10/16/2026
AI-assisted development on a greenfield build and AI-assisted development on a 10-year-old codebase are not the same problem.
On greenfield: context is relatively clean. The AI agent can work within a defined architectural approach without accumulating the wrong patterns. Governance still matters - but the surface area for contextual errors is smaller.
On a legacy codebase: everything changes. Architectural debt means patterns that conflict with each other. Domain logic that lives in institutional memory, not documentation. Data flows with constraints that aren't visible in the code. Security sensitivities accumulated over a decade.
AI agents generating code in a legacy environment - without governance frameworks that account for those constraints - produce output that looks right and is wrong in ways that are expensive to find later.
The right question for any software partner isn't "can you do AI-assisted development?" It's "how does your approach change when the environment has history?"
A partner who gives the same answer for both hasn't done enough of the second kind to know the difference.
We have. It's why environment fit is a core part of how we work.