Systematic, automated evaluation integrated into CI/CD pipelines is revolutionizing how AI engineering teams develop applications with Large Language Models (LLMs). By adopting continuous testing, teams can detect issues early, save time, and deliver higher-quality products, moving beyond manual evaluations to a system that validates every deployment automatically. This approach is proving beneficial for early adopters, enabling faster iteration cycles and reducing unexpected production issues. Automated AI evaluations, or "evals," assess application quality, accuracy, and behavior with every code change, using tools that offer semantic evaluation, agent-specific tests, and production-ready automation. Among the platforms, Braintrust stands out for its comprehensive CI/CD integration, providing a dedicated GitHub Action that runs experiments and posts detailed results on pull requests, allowing teams to track quality changes and address regressions effectively. Other tools, like Promptfoo, Arize Phoenix, and Langfuse, offer varying degrees of CI/CD support and flexibility, with Braintrust noted for its user-friendly, experiment-first approach that eliminates setup complexity and enhances debugging capabilities.