Can you trust AI-generated code? A practical guide to reviewing agent work at scale

The question people actually ask, once they get past the demo, isn’t “can an AI agent write code.” Most have already seen it do that convincingly. The question underneath it is quieter and harder to wave away: can you trust code you didn’t watch get written?

With one agent in one terminal, that’s not really a problem. You’re watching. You read the diff before it merges, you know what changed and why, and if something looks off, you catch it right there. Trust, in that setup, is just attention. You’re paying it, so it’s earned.

The problem shows up the moment there’s more than one agent on more than one task, maybe on more than one machine. Now there are a dozen diffs a day instead of one, and nobody’s reading all of them personally. We run a herd of agents ourselves, and this isn’t a hypothetical for us. It’s the normal Tuesday.

Why volume breaks the “just watch it” model

Two ways of handling that volume both fail. The first is reading everything anyway, and it doesn’t survive contact with a real backlog: nobody scales their personal attention linearly with the number of agents they run, so the queue of unreviewed diffs either piles up (nothing ships) or gets rubber-stamped (review stops meaning anything).

The second is trusting it blindly: shipping whatever an agent produces on the assumption that it’s probably fine. That’s not trust. It’s exposure with better marketing.

The real fix isn’t a smarter agent, and it isn’t a more trusting human. It’s the same fix any engineering team reaches once it outgrows the idea that a senior engineer can read every PR personally. Stop relying on one person’s attention, and build the checking into the process itself, so it happens the same way every time, whether anyone’s watching that day or not.

Make review a stage

That’s what a real task lifecycle is for. Instead of “an agent does the work and someone eyeballs it if they get a chance,” a task moves through a defined pipeline: plan, code, review, merge, verify, done. Each stage is a genuine, server-enforced gate rather than a step someone can quietly skip under deadline pressure. See how it works for the full walkthrough of the pipeline and what each stage actually checks.

Human approval where it matters most

None of this is about removing people from the loop. It’s about being deliberate about where their judgment goes. You approve a plan before any code gets written against it, so you’re weighing in while it’s still cheap to change your mind. You read the hand-off comment a task carries between stages: what the previous agent did, why it did that, and what’s still open. That saves you from re-deriving the state of the work from scratch. And you accept the final result.

Your job is making the calls only you can make. For everything else, you can step back. If you want to watch the work happen instead of waiting for a summary, drop into any agent’s live terminal session right from the browser. You’ll see what it’s reading and changing, live, and catch it early if it hits a question only you can answer. See features for the rest of what that dashboard surfaces.

Trust needs receipts

A process is only as trustworthy as your ability to check it actually ran. That’s what an audit trail is for. Task transitions write a record. So do agent actions and cost events. Every record is scoped to your workspace, queryable, and has no API route to edit or delete it.

If a question ever comes up about what happened on a given task, the answer isn’t “we’re pretty sure.” It’s a record you can pull up: who approved what, when a stage changed, what a session actually did. Security goes deeper on the audit trail and the rest of the trust boundary, including how API keys are revoked.

What “trust” means if you can’t read the diff at all

Everything above still assumes you’re a developer who could, in principle, read the code. Plenty of the people this matters to most can’t: a product lead or an executive with a clear idea of what “done” looks like, but no ability to evaluate a pull request line by line.

For that audience, trust can’t come from reading the work, because reading the work was never on the table. It has to come from knowing the same pipeline ran regardless: the plan was written down before code existed, a real merge request went through review, and verification happened before anything counted as shipped.

That’s the same discipline a careful engineering team holds itself to, whether or not the person filing the task can read a diff. Why CodeHerder lays out that side-by-side in more detail: running agents by hand versus running them through a system that holds the line either way.

What gates don’t promise

Gates raise the floor. They don’t replace judgment, and they can’t catch a stage that’s run in bad faith: a review that rubber-stamps whatever it’s handed is worth exactly nothing, whether a tired human or an agent under time pressure is doing the rubber-stamping.

What the pipeline guarantees is that the check happens. A real plan exists. A real merge request exists. A real review and a real verification step ran before anything reached done. It doesn’t guarantee any individual review was thorough, any more than a company having a code review policy guarantees every review at that company is good.

Structure beats vibes, but it’s not a substitute for the reviewer actually looking, whether that reviewer is a human or an agent. We built the gates. We didn’t build a replacement for someone paying attention.

What you’re actually trusting is the same thing you’d trust in a team that never skips its own gates. If that’s the bar you already hold your own engineers to, it’s a reasonable one to hold agents to as well. How to run a team of AI coding agents covers the coordination side of running a herd; request access to see the gates hold on real work.