Scaling Human-AI Coding Collaboration Requires a Governable Consensus Layer

Abstract:Vibe coding turns natural-language intent into working software, but code generation alone does not give humans and agents a shared account of what they are building. As collaboration spans participants, components, and releases, design choices must remain understandable and open to revision. We argue that scaling human and AI coding collaboration requires a governable consensus layer. Agentic Consensus organizes collaboration around a versioned project-state model linking selected intent, structural commitments, executable artifacts, and evidence. Participants can clarify intent before implementation and coordinate proposed changes against commitments that humans can inspect and revise. Bidirectional synchronization connects intended changes to implementation and exposes conflicts or stale evidence as artifacts evolve. We implemented its review workflow in Consensus Control. In a small exploratory human study, 10 participants used the system on projects from their everyday work and reported clearer, faster understanding of agent-submitted changes. A three-condition comparison with the same participants collected 60 responses, with near-ceiling decision scores and measured representation and pipeline costs. Our research agenda asks how shared project state can preserve human understanding and control as agent contributions accumulate.

Software Engineering (cs.SE); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)

arXiv:2604.17883 [cs.SE]

(or arXiv:2604.17883v2 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2604.17883 Focus to learn more arXiv-issued DOI via DataCite

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