When an AI can generate a spec, a design doc, or a thousand lines of code in a minute, the scarce thing is no longer production — it's trust. How do you know the artifact in front of you is correct, agreed-upon, and safe to build on? The answer is the same one good teams have always used: a review gate. Nothing becomes a source of truth until someone whose job it is to check it has actually checked it.
Every artifact has a gate, and a gatekeeper
In an AI-driven workflow, work moves as a stream of artifacts — a spec, a set of user stories, an architecture decision record, a system diagram, a pull request. Each one stops at a gate before it can move on. At that gate stands a specific reviewer who owns the decision: approve and lock it in, or send it back with notes. The crucial rule is that you never approve your own work. The author and the gatekeeper are different people on purpose, because the whole point of a gate is to catch what the author couldn't see.
This matters even more when an AI did the producing. AI output is fluent and confident, which makes it easy to wave through. A gate forces a deliberate, human "yes" — a moment where someone with authority and context stakes their name on the artifact being right before the team builds on top of it.
- ArtifactAnything produced — a spec, user stories, an ADR, an architecture doc, or code.
Review gateA named reviewer who must approve the artifact. You never self-approve.- Approved (locked)The gate passed: the artifact is accepted and becomes a trusted source of truth.
- Sent backThe gate failed: the artifact returns with notes and goes around again.
How it works: the corrections-vs-new-scope split
Here is the governance rule that does the heavy lifting. When a change is requested mid-flight, the reviewer's first job is to ask what kind of change is this? There are exactly two kinds, and they have different owners.
- A correction brings things back into agreement — the doc no longer matches the code, the spec contradicts itself, an ADR drifted from reality. Corrections restore consistency to work that was already agreed. The tech team owns these and can just make them.
- New scope is the business asking for something it didn't ask for before — a new feature, a different behavior, an expanded requirement. New scope changes what's being built and what it costs. Business or management owns these.
The diagram below shows the reviewer triaging an incoming change down one of these two paths.
- Requested changeAny edit asked of an artifact mid-flight — could be a fix or could be brand-new work.
- Reviewer triagesDecides which kind of change this is before letting it in.
Tech team ownsCorrections — bringing docs, code, and intent back into agreement — belong to the tech team.- Business approvesNew scope is a budget decision. Flag it and route it to business/management to approve.
Why is this split the deepest rule of all? Because quietly folding new scope into a spec is a scope and budget decision dressed up as an edit. If you're a junior — or an AI agent acting on your behalf — you simply don't have the authority to make it. The right move is never to silently absorb new scope. It's to flag it: "this is new, here's the cost, who approves?" and route it to the people who own that call. Smuggling it in isn't being helpful; it's making a decision that wasn't yours to make.
In our stack — when Claude Code (running on one of Anthropic's Claude models) produces a spec or a diff, it's an artifact heading for a gate, not a final answer. Claude Code is built to surface this distinction rather than paper over it: ask it to implement a change and, if the request quietly expands scope, the well-governed move is for it to flag the new scope for your approval instead of folding it in. You — or a designated reviewer — own the gate; the agent's job is to produce something clean enough to pass it and to be honest about what's a correction versus what's new.
Aim to pass the gate the first time
A useful definition falls out of all this: good AI output is output that's good enough to pass its gate on the first try. Not output that's fast, not output that looks plausible — output a reviewer accepts without sending back. That reframes how you direct an AI. A sharp spec up front, clear acceptance criteria, and explicit boundaries on scope all push the work toward a clean first pass. And once it's approved, the artifact is locked: it becomes the trusted ground that the next layer of work — and the next round of verification — stands on.