AWS has released a reference workflow for testing AI agents inside GitHub Actions and failing a pull request when behavior regresses. The pipeline deploys an agent and an OAuth-protected Model Context Protocol server to Bedrock AgentCore, runs evaluation prompts and scores the resulting traces.
AgentCore Evaluations can judge dimensions such as helpfulness, correctness, goal completion, tool selection and tool parameters. Teams may use built-in or third-party evaluators, write a custom model-based judge, or run deterministic checks in an AWS Lambda function. A score threshold—0.8 out of 1 in AWS’s example—can determine whether a merge proceeds.
The reference architecture uses temporary GitHub-issued credentials to assume an AWS role instead of storing long-lived cloud keys. It also demonstrates a shared Cognito identity pool and role-based access to agent tools. For teams that cannot safely call a live protected service during every pull request, the evaluation API can score previously captured traces instead.
This workflow does not make agent quality fully objective: model-based judges and test prompts still require careful design. It does move regression checks into the same automated review process already used for conventional software.