Application and Exploration of AI Agent Technology in the Web3 Field

Recently, a general-purpose AI Agent product named Manus has attracted widespread attention. As an AI agent capable of independent thinking, planning, and executing complex tasks, Manus demonstrates unprecedented versatility and execution ability, providing new ideas and inspiration for the development of AI Agents. With the rapid development of AI technology, AI Agents, as an important branch of artificial intelligence, are gradually moving from theory to practice, showing enormous application potential in various industries, including the Web3 sector.

Basic Concepts of AI Agent

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:

• Large Language Model (LLM) as the "brain"
• Observation and Perception Mechanism
• Reasoning process
• Action Execution Ability
• 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 capabilities. Among them, the ReAct pattern is currently the most widely used design pattern, and its typical process can be described as a cycle of Thinking (Thought) → Acting (Action) → Observing (Observation).

According to the number of agents, AI Agents can be divided into Single Agent and Multi Agent. Single Agent focuses on the combination of LLM and tools, while Multi Agent assigns different roles to different agents, completing complex tasks through collaborative effort.

The Current State of AI Agents in the Web3 Field

In the Web3 industry, the popularity of AI Agents peaked in January this year and has since declined. Currently, the main exploration directions include:

1. Launch Platform Mode: such as Virtuals Protocol, allows users to create, deploy, and monetize AI Agents.
2. DAO Model: Such as ElizaOS, utilizing AI models combined with suggestions from DAO members to make decisions.
3. Business Company Model: Such as Swarms, providing an enterprise-level Multi-Agent framework.

From the perspective of economic models, currently only the launch platform model can achieve a self-sufficient economic closed loop. However, this model also faces the problem of AI agents lacking intrinsic value support.

The Combination of MCP and Web3

The emergence of Model Context Protocol (MCP) has brought new exploration directions for AI Agents in Web3:

1. Deploy the MCP Server to the blockchain network to solve the single point problem and have censorship resistance.
2. Empower the MCP Server with the ability to interact with the blockchain, reducing the technical barrier.
3. Build an Ethereum-based OpenMCP.Network creator incentive network.

Although these directions can theoretically inject decentralized trust mechanisms and economic incentives into AI Agents, they still face challenges in technical implementation and efficiency.

Looking to the Future

The integration of AI and Web3 is an inevitable trend. Although there are still challenges in terms of technology and application, we have reason to believe that AI Agents will play an increasingly important role in the Web3 space as technology continues to advance. In the future, we look forward to seeing more groundbreaking applications that drive the development and innovation of the Web3 ecosystem.