Exploration and Development of AI Agents in the Web3 Field

Recently, a global first universal AI Agent product named Manus has attracted widespread attention. Developed by the Chinese startup Monica, this product demonstrates strong capabilities in independent thinking, planning, and executing complex tasks, providing new ideas and inspiration for the development of AI Agents.

AI Agent Overview

An AI Agent is a computer program capable of autonomously making decisions and executing tasks based on the environment, input, and predefined goals. Its core components include a large language model (LLM) as the "brain", observation and perception mechanisms, reasoning processes, action execution, and memory and retrieval functions.

The design patterns of AI Agent mainly have two development routes: one emphasizes planning ability, while the other emphasizes reflective ability. Among them, the ReAct pattern is currently the most widely used design pattern, with a typical process that includes three steps: thinking, acting, and observing, forming a cyclical process.

According to the number of agents, AI Agents can be divided into Single Agent and Multi Agent. Single Agents mainly rely on the combination of LLM and tools, while Multi Agents complete complex tasks through collaboration among Agents with different role positioning.

The Current State of AI Agents in Web3

In the Web3 industry, the development of AI Agents has experienced fluctuations. Currently, prominent exploration directions include:

1. Launchpad Mode: represented by Virtuals Protocol, allows users to create, deploy, and monetize AI Agents.
2. DAO Model: Represented by ElizaOS, combining the concept of decentralized autonomous organizations.
3. Business Company Model: Represented by Swarms, providing an enterprise-level Multi-Agent framework.

From the perspective of economic models, the launch platform model is currently the most likely to achieve a self-sustaining economic loop. However, this model also faces the challenge of insufficient asset attractiveness, especially in the current market environment.

The Integration 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 achieve decentralization and anti-censorship capabilities.
2. Empower the MCP Server with the ability to interact with the blockchain, lowering the technical barrier.
3. Build an Ethereum-based OpenMCP.Network creator incentive network.

These directions, although theoretically capable of injecting decentralized trust mechanisms and economic incentives into AI Agent applications, still face challenges in terms of technical implementation and efficiency.

Outlook

The integration of AI and Web3 is an inevitable trend. Although there are still many challenges at present, continuous exploration and innovation will drive the development of this field. We need to maintain patience and confidence, looking forward to the emergence of a milestone product that can break external doubts and demonstrate the practicality of Web3.