GitHub has published how it reviews the giant pull requests that AI coding agents generate. The answer is stacked pull requests: breaking a 1,000-plus-line diff into separate layers, each reviewable on its own.
The approach splits a large change into four layers, data, API, wiring, and UI, so each layer can be assigned to a different reviewer with the right expertise. Instead of one overwhelming diff, you get four focused ones.
Why This Is a Real Problem
AI coding agents changed the shape of code review. A human developer produces incremental commits. An agent, given a broad task, often produces one enormous diff at the end, and that diff is very hard to review well.
The failure mode is specific. A thousand-line diff gets skimmed, not read. Errors hide in the middle, the reviewer’s expertise is wasted on parts they do not care about, and the merge happens with less confidence than it should.
That is the problem GitHub is solving, and it is the same problem any team using AI agents at scale will hit.
How Stacked PRs Work
The mechanics are clean. Take the big change, split it by concern, data structures first, then the API surface, then the wiring, then the UI. Each layer becomes its own PR, merged in order, with the later PRs building on the earlier ones.
The key benefit is ownership. A data-layer reviewer looks only at data. An API reviewer looks only at the API contract. Each reviewer sees a diff sized for careful reading, not a wall of changes.
There is a coordination cost. Stacked PRs require ordering discipline, and the sequence has to stay coherent while reviews happen in parallel. GitHub’s own tooling has made stacks easier to manage, which is partly why the practice is spreading.
The Practical Takeaway
If your team uses AI coding agents and review quality has suffered, the fix is not a better linter. It is changing how diffs are structured, and stacked PRs are a proven pattern for that.
The rules of thumb: split by architectural layer rather than by file count, assign reviewers to the layer they know, and keep the stack small enough that the ordering is obvious. Four layers works for a large feature, beyond that the coordination cost starts to eat the benefit.
What It Means
This is GitHub documenting its own workflow, which makes it a strong signal for the industry. When the platform that hosts most code publishes how it reviews AI-generated code, that pattern becomes the default for everyone else to copy.
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