Exploration of AI Agent Applications in the Web3 Field: From Manus to MC

Recently, a globally pioneering universal AI Agent product called Manus has attracted widespread attention in the tech circle. Developed by a Chinese startup, the product experienced a hot scene on its launch day, with invitation codes being hard to come by. As a universal AI Agent, Manus demonstrates the ability to think independently, plan, and execute complex tasks, providing new ideas for AI Agent development.

An AI Agent is a computer program that can autonomously make decisions and execute tasks based on its environment, input, and predefined goals. Its core components include large language models, observation and perception mechanisms, reasoning and thinking processes, action execution, and memory retrieval functions. The design patterns of AI Agents mainly have two development paths: one emphasizes planning capabilities, while the other emphasizes reflective capabilities.

In the Web3 field, the application of AI Agents mainly focuses on three models: Launch Platform Model, DAO Model, and Business Company Model. Among them, the Launch Platform Model allows users to create, deploy, and monetize AI Agents, with Virtuals Protocol being a well-known example. The DAO Model is represented by ElizaOS, which aims to build a community for AI Agent developers. The Business Company Model is exemplified by Swarms, providing an enterprise-level Multi-Agent framework.

However, current AI Agent projects in the Web3 field generally face challenges in the sustainability of their economic models. Most projects lack intrinsic value support, making it difficult to form a positive economic cycle.

The emergence of Model Context Protocol (MCP) brings new exploration directions for AI Agents in Web3. It mainly includes two aspects: first, deploying the MCP Server to the blockchain network to achieve decentralization and censorship resistance; second, empowering the MCP Server with the ability to interact with the blockchain, lowering the technical threshold. In addition, there is the idea of building an OpenMCP.Network creator incentive network based on Ethereum.

Although the combination of MCP and Web3 can theoretically inject decentralized trust mechanisms and economic incentives into AI Agent applications, there are still many challenges with current technology. For example, the difficulty of verifying the authenticity of Agent behavior using zero-knowledge proof technology, and the efficiency issues of decentralized networks also need to be addressed.

The integration of AI and Web3 is an inevitable trend, but it still requires time and continuous exploration. In the future, we look forward to seeing more groundbreaking applications that promote the development of AI Agents in the Web3 field.