What an AI-native SDLC with Claude Code looks like
MasterFabric Team · · 1 min read
Most teams meet AI coding tools as autocomplete. Useful, but it leaves the lifecycle untouched: the same tickets, the same handoffs, the same late surprises in review. An AI-native SDLC changes where the work happens, not just how fast lines get typed.
The loop
In our studio every change runs through the same four steps inside one Claude Code session.
- Plan — the brief becomes a task. Claude Code reads the repository and the constraints before any file changes.
- Build — the diff is drafted in the working tree, where the engineer can see and steer it.
- Prove — tests, lint, and type checks run in the same session, not as a later handoff.
- Review — an engineer reads and accepts the diff. Nothing ships because an agent wrote it.
$ claude
> Plan the change from the brief, then implement it in review.What stays with people
Architecture, security boundaries, data handling, and anything a client will be held accountable for. The agent drafts; the engineer decides. That split is what lets institutions adopt the flow without giving up control.
The goal is not fewer engineers. It is engineers spending their time on the calls that matter.
Where to start
Pick one repository and one recurring kind of change — a form, an endpoint, a migration. Run it through the loop end to end, keep the review strict, and only then widen the scope.