After further thinking about the application scenarios of web3 AI Agent

After further thinking about the application scenarios of web3 AI Agent, I have summarized some more of my thoughts as follows:

1) The most native application function of web3 AI Agent may not be "trading".

Although DeFi trading agents have always been regarded as the endgame form of agents landing in Crypto, AI itself has fuzzy reasoning and hallucination processes, which are naturally contrary to the accuracy and low fault tolerance required by trading scenarios. In my opinion, the advantages of web3 AI Agent in the short term are in the "data cleaning" and "intention analysis" levels, rather than landing on the asset transaction execution layer with absolute accuracy all at once.

For example: cleaning the applicability data on the chain + off-chain, building an effective information map; for example: modeling and risk preference analysis of on-chain user transaction behavior, customizing Smart Money transaction decision assistants, etc.

In the long term, if we can form a decentralized consensus network for A2A operation and a unified interoperable standard for MCP to play a role, it will be highly consistent with the native characteristics of blockchain.

2) Web3 AI Agent may need A2A, an agent communication protocol, more than MCP.

This is because MCP calls relatively mature functional API interfaces. If there is a mature agent application ecosystem, MCP can perfectly solve the data island problem. On the contrary, if the application format itself is immature, MCP's standardized interface will be useless.

In contrast, the A2A protocol can create a certain incremental agent market, which will give rise to a number of specialized vertical agents, such as on-chain data analysis agents, smart contract audit agents, MEV opportunity capture agents, etc. A2A's built-in agent capability registry and P2P messaging network will enable each vertical agent to better adapt to the value of linkage and complex interactive combinations. If it only stays at the MCP protocol level, it is likely that web3 AI Agent will find it difficult to break through the limitations of language interaction.

3) Web3 AI Agent’s demand for infra construction > Application landing.

In the context of web2AI, the pursuit of the practical value of Agent is naturally the highest priority, but if web3 AI Agent wants to build a complete ecosystem, it must fill in the severely missing underlying infrastructure, including a unified data layer, Oracle layer, intent execution layer, decentralized consensus layer, etc.

Compared with the hard competition with web2 at the application layer (which is bound to suffer losses), it is the right way to find a new way at the infra layer and build an infra with the differentiated advantages of web3. Although the application landing lags behind web2 AI, the basic infra such as building a decentralized consensus network for A2A operation and building a unified interoperable standard for MCP to play a role is highly consistent with the native characteristics of blockchain, and the urgency of building infra is not much less than that of application landing.

3) The shift in build mindset from Crypto Native to AI Native.

Looking back at the history of Crypto over the past few years, the adherence to the "decentralized" framework alone has spawned a rich and diverse track and innovation wave. In the future, the AI + Crypto field may go further around "AI autonomy".

Whether it is Agentic or Robotic, they are essentially pursuing a new AI-centric paradigm framework, such as an AI Agent cluster with self-fund management capabilities, a smart contract template that can be self-upgraded according to network environment and feedback, and a DAO governance framework that is dynamically adjusted and optimized based on community contribution. In the final analysis, it is the hard truth to get rid of the simple tool application thinking, let AI have an autonomous evolution system, and let AI drive AI progress.