The Cross-Border Exploration of AI Agents in the Web3 Field

Recently, a startup company in China launched the world's first universal AI Agent product, causing a stir in the tech circle. This product has the capability to autonomously complete tasks from planning to execution, demonstrating unprecedented versatility and execution power. This has not only attracted widespread attention within the industry but also provided valuable product ideas and design inspiration for various 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, showcasing huge application potential across various industries, including the Web3 sector.

Overview of AI Agent

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

The design patterns of AI Agents mainly have two development routes: one focuses on planning ability, while the other emphasizes reflective ability. Among them, the ReAct model is the earliest and most widely used design pattern. ReAct solves diverse language reasoning and decision-making tasks by integrating reasoning (Reasoning ) and acting (Acting ) within language models. Its typical process can be described as the cycle of thinking (Thought ) → acting (Action ) → observing (Observation ).

Based on the number of agents, AI Agents can be divided into Single Agent and Multi Agent. The core of Single Agent lies in the combination of 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 attention on AI Agents in the Web3 industry peaked earlier this year and has since declined significantly, with the overall market value shrinking by more than 90%. Currently, projects with significant buzz and market value are still exploring Web3 around the AI Agent framework, primarily including three models:

1. Launchpad Mode: represented by the 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, combined with suggestions from DAO members for investment.

3. Business Company Model: Represented by Swarms, it provides an enterprise-level Multi-Agent framework.

From the perspective of economic models, currently only the launch platform model can achieve a self-sustaining economic closed loop. However, this model also faces challenges, as the assets to be issued must possess sufficient attractiveness to create a positive feedback loop.

MCP's Exploration of 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 have censorship resistance.

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

3. Build an Ethereum-based OpenMCP.Network creator incentive network, achieving automation, transparency, trustworthiness, and censorship resistance of incentives through smart contracts.

Although the integration of MCP with Web3 can theoretically inject decentralized trust mechanisms and economic incentives into AI Agent applications, there are currently some limitations in the technology, such as the difficulty of verifying the authenticity of Agent behavior through zero-knowledge proof technology and the efficiency issues of decentralized networks.

Conclusion

The application of AI Agents in the Web3 field is still in the exploratory stage, and a milestone product is needed to break the skepticism surrounding the practicality of Web3. The emergence of MCP brings new opportunities and challenges for AI Agents in Web3. The integration of AI and Web3 is an inevitable trend, and we need to maintain patience and confidence while continuously exploring the potential of this field.