MLflow + Model Serving + Lakebase stack for an agent pipeline

Hey! Wanted to share some notes from a pipeline that I build using databricks. Please share any red flags or questions. The core idea: instead of hardcoding model calls, I wired MLflow (tracking + model registry) and Model Serving endpoints in as tools that a LangGraph agent graph can discover and invoke through an MCP interface. So the graph doesn't need to know ahead of time which model version it's hitting, it just asks the MCP server what's exposed, and Model Serving handles routing the request to whatever's currently marked as the production alias in the registry. That decoupling turned o