The next wave of AI Agent's new momentum is brewing, but it will no longer be a posture of purely narrative concept hype as in the past; it must rely on pragmatism and practical applications to sustain itself.

Written by: Haotian

Why do I assert that the next round of AI Agent craze will definitely be based on web2AI standard framework protocols like MCP and A2A? The logic behind it is simple:

1) The dilemma of web3 AI Agents lies in excessive conceptualization, where narrative outweighs practicality. When discussing the grand vision of decentralized platforms and user data sovereignty, the actual user experience of product applications is often dismal. Especially after experiencing a round of conceptual bubble cleansing, very few retail investors are willing to pay for grand yet unfulfilled expectations.

2) The rapid rise of protocols like MCP and A2A in the web2 AI field, forming significant momentum in the AI circle, stems from their "visible and tangible" pragmatism. MCP is like the USB-C interface of the AI world, allowing AI models to seamlessly connect to various data sources and tools, and there are already many practical cases of MCP.

For example: users can directly use Claude to control Blender to create 3D models, some UI/UX practitioners can generate complete Figma design files using natural language, and some programmers can complete code writing, supplementation, and Git submissions in one go using Cursor, among other operations.

3) Previously, everyone expected that web3 AI Agents would give birth to innovative applications in the two major verticals of DeFi and GameFi, but in reality, many similar applications are still stuck at the "showing skills" level of natural language processing interfaces, failing to meet the threshold of practicality.

By combining MCP and A2A, a more powerful Multi-Agent collaboration system can be constructed, breaking down complex tasks for specialized Agents to handle. For instance, allowing an analysis Agent to read on-chain data, analyze market trends, and connect with other prediction Agents and risk control Agents, transforming the past paradigm of single Agent integrated execution into a model of multi-Agent collaborative division of labor.

All successful application cases of MCP provide successful examples for the birth of the new generation of trading and gaming Agents in web3.

In addition to these, the hybrid framework standards based on MCP and A2A also have advantages in user-friendliness for web2 users and application deployment speed. Currently, it is only necessary to consider how to integrate the value capture and incentive mechanisms of web3 with application scenarios like DeFi and GameFi. If a project continues to adhere to pure conceptualism in web3 while refusing to embrace the pragmatism of web2, it is likely to miss the next trend of AI Agents.

In short, the next wave of AI Agent's new momentum is brewing, but it will no longer be a posture of purely narrative concept hype as in the past; it must rely on pragmatism and practical applications to sustain itself.

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