The process involves deploying an AI agent and an OAuth-protected MCP server to the AgentCore runtime. Subsequently, the pipeline invokes the agent with predefined test prompts. The responses are then scored, and any regression in behavior triggers an automatic block of pull requests within the GitHub Actions workflow. This automated evaluation streamlines the development process for AI agents, ensuring consistent performance and reducing the risk of introducing regressions. The integration leverages GitHub Actions for continuous monitoring and feedback, providing a robust mechanism for maintaining agent quality. This approach enables faster iteration and more reliable deployments of AI agents.
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