Author: Haotian
Further reflection on the landing scenarios of web3 AI Agents, distilling 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 viewed as the Endgame form of Agent landing in Crypto, AI itself carries fuzzy reasoning and hallucination processes, which are naturally contradictory to the precision and low error tolerance required in trading scenarios.
In my view, the short-term advantage of web3 AI Agents lies in "data cleaning" and "intent parsing" levels, rather than immediately landing on the absolutely precise asset trading execution layer. For example: conducting on-chain + off-chain applicability data cleaning to build an effective information graph; or developing modeling of user trading behaviors and risk preference analysis, customizing Smart Money trading decision assistants, etc.
2) Web3 AI Agents may need A2A communication protocol functions more than MCP. Because MCP calls are relatively mature functional API interfaces. If there is a mature Agent application ecosystem as a premise, MCP can perfectly solve data island problems. Conversely, if the application ecosystem itself is not mature, MCP's standardized interfaces will lack utility.
In comparison, A2A protocol can create an incremental Agent market, giving rise to specialized vertical Agents such as on-chain data analysis Agents, smart contract audit Agents, MEV opportunity capture Agents, etc. The built-in Agent capability registry and P2P messaging network in A2A will promote better adaptation and complex interactive combination value among vertical Agents. If confined to the MCP protocol level, web3 AI Agents will likely struggle to break through language interaction limitations.
3) Web3 AI Agents' need for infrastructure construction > Application landing. In the web2 AI context, pursuing Agent practical value is naturally the highest priority. However, to build a complete ecosystem, web3 AI Agents must fill severely missing underlying infrastructure, including unified data layer, Oracle layer, intent execution layer, decentralized consensus layer, etc.
Rather than directly competing with web2 at the application layer (which is destined to be disadvantageous), taking an alternative path in the infrastructure layer and building infrastructure with web3's differentiated advantages is the right approach. Although application landing may lag behind web2 AI, building a decentralized consensus network for A2A operation and constructing interactive standards for MCP are naturally highly aligned with blockchain's native characteristics. The urgency of infrastructure construction is not much less than application landing.
4) Shifting from Crypto Native to AI Native build mindset. Looking back at years of Crypto history, just adhering to the "decentralization" framework has derived rich and diverse tracks and innovation waves. In the future AI + Crypto field, it might explore further along the path of "AI autonomy".
Whether Agentic or Robotic, the essence is to pursue a new AI-centric paradigm framework, such as an AI Agent cluster with self-fund management capabilities, a smart contract template that can self-upgrade based on network environment and feedback, a DAO governance framework dynamically adjusted by community contribution, etc. Ultimately, moving beyond simple tool application thinking and allowing AI to have a self-evolving system, letting AI drive AI's progress is the real key.
