Making Agent-Mediated Contributions Governable: A Project-Level Governance Manifest for Open-Source AI Collaboration

2026-07-17

AI Summary

This work addresses the surge in AI agent–generated contributions to open-source projects and the consequent challenges faced by maintainers due to the absence of coordinated governance mechanisms for risk assessment, evidence provision, and review. The paper proposes a project-level governance infrastructure that conceptualizes AI-mediated contributions as governable boundary objects. Central to this framework is the Agent Governance Manifesto (AGM), a bilateral contract linking contributors’ evidence preparation with maintainers’ verification authority. Evaluated through GitHub audits, user studies, and structured validation, the AGM significantly improves risk-label recovery rates (37/38 versus 15/37) and perceived reviewer support (6.14 versus 3.27), while enabling high-fidelity expression of governance state and structural compliance.

Abstract

Generative AI and coding agents are intensifying a central governance tension in open-source software (OSS): they scale contribution generation faster than maintainers can assess risk, evidence, and accountability. Existing responses improve agent-readability and traceability, but project rules must also organize contribution-specific risk, evidence, accountability, and review-gate states. We theorize this organizational arrangement as project-side governability infrastructure. A diagnostic audit of 50 GitHub repositories finds widespread general governance artifacts, observable agent-readability, and fragmented AI-governance cues, but no project-wide arrangement that coordinates shared rules, preparation obligations, verification rights, and maintainer decision authority across AI-mediated contribution workflows. We develop the Agent Governance Manifest (AGM) as a repository-hosted boundary resource and bidirectional governance contract linking contributor-side evidence preparation with maintainer-side verification. In a controlled reviewer-side evaluation with 15 participants and 75 task-level outputs, AGM-supported materials improved exact risk-label recovery (37/38 vs. 15/37) and perceived review support (6.14 vs. 3.27 on a 1-7 scale). In a contributor-side feasibility check, 15 participants completed 45 tasks; all final packages represented the core governance state correctly, and 41 passed strict structural validation. The study develops a three-layer framework of agent-readability, traceability, and governability, theorizes agent-mediated contributions as governable boundary objects, and advances compliance-enabling digital innovation governance while preserving maintainer decision authority.

Problem

• governability
• agent-mediated contributions
• open-source AI collaboration
• AI governance
• boundary objects

Innovation

• Agent Governance Manifest
• governability
• boundary object
• AI-mediated contributions
• open-source governance