AI code review tools like CodeRabbit and Greptile are useful. But if the plan is to let the agent build it and then let the reviewer clean it up afterwards, you may already be too late.
By the time a review tool sees the PR, the agent has already made the decisions that matter. It has chosen the shape of the implementation, made assumptions about the existing code, followed or invented patterns, decided where the change belongs, and turned ambiguity into something concrete. It either wrote a test strategy or quietly skipped one.
A reviewer arriving after all that can still flag issues, suggest fixes, and catch the style problems and missed edge cases. That is real value. But it is not the same as steering the work.
The expensive mistakes in AI-assisted delivery usually happen earlier than the PR. They happen when the agent misreads the system, works in too large a slice, follows the wrong pattern, or optimises for code that runs instead of code that fits. Polishing the output at the end does not help if the direction was wrong from the start.
And this is where the speed quietly disappears. All the time you think you gained by letting the agent move fast gets handed back at the end. The review flags the wrong assumption, the agent reworks it, the change churns through another round, and the work you thought was nearly done turns out to need unpicking. The speed was real. It just moved to the part of the process you were not watching.
Good engineering teams already learned this. You do not improve delivery quality by saving all the thinking for review. You improve it by baking the outcome, the constraints, the acceptance criteria, and the verification into how the work gets done. Agents are no different.
Review tools still belong in the loop. As a check, not as the delivery model. The aim is not to make weak AI output easier to tidy up. It is to have the work arrive in a better state in the first place.
Not post-hoc polish. Workflow-native quality.
By the time a review tool sees the PR, the agent has already made the decisions that matter. It has chosen the shape of the implementation, made assumptions about the existing code, followed or invented patterns, decided where the change belongs, and turned ambiguity into something concrete. It either wrote a test strategy or quietly skipped one.
A reviewer arriving after all that can still flag issues, suggest fixes, and catch the style problems and missed edge cases. That is real value. But it is not the same as steering the work.
The expensive mistakes in AI-assisted delivery usually happen earlier than the PR. They happen when the agent misreads the system, works in too large a slice, follows the wrong pattern, or optimises for code that runs instead of code that fits. Polishing the output at the end does not help if the direction was wrong from the start.
And this is where the speed quietly disappears. All the time you think you gained by letting the agent move fast gets handed back at the end. The review flags the wrong assumption, the agent reworks it, the change churns through another round, and the work you thought was nearly done turns out to need unpicking. The speed was real. It just moved to the part of the process you were not watching.
Good engineering teams already learned this. You do not improve delivery quality by saving all the thinking for review. You improve it by baking the outcome, the constraints, the acceptance criteria, and the verification into how the work gets done. Agents are no different.
Review tools still belong in the loop. As a check, not as the delivery model. The aim is not to make weak AI output easier to tidy up. It is to have the work arrive in a better state in the first place.
Not post-hoc polish. Workflow-native quality.