Generic LLM judges and static prompts fail to capture domain-specific nuance in football defensive analysis. The architecture for self-optimizing agents built on Databricks Agent Framework allows developers to continuously improve AI quality using MLflow and expert feedback. The agent, such as a DC Assistant for American Football, can interact with users via Databricks Apps, creating a tool-calling agent for specific domain expertise. The build phase creates an initial prototype, while the optimize phase accelerates to production by continuously optimizing the agent based on feedback.
