How to review AI-generated code without slowing down

The dirty secret of AI coding agents: they write code faster than you can read it. Hand a task to an agent, come back ten minutes later, and there's a 400-line diff waiting: correct-looking, confidently written, and completely unverified. Multiply that across a fleet of agents and the work of writing code all but disappears. What's left is the work of trusting it.

That's the part nobody warns you about. Review is the new bottleneck. Here's how to keep it from strangling everything the agents just sped up.

Why review is suddenly the hard part

Before agents, writing and reviewing were roughly balanced: you spent hours on a feature and minutes skimming the diff. Agents inverted that. A prompt turns into a working feature in minutes, but the diff still takes a human just as long to read as it always did.

Skip the review and you're shipping unverified code at machine speed. And AI is very good at producing code that looks right while being subtly wrong. The confident tone is the trap. So the goal isn't to review less. It's to make each review fast, honest, and impossible to skip. Review minutes are also the first number to watch when you measure whether agents actually make you faster: they're usually the biggest entry on the cost side of that ledger.

Review AI code like a stranger wrote it

The most useful mental model: treat every agent result like a pull request from a new contributor you don't know yet. That means reading with a checklist, not a vibe:

• Does it actually do what you asked? Re-read your own brief first, then check the diff against it, not against what the agent claims it did.
• Did it change more than it needed to? Agents love to "helpfully" refactor unrelated code. Scope creep in a diff is a red flag.
• Are the edge cases handled, or just the happy path? Empty inputs, errors, auth, the null case.
• Do the tests prove anything? An agent can write a test that passes without testing the thing. Read the assertions, not the green checkmark.
• Would you have written it this way? If not, is it a real problem or just a style difference? Send back the real ones; let the style go.

None of this is new: it's ordinary code review. What's new is that you're now doing it constantly, for code you didn't write, sometimes across several tasks at once. The checklist also delegates: an agent can run it as a first pass before you read a line, the setup covered in make an agent your first reviewer.

Small tasks make review survivable

The single biggest lever on review speed is task size. A 60-line diff for one scoped change reviews in two minutes. A 600-line diff where an agent went off and "finished the whole feature" is effectively unreviewable. You'll rubber-stamp it, and that's exactly where the bugs get in.

So scope the work before you hand it off. A good agent task looks like a good ticket: a clear title, notes on intent, and acceptance criteria you can check the result against. Write those and you get two wins at once: a sharper agent and a diff small enough to actually review. It's the same discipline that makes vibe coding work instead of blowing up.

The other lever is timing. With Claude Code plan mode you review the approach before any code exists, so a wrong direction costs a sentence to fix instead of a diff to unwind.

Give review a lane, not a scroll

Here's where the tooling matters. Review falls apart when finished work has nowhere to land: you're scrolling back through a terminal trying to remember what an agent changed, with no diff and no "done" signal.

A board fixes that. In SanuDesk, you write a card, deploy it to a fresh agent session, and when the agent finishes, the card moves itself to In Review with a "what changed / how to test" note attached. Review stops being something you have to remember and becomes a lane you work through: open the card, read the summary, check the diff, approve it or send it back with feedback. The finished work waits for you instead of scrolling out of sight.

Because each card ran as its own agent session (tiled in a grid next to the others), you can see exactly which task produced which change, and you never lose the context of what the agent was told to do.

Reviewing a fleet, not a single agent

The review problem gets sharper the more agents you run. One agent, one diff: easy. Five agents finishing around the same time is where people drown. The fix is the same, just structured: running several agents at once only works if each one funnels its result into the same review lane, so a solo developer can direct a whole fleet and still lay eyes on every change before it ships.

And the reviews that are pure routine, like "does this recurring cleanup still pass the tests?", can be handed to a Loop that runs on a schedule and journals every result, so you only step in when something actually needs a human.

The bottom line

AI agents didn't remove the work. They moved it. The judgment that used to go into writing every line now goes into reviewing every diff, and your leverage is how fast and how honestly you can do that. Keep tasks small, review like a skeptic, and give finished work a place to land.

That's the workflow SanuDesk is built around: bring your own Claude Code, Codex or Gemini accounts, hand out work from a board, and review every result before it ships (free to start, no account required).

Download SanuDesk free and open your first project, or see the whole review loop on the features page.