The Development and Exploration of AI Agents in the Web3 Field

Recently, a universal AI Agent product called Manus has attracted widespread attention. As the world's first product of its kind, Manus demonstrates strong abilities in independent thinking, planning, and executing complex tasks, providing valuable product ideas and design inspiration for the development of AI Agents. With the rapid advancement of AI technology, AI Agents, as an important branch of artificial intelligence, are gradually transitioning from concept to reality, showcasing immense application potential across various industries, including the Web3 industry.

An AI Agent is a computer program that can make decisions and execute tasks autonomously based on the environment, inputs, and predefined goals. Its core components include a large language model (LLM) as the "brain", observation and perception mechanisms, reasoning thinking processes, action execution, and memory and retrieval functions.

The design patterns of AI Agents mainly have two development routes: one focuses on planning capabilities, while the other emphasizes reflective abilities. Among them, the ReAct pattern is the earliest and most widely used design pattern, and its typical process can be described as a cycle of "Think → Act → Observe."

According to the number of agents, AI Agents can be divided into Single Agent and Multi Agent. The core of Single Agent lies in the combination of LLM and tools, while Multi Agent assigns different roles to different agents, completing complex tasks through collaborative cooperation.

Model Context Protocol (MCP) is an open-source protocol designed to address the connection and interaction issues between LLMs and external data sources. It provides three capabilities to extend LLMs: knowledge expansion, function call execution, and pre-written prompt templates.

In the Web3 industry, the development of AI Agents has experienced ups and downs. Currently, the main exploration directions include launch platform models, DAO models, and commercial company models. Among them, launch platform models such as Virtuals Protocol allow users to create, deploy, and monetize AI Agents; DAO models like ElizaOS are dedicated to building a community for AI Agent developers; and commercial company models such as Swarms provide enterprise-level Multi-Agent frameworks.

The emergence of MCP has brought new exploration directions for Web3 AI Agents. One possible direction is to deploy the MCP Server on blockchain networks, addressing single point issues and possessing anti-censorship capabilities. Another direction is to enable the MCP Server to interact with the blockchain, lowering the technical barriers. Additionally, there is the idea of building a creator incentive network called OpenMCP.Network based on Ethereum.

Although the integration of Web3 and AI faces many challenges, such as the immaturity of zero-knowledge proof technology and the efficiency issues of decentralized networks, this integration is an inevitable trend. We need to maintain patience and confidence, continuously exploring the applications and development of AI Agents in the Web3 field.