Exploration and Application of AI Agents in the Web3 Field

Recently, a global first universal AI Agent product named Manus has caused a sensation in the domestic tech circle. 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 concept to real-world application, showing great potential in various industries, and the Web3 industry is no exception.

Overview of AI Agent

AI Agent is a computer program that can autonomously make decisions and perform tasks based on the environment, input, and predefined goals. Its core components include:

1. Large language models (LLM) as the "brain"
2. Observation and Perception Mechanism
3. Reasoning Process
4. Action Execution Capability
5. Memory and Retrieval Function

The design patterns of AI Agents mainly have two development paths: one emphasizes planning ability, while the other emphasizes reflective ability. Among them, the ReAct pattern is currently the most widely used design pattern, and its typical process can be described by the "Think → Act → Observe" cycle.

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

The Current State of AI Agents in Web3

The popularity of AI Agents in the Web3 industry peaked earlier this year but has since declined; however, some projects still maintain a high level of attention, primarily focusing on the following models:

1. Launch Platform Mode: Represented by Virtuals Protocol, it allows users to create, deploy, and monetize AI Agents.
2. DAO Model: Represented by ElizaOS, it makes decisions based on AI models and suggestions from DAO members.
3. Business company model: represented by Swarms, providing an enterprise-level Multi Agent framework.

From the perspective of economic models, currently only the launch platform model can achieve a relatively self-sufficient economic closed loop. However, this model also faces the problem of the assets themselves lacking intrinsic value support, which can easily lead to a rapid decline to zero.

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 enhance censorship resistance.
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.

Although these directions can theoretically inject decentralized trust mechanisms and economic incentives into AI Agents, there are still many challenges in technical implementation, such as the difficulty of verifying the authenticity of Agent behavior using zero-knowledge proof technology and efficiency issues in decentralized networks.

Conclusion

The integration of AI and Web3 is an inevitable trend. Although there are still many technical and application challenges at present, the future is promising. We need to maintain patience and confidence, continuously explore the application potential of AI agents in the Web3 field, with the aim of creating truly milestone products that change the external skepticism about the practicality of Web3.