Legacy to AI-First Project
Moving legacy projects to AI-first incrementally.
Legacy to AI-First Project
Moving a legacy project to AI-first does not mean a big-bang rewrite. We focus on incremental adoption, technical debt, and people and process.
Legacy to AI-First
We transition legacy projects toward AI-first practices step by step: incremental adoption, risk control, and measurable outcomes.
Incremental adoption
We introduce AI tooling and agents in low-risk areas first: tests, docs, small features. We expand as the team and codebase are ready.
Example order we often use:
| Phase | Focus | Why low-risk |
|---|---|---|
| 1 | Docs and comments | No runtime behavior; easy to review and revert. |
| 2 | Unit tests for existing code | Tests are scoped; AI can draft, human verifies. |
| 3 | New, isolated features | Clear contract; AI can help with boilerplate. |
| 4 | Refactors and migrations | After we have patterns and tooling (see Migrations Garage). |
Technical debt
We pay down debt where it blocks automation or agent use: e.g. untestable code, missing APIs. This makes the project easier for both humans and agents.
People and process
We train the team on prompts, review standards, and when to rely on AI vs. human judgment. Process evolves with the tooling.
Definition of “AI-first”
AI-first here means: the project is designed and operated so that AI-assisted development and, where appropriate, agents are first-class. It does not mean every line of code is AI-generated.