Development and Application Exploration of AI Agents in the Web3 Field

In early March, a globally pioneering universal AI Agent product developed by a Chinese startup attracted widespread attention. This product possesses the ability to think independently, plan, and execute complex tasks, demonstrating unprecedented versatility and execution capability. This has not only sparked heated discussions within the industry but also provided valuable product ideas and design inspiration for various AI Agent developments. With the rapid advancement of AI technology, AI Agents, as an important branch of artificial intelligence, are gradually transitioning from concept to reality, showcasing tremendous application potential across various industries, including the Web3 sector.

Overview of AI Agent

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

• Large Language Models (LLM) as its "brain"
• Observation and Perception Mechanism
• Reasoning process
• Action Execution
• Memory and Retrieval

The design patterns of AI Agents mainly have two development paths: one focuses on planning ability, while the other emphasizes reflection ability. Among them, the ReAct model is currently the 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. 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.

Current Status of AI Agents in Web3

In the Web3 industry, while the market value of AI Agent-related projects has significantly shrunk, there are still some projects exploring the applications of AI Agents. The main models include:

1. Launchpad mode: Allows users to create, deploy, and monetize AI Agents. Representative projects include Virtuals Protocol.

2. DAO Model: Utilize AI models combined with DAO member suggestions for decision-making. Representative project includes ElizaOS.

3. Business Company Model: Provides an enterprise-level Multi-Agent framework. Representative projects include Swarms.

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 issue of insufficient asset attractiveness, especially in the current market environment.

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 achieve decentralization and anti-censorship.

2. Empower the MCP Server with the ability to interact with the blockchain, reducing the technical barriers for DeFi operations.

3. Build an Ethereum-based OpenMCP.Network creator incentive network to provide sustainable economic incentives for MCP Server providers.

Although the combination of MCP and Web3 theoretically injects decentralized trust mechanisms and economic incentives into AI Agent applications, current technology still faces several challenges, such as the difficulty of verifying the authenticity of Agent behavior with zero-knowledge proof technology and the efficiency issues of decentralized networks.

Conclusion

The application of AI agents in the Web3 field, although facing many challenges, is still a direction full of potential. With continuous technological advancements and the exploration of innovative models, we have reason to believe that the integration of AI and Web3 will bring more groundbreaking applications. In this process, maintaining patience and confidence, and continuously exploring and innovating will be key to promoting the development of this field.