Every engineering team asks the same question: how do we adopt AI coding agents without breaking the SDLC we spent years building? CI catches real bugs, code review catches design issues, staging catches integration failures. Handing an agent write access to all of it at once is not a migration plan.

What does not work

Two approaches fail. Giving an agent full GitHub write access and hoping review catches everything does not scale: review was designed for human mistakes, not agent-scale mistakes, and a reviewer can catch one bad line in a 200-line diff but not ten across five PRs opened at once. The big-bang rewrite fails differently: rebuild your whole pipeline around agents in one sprint and you lose the baseline, so you no longer know whether the new process is better or worse.

What works

Step one: model your current SDLC as a pipeline. Write down every step from commit to deploy: code review, test suite, staging deploy, integration tests, production deploy. Each step has an owner (human or automated), a trigger, and a pass/fail condition.

Step two: replace one step with an agent step behind a gate. Pick the most bounded, lowest-risk step: PR description generation, test case suggestion, changelog drafting. The agent produces output, a deterministic gate checks it (schema valid? matches the diff? no secrets?), and a human reviews the gate result before the output is used. The gate catches what the agent cannot see; the human sees the evidence.

Step three: measure, then raise autonomy. Track how often the gate catches problems. If after a month it has never fired, the step is safe to auto-approve. If it fires regularly, the step needs a better prompt or a tighter gate. You do not guess - you have the data.

Farna Control is built for this. Define your SDLC as a pipeline, add a single sandbox_agent node for one step, wire a gate after it, and leave the rest unchanged. The gate is deterministic - a regex, a schema check, or a scored eval - and the audit trail records every decision. Add a second agent step by adding another node.

The principles behind this: humans in the loop where it matters and immutability of runs - each run is a snapshot you can inspect, pause, and resume, so the migration is always reversible.