Quality Gate

A continuous integration and continuous delivery quality gate model is established to prevent artificial intelligence agents from degrading in performance alongside code changes. This structure automatically evaluates the agents.

This process, which ensures that code changes are tested before reaching the production environment, blocks relevant pull request operations when a performance drop is detected.

GitHub Actions and AgentCore Integration

Within the scope of the process, the GitHub Actions pipeline successfully deploys the agent to the AgentCore runtime. It then ensures the agent is tested with evaluation prompts using the AgentCore Evaluate API.

In the event of a regression in agent performance, the system automatically halts the pull request process and prevents errors from moving to production.

Authorization and Security Infrastructure

The architecture features an artificial intelligence agent connected to a Model Context Protocol server equipped with role-based access control. A shared Cognito pool is used for authentication operations.

The system supports both machine-to-machine and user-scoped authentication flows to create a secure operating environment.

Comprehensive Implementation Details

The solution covers deploying agent and server components to the AgentCore runtime using CDK infrastructure coding, along with conducting instant tests using built-in evaluators.

Additionally, OAuth challenge management processes are addressed to enable CI pipelines to authenticate without user context.