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

TL;DR

This paper empirically evaluates agentic AI as autonomous voters in decentralized governance, demonstrating their potential to enhance decision-making with interpretable and auditable signals grounded in blockchain data.

Contribution

It introduces a novel framework for deploying agentic AI in DAO voting, including a modular workflow and evaluation metrics for alignment with human and token-weighted outcomes.

Findings

Agentic AI decisions strongly align with human and token-weighted outcomes.

The AI provides interpretable and auditable signals in DAO governance.

The study advances explainable AI in decentralized financial systems.

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.