Pull requests that merge without a human reading the code.
That doesn't mean the humans have left. It means they're looking somewhere else.
We put agents in a loop around the pull request. A reviewer agent reviews it. An author agent addresses the findings. The reviewer reviews again.
The loop converges: each round has fewer findings, until a review comes back clean. Then the PR merges.
Turn on auto-merge, stop looking, and hope.
Not to approve each PR, but to assess the quality of the automated loop. You're judging the reviewer, not the change.
Attention steps down as trust in the loop is earned. Each escape sends eyes back to the code, and then into tuning the loop.
Three PRs each passed review, and each added its own way to calculate an order total. Nobody noticed, because each one was fine on its own.
Then a rounding bug shows up in a user report. You fix it. Another report arrives about the same bug, somewhere else. Then another. One defect, many bug reports.
A review loop that lets duplication in is quietly multiplying future bugs.
calcTotal()orderSum()totalFor()The obvious targets are a stronger model and a better prompt. They help, but they're not enough on their own.
We also need to address the context the reviewer works in, and the structural guardrails around it. The strongest fix makes the defect impossible, so no reviewer has to catch it.
We're engineering the environment to tune the entire system.
Zero-eyes approval isn't a switch you flip for the whole codebase. It works well for some kinds of change, in some parts of the code, and fails for others.
Map it. Then engineer the system to fix the failures, one class at a time, and watch the map turn green.
Illustrative map.
Zero-eyes PR approval doesn't mean humans are out of the loop. It means humans are looking at the loop, at what escapes the loop, and at how to make the loop stronger.