How Databricks Just Showed Everyone What MCP Actually Looks Like in Production

Drug discovery takes over a decade and costs billions. Researchers jump between PubMed, chemical databases, internal compound libraries, safety reports - all disconnected, all siloed. Databricks just published a blueprint showing how MCP closes that gap. And honestly, it's one of the most concrete agentic AI demos I've seen from any major data platform this year. Let me break it down. What AiChemy Actually Is AiChemy is a multi-agent system Databricks built on their own platform. The architecture is simple to describe but hard to execute: a supervisor agent that routes tasks across multiple specialized sub-agents, each connected to a different data source. The data sources include external MCP servers - OpenTargets for disease-gene associations, PubChem for molecular properties, PubMed for literature - and internal Databricks-managed MCP servers connected to proprietary chemical libraries. One internal source is a Genie Space, which gives the agent text-to-SQL access over a structured