DAO-AI: Evaluating Collective Decision-Making through Agentic AI in Decentralized Governance

2025-10-23

AI Summary

This study investigates the effectiveness and consistency of autonomous AI agents in collective decision-making within decentralized autonomous organizations (DAOs), addressing whether AI can reliably substitute for or augment human participants in on-chain governance. Method: We design a modular AI agent system—built upon the Agentics framework and MCP workflow—that integrates NLP, on-chain data analytics, and verifiable historical retrieval to enable proposal comprehension, retrieval of past discussion contexts, and independent voting. The system is empirically evaluated across 3,000+ real-world on-chain protocol proposals in a financial simulation environment. Contribution/Results: This work presents the first systematic validation of AI agents as independent governance participants, introducing an interpretable, auditable, and data-driven voting signal generation mechanism. Empirical results demonstrate high alignment between AI-generated votes and both human consensus and token-weighted outcomes, confirming the agents’ reliability, transparency, and trustworthiness in supporting real-world DAO decision-making.

Technology Category

• Humans and AI: Voting
• Multiagent Systems: Mechanism Design
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search

Abstract

This paper presents a first empirical study of agentic AI as autonomous decision-makers in decentralized governance. Using more than 3K proposals from major protocols, we build an agentic AI voter that interprets proposal contexts, retrieves historical deliberation data, and independently determines its voting position. The agent operates within a realistic financial simulation environment grounded in verifiable blockchain data, implemented through a modular composable program (MCP) workflow that defines data flow and tool usage via Agentics framework. We evaluate how closely the agent's decisions align with the human and token-weighted outcomes, uncovering strong alignments measured by carefully designed evaluation metrics. Our findings demonstrate that agentic AI can augment collective decision-making by producing interpretable, auditable, and empirically grounded signals in realistic DAO governance settings. The study contributes to the design of explainable and economically rigorous AI agents for decentralized financial systems.

Problem

• Evaluating AI agents as autonomous decision-makers in decentralized governance
• Measuring alignment between AI voting decisions and human outcomes
• Developing explainable AI for collective decision-making in financial systems

Innovation

• Agentic AI voter interprets proposals autonomously
• Modular composable program workflow defines data flow
• Financial simulation environment uses verifiable blockchain data