Further reflection on the landing scenarios of web3 AI Agents has led to several forward-looking thoughts, as follows:

1) The most native application function of web3 AI Agents may not be "trading." Although DeFi trading Agents have always been seen as the endgame form of Agents landing in Crypto, the inherent ambiguity and hallucination processes of AI naturally contradict the precision and low tolerance for error required in trading scenarios.

In my view, the short-term advantages of web3 AI Agents lie in "data cleaning" and "intent analysis," rather than immediately landing on the asset trading execution layer with absolute precision. For example: cleaning data applicable both on-chain and off-chain to build effective information graphs; or modeling on-chain user trading behavior and analyzing risk preferences to customize Smart Money trading decision assistants, etc.;

2) The need for A2A (Agent-to-Agent) communication protocol functionality in web3 AI Agents may be greater than that of MCP (Multi-Chain Protocol). This is because MCP calls are relatively mature functional API interfaces. If there is a mature Agent application ecosystem, MCP can perfectly solve the data island problem. Conversely, if the application landscape itself is immature, the standardized interfaces of MCP will lack utility.

In contrast, the A2A protocol can create a certain incremental market for Agents, leading to the emergence of a batch of specialized vertical Agents, such as on-chain data analysis Agents, smart contract auditing Agents, MEV opportunity capturing Agents, etc. The built-in Agent capability registry and P2P messaging network of A2A will encourage various vertical Agents to better adapt to linkage and complex interaction combinations. If it only stays at the MCP protocol level, it may be difficult for web3 AI Agents to break through the limitations of language interaction.

3) The demand for infrastructure construction in web3 AI Agents is greater than application landing. In the context of web2 AI, pursuing the practical value of Agents naturally has the highest priority. However, for web3 AI Agents to build a complete ecosystem, they must fill the severely lacking underlying infrastructure, including unified data layers, Oracle layers, intent execution layers, decentralized consensus layers, etc.

Rather than directly competing with web2 at the application level (which is bound to suffer losses), finding a new path at the infrastructure level to build infrastructure with differentiated advantages in web3 is the right way. Although there is a relative lag in application landing compared to web2 AI, building a decentralized consensus network for A2A operation and establishing unified interactive operational standards for MCP naturally aligns with the inherent characteristics of blockchain. The urgency of building infrastructure is not much less than that of application landing.

4) A shift in the mindset from Crypto Native to AI Native building. Looking back at the history of Crypto over the past few years, the adherence to a "decentralized" framework has given rise to a rich variety of tracks and waves of innovation. In the future, the AI + Crypto field may go further around "AI autonomy."

Whether Agentic or Robotic, the essence is to pursue a brand new AI-centered paradigm framework, such as a cluster of AI Agents with self-funding management capabilities, a set of smart contract templates that can self-upgrade based on network environment and feedback, and a DAO governance framework that dynamically adjusts and optimizes based on community contribution. Ultimately, it is about moving away from simple tool application thinking, allowing AI to have a self-evolving system, and letting AI drive AI progress, which is the hard truth.

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