The pull request goes in. An AI reviews it. The review suggests changes. The developer asks an AI to make those changes. The updated pull request goes back through review.
Then the process starts again.
This is the Review Loop: a development process in which review and repair become an ongoing conversation between AI systems while the human watches, approves, and tries to remember what the code was supposed to do.
The problem is not that every suggestion is bad. Many of them are reasonable. The problem is what happens when a series of reasonable local fixes begins to replace a coherent understanding of the whole change.
How the loop starts
An AI review can catch real issues. It might notice a missing edge case, an unclear variable name, an error that is not handled, or a path that has not been tested. Asking an AI to address those comments can save time.
The first round feels productive because it is productive. A real issue was found, and a plausible fix was made.
The trouble begins when every round creates an automatic reason for another round. The new code is reviewed, more comments appear, and the developer asks for another batch of changes. Each adjustment is small enough to accept. None seems serious enough to stop the process and reconsider the design.
After several cycles, the code may still be locally correct. A function handles the case the reviewer identified. A name is clearer. An error path has been added. Yet the pieces no longer fit together as cleanly as they did at the start. The code has accumulated decisions without anyone taking responsibility for the overall shape.
That is how code becomes exhausted.
Local fixes, global confusion
The developer often begins with a clear intention: add a feature, change a behavior, or fix a bug. The original design may have been simple enough to explain.
After several AI-generated review and repair cycles, the result can drift away from that intention. The drift is difficult to see because no single change caused it. Each step looked justified at the time. The model responded to the latest comment, not to the complete history of the design.
That distinction matters. A reviewer looking at one issue may optimize one function. The author, or the team, needs to maintain a system.
The more rounds the code passes through, the more the developer's own understanding can weaken. Instead of deciding what should change, they begin checking whether the latest suggested patch looks acceptable. Instead of explaining the design, they compare the current version with the previous one and trust that the accumulated comments point in the right direction.
At some point, the developer is no longer acting as the author. They are acting as an approver.
Approving code you do not understand is not review. It is rubber-stamping.
Why endless review feels responsible
The Review Loop is seductive because it wears the appearance of care. More review sounds safer than less review. More comments sound like more quality. A developer can point to every change and explain which review comment caused it.
That makes the cost easy to miss.
There is the direct cost of spending time on repeated review cycles. There is also the harder-to-measure cost of losing the thread of the design. The developer has to reconstruct why the code looks the way it does, and the answer may simply be that several independent suggestions changed it one piece at a time.
The loop can also make a pull request look polished while leaving its purpose unclear. The code has been cleaned up, reworded, and adjusted, but no one has stopped to ask whether the complete change still solves the original problem in the simplest way.
More review is not automatically better review. Review is useful when it improves a human's understanding of a change. It becomes counterproductive when it replaces that understanding with a sequence of approvals.
Put a limit on the loop
The simplest countermeasure is a limit.
Decide in advance how many AI review cycles a pull request can go through. After two rounds, stop and have a human review the whole change. Read the diff from the beginning. Restate the original intent. Check whether the current design still serves it. If the code has drifted, rewrite the relevant part instead of asking for another narrow fix.
This does not require treating AI review as useless. It means giving it a defined role. An AI can point out issues and handle routine changes. A human still needs to own the design and understand the final code.
The Review Loop ends when someone stops responding to comments one at a time and looks at the work as a whole.
That pause is not wasted time. It is the part of the process that keeps the code maintainable.