This paper introduces a structured approach to improving decision making in Decentralized Autonomous Organizations (DAO) through the integration of the Question-Option-Criteria (QOC) model and AI agents. We outline a stepwise governance framework that evolves from human led evaluations to fully autonomous, AI-driven processes. By decomposing decisions into weighted, criterion based evaluations, the QOC model enhances transparency, fairness, and explainability in DAO voting. We demonstrate how large language models (LLMs) and stakeholder aligned AI agents can support or automate evaluations, while statistical safeguards help detect manipulation. The proposed framework lays the foundation for scalable and trustworthy governance in the Web3 ecosystem.

翻译：本文提出了一种通过整合问题-选项-标准（QOC）模型与人工智能代理来改进去中心化自治组织（DAO）决策的结构化方法。我们概述了一个从人类主导评估逐步演进至完全自主、人工智能驱动过程的渐进式治理框架。通过将决策分解为基于加权标准的评估，QOC模型提升了DAO投票的透明度、公平性和可解释性。我们展示了大型语言模型（LLMs）以及与利益相关者对齐的人工智能代理如何支持或自动化评估过程，同时统计保障机制有助于检测操纵行为。所提出的框架为Web3生态系统中可扩展且可信的治理奠定了基础。