AI Agent's Cross-Boundary Exploration in the Web3 Field

Recently, a startup company in China launched the world's first universal AI Agent product, which has sparked heated discussions in the tech community. On the first day of its launch, the invitation code was in high demand. This product has the ability to autonomously complete tasks from planning to execution, demonstrating unprecedented versatility and execution capability, providing valuable product ideas and design inspiration for AI Agent development.

With the rapid development of AI technology, AI Agents, as an important branch of artificial intelligence, are gradually moving from concept to reality and demonstrating enormous application potential across various industries, including the Web3 sector.

Overview of AI Agent

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

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

The design patterns of AI Agents mainly have two development paths: one focuses on planning capabilities, while the other emphasizes reflection capabilities. Among them, the ReAct pattern is currently the most widely used design pattern, and its typical process is the cycle of thinking (Thought) → action (Action) → observation (Observation).

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

Current State of AI Agents in Web3

The popularity of AI Agents in the Web3 industry peaked earlier this year and then saw a significant decline, with the overall market value shrinking by more than 90%. Currently, the projects with the largest buzz and market value are still those exploring Web3 around the AI Agent framework, which mainly consists of three models:

1. Launch Platform Mode: Allows users to create, deploy, and monetize AI Agents.
2. DAO Model: Utilize AI models in conjunction with DAO member suggestions for investment decisions.
3. Business Company Model: Provides an enterprise-level Multi-Agent framework.

From the perspective of the economic model, currently only launch platforms can achieve a self-sufficient economic closed loop. However, this model also faces challenges, as the assets to be issued must have "attractiveness" in order to form a positive flywheel.

Integration of MCP Protocol and Web3

Model Context Protocol (MCP) is an open-source protocol designed to address the connection and interaction issues between LLMs and external data sources. The emergence of MCP brings new exploration directions for AI Agents in Web3:

1. Deploy the MCP Server to the blockchain network to solve the single point problem and possess anti-censorship capabilities.
2. Empower the MCP Server to interact with the blockchain, reducing the technical barrier.

In addition, there is a plan for a creator incentive network called OpenMCP.Network built on Ethereum. This network will use smart contracts to achieve automation, transparency, trust, and censorship resistance in incentives.

Outlook

Although the combination of MCP and Web3 theoretically injects decentralized trust mechanisms and economic incentives into AI Agent applications, the current zero-knowledge proof technology still struggles to verify the authenticity of Agent behavior, and decentralized networks also face efficiency issues. This is not a solution that can succeed in the short term.

The integration of AI and Web3 is an inevitable trend. We need to maintain patience and confidence, continuously explore, and look forward to the emergence of a milestone product that breaks the external doubts about the practicality of Web3.