AI Agent: A Cross-Domain Exploration from Manus to Web3

Recently, the Chinese startup Monica launched Manus, the world's first universal AI Agent product, which has attracted widespread attention. As an AI Agent capable of independent thinking, planning, and executing complex tasks, Manus demonstrates unprecedented versatility and execution capability, providing valuable product ideas and design inspiration for AI Agent development.

AI Agents, as an important branch of artificial intelligence, are moving from concept to real-world applications. They are computer programs capable of making decisions and executing tasks autonomously based on the environment, inputs, and predefined goals. The core components of AI Agents include large language models (LLMs), observation and perception mechanisms, reasoning processes, action execution, as well as memory and retrieval.

Currently, there are two main development routes for the design patterns of AI Agents: one emphasizes planning capabilities, including REWOO, Plan & Execute, LLM Compiler, etc.; the other emphasizes reflective capabilities, including Basic Reflection, Reflexion, Self Discover, LATS, etc. Among them, the ReAct model is the most widely used design pattern, and its typical process can be described as a cycle of thinking → acting → observing.

In the Web3 industry, the development of AI Agents has also garnered attention. Currently, the prominent areas of exploration include:

1. Launch Platform Mode: Allows users to create, deploy, and monetize AI Agents, such as Virtuals Protocol.

2. DAO model: Utilize AI models to make decisions based on suggestions from DAO members, such as ElizaOS.

3. Business Company Model: Provide enterprise-level Multi-Agent frameworks, such as Swarms.

However, these models also face their own challenges. For example, the assets that the launch platform needs to issue must be attractive enough to create a positive flywheel; while in the current market environment, attracting creators and maintaining the operation of the economic model are both facing difficulties.

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 the single point issue and have censorship resistance.

2. Empower the MCP Server to interact with the blockchain, lowering the technical barrier.

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

Although the combination of MCP and Web3 can theoretically inject decentralized trust mechanisms and economic incentive layers into AI Agent applications, current technology still has limitations. For example, zero-knowledge proof technology struggles to verify the authenticity of Agent behavior, and decentralized networks also face efficiency issues.

The integration of AI and Web3 is an inevitable trend. Although there are still many challenges at present, continuous exploration and innovation will drive the development of this field. In the future, we look forward to seeing more groundbreaking products and applications that bring substantial value and change to the Web3 world.