Decentralized AI Agents and Blockchain: Architectures, Coordination Mechanisms, and Governance Frameworks

Abstract

Autonomous AI agents capable of holding digital assets, signing transactions, and executing smart contracts on public blockchain networks have moved from research prototypes to active deployment over the past two years. Despite this pace of adoption, no systematic treatment of their architecture, coordination protocols, and governance structures exists that spans the full design space. This survey addresses that gap through a systematic review of the literature from 2019 to 2026, covering 177 peer-reviewed publications and 14 system documentation sources, identified through a structured search of IEEE Xplore, the ACM Digital Library, Scopus, and arXiv. We classify deployed and proposed systems along four architectural dimensions: on-chain execution, off-chain agents with on-chain settlement, verifiable off-chain computation, and multi-agent on-chain interaction. Then, we examine the coordination mechanisms through which agents reach collective decisions, covering auction-based protocols, cooperative multi-agent reinforcement learning, token-incentive structures, and gossip-based peer-to-peer coordination. Governance is treated as a distinct dimension, analysed through a technical lens, covering on-chain parameter control, dispute resolution, and DAO structures, and an organizational one, covering accountability, incentive alignment, principal–agent dynamics, and regulatory compatibility). We survey applications across decentralized finance, supply chain, IoT, and agent marketplace domains, and identify six open research problems whose resolution is a prerequisite for broader deployment. The convergence of mechanism design and multi-agent reinforcement learning in asynchronous blockchain environments is identified as the direction of greatest near-term research value.