Exploration of AI Agent Development in the Web3 Field

Recently, a Chinese startup launched the world's first universal AI Agent product, which has attracted widespread attention. This product has the capability to autonomously complete tasks from planning to execution, demonstrating unprecedented versatility and execution ability. The explosive popularity of this product has not only garnered attention within the industry but also provided valuable product ideas and design inspiration for various AI Agent developments.

AI Agents, as an important branch of artificial intelligence, are gradually transitioning from concept to reality and demonstrating huge application potential in various industries, including the Web3 sector. The core components of AI Agents include large language models as their "brain", observation and perception mechanisms, reasoning and thinking processes, action execution, and memory and retrieval.

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 mode is currently the most widely used design pattern, and its typical process is a cycle of thinking, acting, and observing.

According to 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.

In the Web3 industry, the development of AI Agents mainly focuses on three models: Launch Platform Model, DAO Model, and Commercial Company Model. Among them, the Launch Platform Model is currently the only model that can achieve a self-sustaining economic closed loop. However, this model also faces the problem of insufficient asset attractiveness.

The emergence of Model Context Protocol (MCP) has brought new exploration directions for AI Agents in Web3. One direction is to deploy the MCP Server on the blockchain network, addressing single point issues and possessing censorship resistance. Another direction is to enable the MCP Server to interact with the blockchain, lowering the technical barriers.

In addition, there are proposals for building an OpenMCP.Network creator incentive network based on Ethereum. This network will require the use of smart contracts to achieve automation, transparency, trustworthiness, and resistance to censorship in incentives.

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

The integration of AI and Web3 is an inevitable trend. Although there are still many challenges at present, we need to maintain patience and confidence, continuously exploring the possibilities in this field. The Web3 world still needs a milestone product to break the skepticism about the practicality of Web3.