# AI Agents Meet Blockchain: A Survey on Secure and Scalable Collaboration for Multi-Agents
## 1 Introduction
### Motivation and Contribution  
Blockchain serves as a decentralized and tamper-proof ledger to ensure secure and immutable transaction records among AI agents [23; 24]. By integrating blockchain, AI agents benefit from enhanced trust through immutable records to ensure that interactions are transparent and verifiable. Additionally, blockchain provides strong data security by using cryptographic techniques, which reduces the risk of tampering or unauthorized data access. This is critical for maintaining the integrity and reliability of autonomous agent operations. Its integration into agent-based environments enables reliable interactions across various functions, such as payments, ownership transfers, and contractual agreements. Moreover, the integration effectively addresses key challenges, such as privacy [17], accountability [25], and data persistence [26], to advance trust and verifiability in autonomous agent operations. Furthermore, it enhances the reliability and transparency of agent-driven systems and supports the development of decentralized intelligence and autonomy. Despite the importance of blockchain integration with AI agents and vice versa, existing studies [8; 23; 24; 26; 27; 28] did not emphasize the challenges of integrating these technologies, such as scalability, interoperability, and privacy concerns. To address these gaps, we offer an integrated and in-depth analysis that covers both the blockchain's role in ensuring AI trustworthiness and the potential of blockchain in enhancing AI agents' capabilities. Specifically, our contributions include a thorough discussion of blockchain's role as a reliable ledger and its utility in intelligent consensus mechanisms that facilitate autonomous decision making by AI agents. Additionally, we provide a detailed exploration of zero-shot learning for blockchain operation, which refers to the ability of AI agents to generalize and perform new tasks without prior specific training data. This approach is important for blockchain operation because it enhances AI agents' adaptability to dynamic and unpredictable blockchain environments and enables them to handle new scenarios more effectively. We also categorize and examine practical application scenarios, such as DeFi, smart cities and governance, and the Internet of Things (IoTs) [29], to offer insights into real-world use cases where the intersection of AI and blockchain holds transformative potential. Furthermore, we outline future directions to address ongoing challenges in scalability and performance, data privacy and security, and interoperability and standardization. We also highlight the emerging trends and open research questions, which are critical for both academic and industrial communities. Note that the term \"decentralized\" is used throughout the survey, which refers blockchain, Web3, and DeAI ecosystem. The summarized contributions of our survey are as follows:  
* We explore how AI agents enhance blockchain systems, with a focus on their ability to improve intelligent decision making, optimize consensus mechanisms, and enable zero-shot learning. The result of these capabilities is an increase in adaptability, efficiency, and resilience in decentralized environments. * We investigate how blockchain enhances the capabilities of AI agents with the provision of a secure and decentralized infrastructure that supports coordination, governance, and data sharing. The integration leads to strengthened autonomy and scalability for AI agents alongside the assurance of privacy and reliability in collaborative environments. * We categorize various application and use case scenarios in DeFi, supply chain management, DAOs, edge computing, and fault management. These examples demonstrate improvements in operational efficiency, trust, and transparency while enhancing the adaptability and decision-making capabilities of AI agents in decentralized environments.