GenAI systems change behavior in ways that are difficult to predict. Prompt updates, model upgrades, and data changes can introduce regressions that are hard to detect before they reach users. Unlike traditional software, behavior can shift without a clear diff, test failure, or obvious rollback path. This session focuses on the mechanics of deploying GenAI systems safely in production. We’ll discuss how teams treat prompts, models, and configuration as deployable artifacts, roll out behavior changes incrementally, and recover when outputs degrade rather than fail outright. The session also touches on maintaining environment parity, canarying AI behavior, and integrating GenAI components into existing CI/CD pipelines.