Large language models (LLMs) are challenging to manage in production environments due to their unpredictable behavior when faced with real users. This complexity is heightened by the need for personalization, which requires context-aware responses tailored to different user types and roles, making it difficult to manage AI logic embedded in code. Snowflake Cortex and LaunchDarkly provide a solution by allowing AI behavior to be configured outside the codebase, enabling real-time updates and targeted changes without full redeployment. This approach decouples AI logic from deployments, treating prompts and models like feature flags that are configurable, observable, and reversible at runtime. The integration allows for monitoring performance and cost, ensuring data remains within a secure environment. This setup is exemplified by an AI-powered support agent that can adapt to user behavior while maintaining control and visibility, offering a flexible and secure foundation for deploying adaptive AI in production.