AI code review: how to ship agent-written code without losing control

The uncomfortable truth about agentic coding: the agent can produce correct-looking code faster than you can read it. If your workflow doesn't scale review, you end up either rubber-stamping everything (losing control) or reading every line (losing the speed you adopted the agent for).

The fix isn't more trust — it's a review workflow designed for machine-generated diffs.

Never let an agent write straight to your working tree. The entire interaction should produce a reviewable change set — like a pull request, but at task granularity.

In Deyin this is the default posture: every task ends in the change review panel where you accept or reject each diff. Nothing touches your files silently. This single constraint converts the agent from a risk into a proposal generator.

Most agent damage happens during *exploration* — running commands, installing packages, poking at state. Keep planning and research read-only until a plan is explicitly approved.

You want the property that the worst outcome of a bad plan is a wasted plan, not a mutated repo. Deyin's plan → build split exists for exactly this: the agent researches, you edit the plan, only then does it build.

Vibes are not verification. For autonomous work, define the goal mechanically: the migration is done when the test suite passes; the bug is fixed when the regression test fails before and passes after.

Deyin's goal mode takes this literally — you set a verifiable objective and the agent iterates until tests pass. 26 tests passed is a finish line; I believe the change is complete is not.

**Boundary first.** Scan which files changed. Anything outside the task's blast radius gets rejected before you read a line.

**Tests tell the truth.** Did the agent add or adjust tests to match new behavior, or only to make existing tests pass? New behavior without new tests is a rejection.

**Delete the scaffolding.** Agents love defensive code: extra try/catch, console logging, TODO comments. Strip it like you would from any junior PR.

**Run it yourself.** The agent ran the tests; you run the app. Thirty seconds of clicking the feature catches what test suites miss.

For scheduled or long-running work you can't babysit every diff. The answer is evidence, not vigilance: every run should leave a trail of what it read, changed, and verified, so review becomes sampling plus spot checks.

Deyin keeps complete session histories locally (JSONL) and its delivery mode adds evidence ledgers and readiness gates for longer goals — review the trail, not just the output. And because sessions are local-first, your code never trains anything.