Making Agent-Mediated Contributions Governable: A Project-Level Governance Manifest for Open-Source AI Collaboration
Software EngineeringComputers and Society
Summary
The authors studied how AI tools are making it easier and faster for people to contribute code to open-source projects, but this creates problems because maintainers can’t keep up with checking the work. They looked at 50 GitHub projects and found that while there are some ways to track AI contributions, there is no big system to organize rules and reviews for these AI-assisted contributions. To solve this, they created the Agent Governance Manifest (AGM), a tool that helps contributors provide clear evidence about their AI-generated code and helps maintainers verify it. Their tests showed that AGM improves the accuracy and ease of reviewing AI-influenced code while keeping maintainers in control. The authors also proposed a framework to understand and manage AI contributions to open-source software better.
Authors
Jinjin Gao, Luyang Li, Shufen Guo, Ligang He, Xiaoning Sun
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.