Brownfield AI: Integrating LLM Features into Legacy Codebases Without a Rewrite
Every AI demo starts with a blank slate. A fresh repo, no dependencies, no legacy authentication system, no decade of business logic encoded in stored procedures. The demo works beautifully. Then someone asks: "Can we add this to our actual product?"
That's where brownfield AI begins — and where most teams get stuck. The gap between a working prototype and a production integration inside a ten-year-old monolith is not a matter of scaling up. It's a fundamentally different engineering problem, one that requires adapter patterns, careful boundary design, and a deep respect for the existing system's constraints.
