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

• URL: http://arxiv.org/abs/2607.15769v1
• Date: Fri, 17 Jul 2026 08:58:54 GMT
• Title: Making Agent-Mediated Contributions Governable: A Project-Level Governance Manifest for Open-Source AI Collaboration
• Abstract summary: Generative AI and coding agents are intensifying a central governance tension in open-source software.<n>Existing responses improve agent-readability and traceability, but project rules must also organize contribution-specific risk, evidence, accountability, and review-gate states.<n>We theorize this organizational arrangement as project-side governability infrastructure.
• Score: 12.77923521434568
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Governing at Machine Speed: An Adaptive Intelligence Architecture for Real-Time AI Policy Enforcement [0.0]
  Enterprise AI adoption has reached 78% of organizations globally, yet the infrastructure to govern that adoption has not kept pace.<n>We propose AGIL, a conceptual five-layer architecture designed to use machine learning for real-time AI governance enforcement.
  arXiv  Detail & Related papers  (2026-09-11T19:35:00Z)
• Behavioral Governance for Autonomous AI Agents: The AgentBound Framework [2.4469484645516832]
  We present AgentBound, a runtime governance framework that provides verifiable behavioral oversight for autonomous AI agents.<n>AgentBound evaluates each proposed action using three independent authorities: delegated authorization, owner-signed behavioral constitutions, and site action contracts.<n>AgentBound generates cryptographically verifiable governance receipts that bind every action to the exact delegation, policy, and semantic artifacts governing the decision.
  arXiv  Detail & Related papers  (2026-06-29T23:12:06Z)
• Toward Pre-Deployment Assurance for Enterprise AI Agents: Ontology-Grounded Simulation and Trust Certification [0.0]
  Post-deployment monitoring, human-in-the-loop controls, and prompt-level guardrails offer limited assurance once an agent is operating in production.<n>Ontology-grounded verification framework is first to combine three components.<n>System generates regulatory, operational, and adversarial test scenarios automatically.
  arXiv  Detail & Related papers  (2026-06-02T02:37:11Z)
• The Authorization-Execution Gap Is a Major Safety and Security Problem in Open-World Agents [111.54911637435269]
  The Authorization-Execution Gap (AEG) is a major safety and security problem in open-world agents.<n>The AEG is the divergence between what a principal intends to authorize and what an open-world agent ultimately executes.
  arXiv  Detail & Related papers  (2026-05-10T04:05:31Z)
• TRUST: A Framework for Decentralized AI Service v.0.1 [47.384270414446604]
  Large Reasoning Models (LRMs) and Multi-Agent Systems (MAS) in high-stakes domains demand reliable verification.<n>We introduce TRUST (Transparent, Robust, and Unified Services for Trustworthy AI), a decentralized framework with three innovations.<n>We prove a Safety-Profitability Theorem ensuring honest auditors profit while malicious actors incur losses.
  arXiv  Detail & Related papers  (2026-04-29T19:32:58Z)
• Governed Reasoning for Institutional AI [0.0]
  Cognitive Core is a governed decision substrate built from nine typed cognitive primitives.<n>Human review is a condition of execution rather than a post-hoc check.<n>We benchmark three systems on an 11-case balanced prior authorization appeal evaluation set.
  arXiv  Detail & Related papers  (2026-04-12T14:09:18Z)
• From Reactive to Proactive: A Multi-Regulatory Empirical Analysis of 480 AI Incidents and a Data-Driven Governance Compliance Framework [0.0]
  The study presents a cross empirical analysis of 480 realworld AI incidents from the AI Incident Database.<n>The results reveal substantial governance gaps, indicating persistent weaknesses in post-deployment accountability.<n>The study proposes the Proactive AI Governance Compliance Framework (PAGCF) to shift governance from reactive incident response toward pre-deployment compliance.
  arXiv  Detail & Related papers  (2026-04-10T21:22:55Z)
• Inference-Time Scaling of Verification: Self-Evolving Deep Research Agents via Test-Time Rubric-Guided Verification [71.98473277917962]
  Recent advances in Deep Research Agents (DRAs) are transforming automated knowledge discovery and problem-solving.<n>We propose an alternative paradigm: self-evolving the agent's ability by iteratively verifying the policy model's outputs, guided by meticulously crafted rubrics.<n>We present DeepVerifier, a rubrics-based outcome reward verifier that leverages the asymmetry of verification.
  arXiv  Detail & Related papers  (2026-01-22T09:47:31Z)
• Towards a Science of Scaling Agent Systems [79.64446272302287]
  We formalize a definition for agent evaluation and characterize scaling laws as the interplay between agent quantity, coordination structure, modelic, and task properties.<n>We derive a predictive model using coordination metrics, that cross-validated R2=0, enabling prediction on unseen task domains.<n>We identify three effects: (1) a tool-coordination trade-off: under fixed computational budgets, tool-heavy tasks suffer disproportionately from multi-agent overhead, and (2) a capability saturation: coordination yields diminishing or negative returns once single-agent baselines exceed 45%.
  arXiv  Detail & Related papers  (2025-12-09T06:52:21Z)
• Making LLMs Reliable When It Matters Most: A Five-Layer Architecture for High-Stakes Decisions [51.56484100374058]
  Current large language models (LLMs) excel in verifiable domains where outputs can be checked before action but prove less reliable for high-stakes strategic decisions with uncertain outcomes.<n>This gap, driven by mutually cognitive biases in both humans and artificial intelligence (AI) systems, threatens the defensibility of valuations and sustainability of investments in the sector.<n>This report describes a framework emerging from systematic qualitative assessment across 7 frontier-grade LLMs and 3 market-facing venture vignettes under time pressure.
  arXiv  Detail & Related papers  (2025-11-10T22:24:21Z)
• Alita-G: Self-Evolving Generative Agent for Agent Generation [54.49365835457433]
  We present ALITA-G, a framework that transforms a general-purpose agent into a domain expert.<n>In this framework, a generalist agent executes a curated suite of target-domain tasks.<n>It attains strong gains while reducing computation costs.
  arXiv  Detail & Related papers  (2025-10-27T17:59:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.