It is four in the afternoon and you have approved things you did not really read. Not out of laziness. The diffs kept coming, each one plausible, each one another wall of code an agent produced in the time it took you to finish the last one. Somewhere in the last few hours “review” quietly became “skim and hope.” You know it, and you feel the low dread that comes with it.
This is review fatigue, and it is not a discipline problem you can willpower your way out of. It is arithmetic. The agents write faster than you read, and the gap does not close by reading faster.
Why AI review costs more, not less
There is a specific reason reviewing agent code is heavier than reviewing a teammate’s, and it is worth naming precisely. When a colleague sends you a pull request, the intent comes attached. You were in the standup. You saw the ticket. You know why they chose this approach because you can ask, or because the commit message carries the reasoning of a person who held the whole problem in their head. Reviewing that code is mostly checking a shared understanding.
Agent code arrives stripped of all that. There is no intent riding along, no reasoning you can reconstruct by asking, no shared context from a conversation you were both in. You are handed a plausible artifact and asked to reconstruct, from the diff alone, both what it was supposed to do and whether it does it. Developer surveys keep reporting the same thing practitioners feel: reviewing AI-generated code frequently costs more attention than reviewing a human colleague’s. That is not a knock on the models. It is what happens when the author cannot be interrogated and the intent did not travel with the code.
Multiply that per-diff cost by agent throughput and fatigue is not a risk, it is the guaranteed steady state.
Reading harder is the wrong axis
The instinct is to try harder: concentrate more, review every line, catch everything. It fails for a structural reason. Your reading capacity is fixed and the agents’ production capacity is not. Any strategy that answers more output with more careful reading loses by construction, and it loses in the worst way, by degrading gradually until you are rubber-stamping without admitting it.
The other instinct is to give up and trust the output, which is how locally correct, globally incoherent code accumulates until the system no longer makes sense as a whole. Neither reading everything nor reading nothing is available. What is available is reading differently: spending your fixed attention where it buys the most certainty per minute.
The moments that carry the most information
Not all review moments are equal. Some cost a minute and settle the direction of an entire task. Others cost an hour and catch a typo. The trick is to spend your attention in the order of leverage.
Scope, first. Before the agent builds, approve what it is about to do. What behavior changes, what stays untouched, what the result has to satisfy. This is the highest-leverage minute you will spend, because steering intent before the code exists is far cheaper than auditing a finished implementation and discovering it solved the wrong problem. The cheapest review is the one you do on the plan, before a line is written, which is much of what spec-driven development is for.
Diff, against the scope. When the code comes back, read it against the scope you approved, not against a vague memory of what you wanted. The question is narrow and answerable: does this diff do what we agreed, and only that? A bounded question is reviewable. “Is this good?” is not.
Evidence, last. Then look at what actually ran: which commands, what they printed, against which contract. This is where you confirm behavior instead of inferring it, and it is the fastest of the three when the evidence was captured as the work happened rather than reconstructed by you afterward.
Scope, then diff, then evidence. Three checkpoints replace one unbounded reading task. Each is a specific, answerable question, and together they let a human stay in control of far more output than they could ever read line by line.
Not every diff deserves the same read
The three checkpoints tell you when to spend attention. Triage tells you how much, because treating every change as equally risky is its own way of burning out. A one-line copy fix and a change to the authentication path are both “a diff,” and reading them with the same intensity means either over-reading the trivial one or under-reading the dangerous one. Usually both, on the same afternoon.
Sort by blast radius, not by arrival order. A change touching money, auth, data deletion, or a shared interface earns a slow, line-by-line read of the diff and a hard look at the evidence, every time, no matter how confident the report. A change to an isolated leaf with tests around it can often be trusted at the scope-and-evidence level without a full line read, because the cost of it being wrong is bounded and the proof is right there. The skill is not reading everything closely. It is knowing which changes cannot afford a light read and reserving your deepest attention for those.
This is also why the scope checkpoint pays off twice. Approving scope up front does not only steer the work, it tells you in advance which bucket the incoming change belongs in. You already know, before the diff arrives, whether this one is a leaf or a load-bearing wall, so the review effort is budgeted before you are tired instead of decided in the moment when your judgment is worst.
What has to be true for this to work
This order only works if two things hold. The scope has to exist as something you actually approved, not something you can reconstruct after the fact. And the evidence has to be captured automatically, as a byproduct of the work, or you are back to re-running things by hand and the fatigue returns through the side door.
That second condition is the link between review fatigue and the rest of verification. If completion already requires a real demonstration, the evidence is sitting there when you get to the third checkpoint. If work closes with its proof attached, the review’s final step is reading a record, not rebuilding one. Review stops being the place where all the missing verification piles up, which is exactly why trust, not output, is the bottleneck: a review you can trust is what lets the throughput mean anything.
Where PaellaDoc fits
PaellaDoc is built around approving scope up front and capturing evidence as work runs, so review lands where it has leverage instead of at the end as an exhausting last line of defense. You steer the intent before the agent builds. The evidence is there when the change comes back. The diff gets read against a contract you already agreed to, not against your tired memory. The reviewer stays in control without pretending to read everything, because the process, not heroics, is what makes the output trustworthy.
The code will keep arriving faster than you can read it. That is permanent. Reviewing at the right moments is how you stay in charge of it anyway.
Frequently asked questions
Why is reviewing AI-generated code more tiring than reviewing a colleague’s?
Because the intent does not travel with it. A teammate’s pull request comes with shared context: the standup, the ticket, a commit message carrying a person’s reasoning, an author you can ask. Agent code arrives stripped of all that, so you have to reconstruct from the diff alone both what it was meant to do and whether it does it. Multiply that heavier per-diff cost by agent throughput and fatigue is the guaranteed steady state.
How do I keep up with code an agent writes faster than I can read?
You cannot, by reading faster or harder, because your reading capacity is fixed and the agent’s output is not. Read differently instead: spend your fixed attention where it buys the most certainty per minute. Approve scope before the agent builds, read the diff against that scope, then confirm the evidence of what ran. Three bounded checkpoints replace one unbounded reading task.
Should I review every line of AI-generated code?
Not equally. Reading a one-line copy fix with the same intensity as a change to the authentication path means over-reading the trivial one and under-reading the dangerous one. Sort by blast radius: changes touching money, auth, data deletion, or shared interfaces earn a slow line-by-line read every time. An isolated leaf with tests around it can often be trusted at the scope-and-evidence level.
What has to be in place for staged review to work?
Two things. The scope has to exist as something you actually approved up front, not something reconstructed after the fact. And the evidence has to be captured automatically as a byproduct of the work, or you are back to re-running things by hand and the fatigue returns through the side door. When completion already requires a demonstration, the evidence is waiting at the final checkpoint.