# AI Agents Meet Blockchain: A Survey on Secure and Scalable Collaboration for Multi-Agents
## 3 Preliminaries
### AI Agents  
AI agents play a crucial role in decentralized environments, enabling intelligent decision making, automation, and adaptability without relying on centralized authorities [20; 30]. Technically, an AI agent is a computational entity that perceives its environment through various data inputs, processes this information, and takes action using actuators (mechanisms that execute actions) to maximize its chances of success at achieving specific goals. AI agents use a combination of AI techniques, including reinforcement learning (RL), federated learning (FL), and natural language processing (NLP), to autonomously adapt, learn, and make decisions in complex settings.  
AI agents are similar to several related terms, but there are subtle distinctions:  
* Autonomous Agents [16; 31]: While all AI agents are autonomous, not all autonomous agents use AI. Autonomous agents are systems that operate independently based on pre-programmed rules or logic, whereas AI agents utilize learning and adaptability to make informed decisions.
* Multi-Agent Systems (MAS) [32; 33]: A multi-agent system consists of multiple AI agents working collaboratively or competitively to achieve individual or shared objectives. In MAS, agents can communicate and negotiate with each other, which adds a layer of complexity compared with single-agent AI systems.
* Agent AI [15; 31]: This term is often used interchangeably with AI agents but generally refers to the broader concept of agents powered by AI to execute tasks. It emphasizes the AI technology aspect of the agent.
* Intelligent Agents [34; 35]: These are agents equipped with AI capabilities to perceive, reason, and act in an environment to achieve certain goals. AI agents fall under this category, highlighting their intelligent capabilities such as learning, problem solving, and decision making.  
The distinctions among these terms help clarify the roles and capabilities of AI agents within various environments, particularly in decentralized settings where autonomous decision making and collaboration are crucial.  
As shown in Figure 1, the integration of LLMs has significantly revolutionized the capabilities of AI agents [15; 31]. While RL and FL have been instrumental in developing adaptive and distributed learning systems, the incorporation of LLMs has driven AI agents into a new realm of cognitive abilities and task execution. For example, in the healthcare domain, AI agents equipped with LLMs can assist in diagnosing medical conditions by analyzing patient data and providing detailed insights, significantly improving accuracy and efficiency. In finance, AI agents use LLMs to predict market trends, automate trading strategies, and optimize investment decisions, thereby reducing human intervention and enhancing profitability. LLMs (e.g., GPT-4) serve as advanced policy instructors to enhance the decision-making capabilities of RL agents. Another example is the ReAct (Reasoning and Acting) framework, which demonstrates how LLMs can facilitate executing complicated sequential decision-making tasks by integrating both reasoning and action into their processes [16]. This integration enables agents to think about their actions and adapt accordingly, improving flexibility and efficacy in dynamic settings. As a result,ReAct allows for more nuanced and context-aware responses compared with typical RL or FL approaches.  
In our survey paper, our primary focus is on GenAI and LLM-based agents, as they represent the forefront of intelligent and adaptable systems in decentralized environments. However, we also include a comprehensive discussion of other AI agents to provide a holistic view of the evolving landscape of decentralized intelligence.