Development and Exploration of AI Agents in the Web3 Field

The explosive popularity of the General AI Agent product Manus has sparked widespread attention within the industry towards AI Agent technology. As an important branch of artificial intelligence, AI Agents are gradually moving from concept to real-world applications, demonstrating immense potential across various industries, including the Web3 sector.

Core Technology of AI Agent

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

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

The design patterns of AI Agents mainly have two development routes: one focuses on planning capabilities, such as REWOO, Plan & Execute, etc.; the other emphasizes reflective capabilities, such as Basic Reflection, Reflexion, etc. Among them, the ReAct model is the most widely used, and its typical process is the cycle of thinking (Thought) → action (Action) → observation (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 to collaboratively complete complex tasks.

The Current State of AI Agents in Web3

The popularity of AI Agents in the Web3 industry peaked in January this year and has since significantly declined, with the overall market value shrinking by more than 90%. Currently, the main projects still generating buzz are those exploring Web3 around the AI Agent framework, which can be categorized into three types:

1. Launch platform mode: represented by Virtuals Protocol, allows users to create, deploy, and monetize AI Agents.

2. DAO Model: Represented by ElizaOS, it uses AI models to simulate investment decisions, combining suggestions from DAO members for investments.

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 self-sufficient economic closed loop. However, this model also faces challenges such as insufficient asset attractiveness and a lackluster market.

The Combination of MCP Protocol 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 single point issues and ensure censorship resistance.

2. Empower the MCP Server to interact with the blockchain, such as conducting DeFi transactions and management.

3. Build an Ethereum-based OpenMCP.Network creator incentive network, achieving automation, transparency, and trust in incentives through smart contracts.

However, these solutions still face technical challenges, such as the difficulty of verifying the authenticity of Agent behavior using zero-knowledge proof technology, and efficiency issues in decentralized networks.

Outlook

The integration of AI and Web3 is an inevitable trend. Although AI Agents in Web3 currently face many challenges, with continuous technological advancements, it is believed that milestone products will emerge in the future, breaking the skepticism about the practicality of Web3. We need to maintain patience and confidence, continuously exploring the applications and developments of AI Agents in the Web3 field.