DAO-AI: Evaluating Collective Decision-Making through Agentic AI in Decentralized Governance
It addresses the design of explainable and economically rigorous AI agents for decentralized financial systems, though it is incremental as it applies existing methods to new data.
This paper tackles the problem of evaluating agentic AI as autonomous decision-makers in decentralized governance by analyzing over 3K proposals from major protocols, finding strong alignments between the agent's decisions and human or token-weighted outcomes.
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.