The first time an agent hands you a 600-line pull request that passes every test, you feel the shape of the new job. The code compiles. The tests are green. And you still have to decide whether to trust it, because the agent wrote plausible code, not necessarily correct code, and plausible is a much lower bar than correct.
Reviewing human code, you lean on a shortcut you rarely notice: the author had intent. You can ask them what they meant, and their answer tells you whether the logic matches the requirement. An agent has no durable intent to interrogate. It produced text that looked right given a prompt. That is why 96% of developers report they do not fully trust the functional accuracy of AI-generated code, why those pull requests wait four to six times longer for approval, and why generated code ships an estimated 15 to 18% more security vulnerabilities. The reviewer became the bottleneck, and most teams are trying to clear it with willpower.
Willpower does not scale. A loop does.
Make the machine review first
A human should never be the first thing that reads an agent's output. Put every cheap, mechanical check ahead of them so their attention lands where it matters.
Types catch whole classes of nonsense for free. Tests written before the generation, not after, tell you whether the code does the thing you asked rather than the thing it decided to do. Linters and static analysis flag the security patterns that generated code reaches for by habit. Above all of it sits an eval suite: a set of checks that encode the behavior you actually care about, run on every change, and fail loudly when the agent drifts. Without that suite, "more code" just means "more reading," and the reading is what is killing you.
The rule is simple. If a property can be proven by a machine, a human must never be the one proving it.
Verify adversarially
The most useful pattern I install on teams is a second agent whose only job is to attack the first one's work. Not to praise it, not to summarize it, but to find the input that breaks it, the requirement it quietly ignored, the edge case it smoothed over. A generator that is graded by a skeptic produces very different output than one that is graded by a reviewer hoping to approve and move on.
Run the skeptic before the human. Feed it the spec and the diff and ask a single question: where is this wrong? When it comes back empty twice on genuinely adversarial prompting, the change has earned a human's time. When it keeps finding holes, the change goes back to the generator, and no person spent an hour discovering what a second model found in ninety seconds.
Read for intent, not syntax
Once the machine layer is clean, the human review changes character. You stop hunting for missing semicolons and start asking the questions only a person can answer. Does this match what the ticket actually needed? Does it fit how the rest of the system is built, or is it a locally clever solution that fights every convention around it? Would the next engineer understand why it exists?
This is the part of review that does not get automated, and it is the part that was always worth the most. The loop exists to protect it. Every mechanical check you push down the stack buys back attention for the judgment that keeps a codebase coherent.
Build a trust ladder
Not every change deserves the same scrutiny, and pretending otherwise is how teams burn out. Draw an explicit ladder. At the bottom, agents merge low-risk work unsupervised once the evals pass: dependency bumps, documentation sync, test additions, mechanical refactors. In the middle, a human reads the diff but the machine gate has already cleared the obvious. At the top, anything touching auth, money, migrations, or customer data gets a skeptic pass, a human read, and a second signoff, no exceptions.
The ladder is a living document. As the guardrails prove themselves on the low rungs, work climbs. As an incident teaches you something, work drops. The point is that the decision is deliberate and visible, not a mood that changes with how tired the reviewer is on a Friday afternoon.
Measure what shipped and held
Track verified throughput, not lines produced. Count the changes that passed the loop and stayed green in production a week later. Watch review latency and the escape rate, meaning the defects that made it past every gate. A healthy loop shows rising throughput with a flat or falling escape rate. If throughput climbs while escapes climb with it, your gates are theater, and you are shipping the productivity paradox in a nicer wrapper.
The teams that win the agentic era are not the ones generating the most code. They are the ones who built a loop that lets them trust it.