Autonomous AI agents in decentralized finance: Market dynamics, application areas, and theoretical implications

This paper investigates the intersection of artificial intelligence (AI) agents—autonomous software entities capable of adapting, learning, and executing multi-step operations—and decentralized finance (DeFi) ecosystems. It highlights how the adaptive decision-making capabilities, flexible governance frameworks, and data-driven optimization strategies of AI agents reshape market coordination and organizational architectures. Drawing on a qualitative analysis of 306 major crypto AI agents, the study introduces a typology that maps their diverse application areas, including algorithmic trading, portfolio management, sentiment-driven communities, and immersive entertainment. To further conceptualize the role of AI in decentralized governance, the paper develops a quadrant-based framework that distinguishes four archetypal system configurations: Traditional Decentralized Autonomous Organization (DAO) Tools, Maximally Distributed Agency, Closed Systems, and AI Dictatorships. These configurations, defined by varying degrees of autonomy and decentralization, reveal critical trade-offs between transparency, efficiency, adaptability, and control. This framework serves as a lens to theorize how AI agents reconfigure trust mechanisms, power dynamics, and decision-making processes in decentralized ecosystems. Grounded in economic and socio-technical theory, the paper positions AI agents as transformative intermediaries in tokenized environments. While demonstrating their capacity to streamline operations, enhance decision quality, and enrich user engagement, the study also addresses the governance risks posed by algorithmic control and systemic opacity. Taken together, the conceptual and empirical insights lay a foundation for ongoing interdisciplinary inquiry into the evolving role of AI agents in decentralized finance. • Introduces a typology of 306 AI agents across key DeFi application areas • Maps AI agent roles in trading, governance, community, and entertainment • Develops a governance framework for AI agent autonomy and decentralization • Shows how AI agents reduce transaction costs and reshape market structures • Highlights risks of opacity, misalignment, and centralization in DeFi AI use