architecture governance

In organizations lacking a formal architect role, software architecture decisions are frequently made by practitioners without such titles. This study employs a mixed-methods approach, combining a survey of 54 practitioners with in-depth interviews of 7 participants, to investigate who actually makes architectural decisions and under what contextual conditions. The findings reveal that informal architects are extensively involved in critical architectural choices, while formally designated architect roles are primarily deemed necessary in large enterprises or complex teams. These results challenge the prevailing research paradigm that centers on formal architects, instead illuminating how architectural responsibilities are distributed across real-world development environments and highlighting their strong dependence on organizational context.

Enterprise-scale general-purpose agents lack built-in, reusable governance mechanisms for autonomous cross-tool operation, making it difficult to satisfy requirements for compliance, auditability, and behavioral controllability. This work proposes the CUGA policy system, which embeds runtime governance capabilities into five critical checkpoints of the agent execution pipeline—intent protection, playbook guidance, tool invocation control, human approval gating, and output formatting—through a modular “policy-as-code” architecture. Without requiring model fine-tuning, CUGA enables proactive, continuous, and structured behavior control. By integrating typed governance primitives, dynamic playbook injection, and human-in-the-loop approval, the system effectively blocks malicious requests, enforces structured tool sequences, and triggers manual review for high-risk operations in healthcare scenarios, significantly enhancing policy adherence, execution consistency, and deployment safety.

In multi-stakeholder platforms, software architecture decisions often implicitly entrench conflicting requirements without systematic support for mapping governance principles to technical design. This work proposes the first governance-architecture alignment framework, explicitly linking five core governance principles to the space of architectural decisions, thereby rendering implicit governance stances identifiable and contestable. The framework also exposes how default technical choices can obscure underlying value commitments. Feasibility is preliminarily demonstrated through a constructive case study of a pig-farming knowledge platform in Rwanda. Future work will employ pre- and post-intervention user judgment studies to evaluate the framework’s impact on actual governance outcomes.

This study addresses the challenges of open-source software governance, where ambiguously defined roles and permissions often lead to unclear accountability and excessive burdens on core maintainers. For the first time, it systematically analyzes governance documents such as GOVERNANCE.md in GitHub projects, applying institutional grammar to structurally dissect roles in terms of their scope, authority, obligations, and lifecycle. The research uncovers a phenomenon termed “role drift” and identifies the “maintainer paradox”: while core contributors foster community engagement, they frequently become bottlenecks in governance. Empirical findings reveal substantial variation in responsibilities among identically named roles across projects and demonstrate that a small number of individuals often concentrate technical, managerial, and community-facing functions. These insights provide critical foundations for improving role design and enhancing the sustainability of open-source communities.

This work addresses the misalignment between capability boundaries and governance boundaries in current AI systems, which engenders uncontrolled risks and renders formal regulatory mechanisms ineffective. To resolve this, the paper introduces a “coterminous governance” framework that mandates strict alignment between these boundaries. Leveraging Rice’s theorem, it proves that behavioral governance is undecidable under Turing-complete architectures, thereby necessitating governance to be intrinsically embedded within system design rather than imposed ex post facto. The authors realize this principle through an architecture that decouples computation from effect, integrating governance checks directly into the execution pipeline instead of relying on a separate oversight layer. Using Coq-based formal verification—encompassing 454 theorems across 36 modules—the study establishes coterminous governance as a necessary criterion for verifiable AI governance systems.

This study addresses the pervasive lack of structural integrity in current AI governance documents, which often fail to meet critical requirements such as traceability, dynamic re-verification, and objective evidence. To bridge this gap, the work systematically adapts structural governance principles from aviation software certification standards (DO-178C/DO-330) and proposes a novel integrity framework tailored for static AI governance artifacts. The framework introduces three key concepts—“epoch constraints,” “proof surfaces,” and “structural gaps”—and establishes the seven-principle PromptQ system. Structural analysis of mainstream governance documents reveals that 37% fall below a basic quality threshold, thereby demonstrating the framework’s effectiveness and practicality in enhancing the rigor and verifiability of AI governance documentation.

This study addresses the challenges of collaboration and quality control in open-source deep learning projects stemming from inadequate governance mechanisms. Drawing on the Institutional Analysis and Development (IAD) framework, it employs a mixed-methods empirical approach combining document content analysis and code commit tracking across PyTorch, TensorFlow, and PaddlePaddle. The analysis encompasses 109 governance documents and over 1,700 code commits, systematically uncovering the structure, temporal evolution, and functional dimensions of governance rules. The research identifies 17 rule themes and 7 rule types, revealing a distinct evolutionary pattern wherein operational rules emerge early and undergo frequent revisions, while structural rules appear later and evolve more steadily. Four core governance functions are distilled, culminating in 33 actionable recommendations for effective open-source AI project governance.