What would happen if Google's A2A and Anthropic's MCP protocols became the gold communication standards for the development of web3 AI agents? The intuitive feeling is' not adapting to the local conditions'. In my opinion, there are significant differences between the environment faced by web3 AI agents and the web2 ecosystem, and the challenges faced in implementing core communication protocols are also vastly different 1) Application maturity gap: A2A and MCP have rapidly become popular in the web2 field because they serve mature application scenarios and are essentially "value amplifiers" rather than value creators. However, most web3 AI agents are still in the early stage of one click release of agents, lacking deep application scenarios (DeFAI, GameFAi, etc.), making it difficult for these protocols to be directly integrated and realize their value. For example, when users write code in cursor, they can use the MCP protocol as a connector to update and publish the code to Github with just one click without leaving the current working environment. The MCP protocol adds an extra layer of functionality. But if users use the strategy of local feeding fine-tuning to execute on chain transactions in the web3 environment, they may be confused when they reach out and analyze the on chain data. 2) Infrastructure deficiency pitfall: In order for web3 AI agents to build a complete ecosystem, they must first fill the seriously missing underlying infrastructure, including the unified data layer, Oracle layer, intent execution layer, decentralized consensus layer, and so on. Often, in the web2 environment, the A2A protocol allows agents to easily call standardized APIs to achieve functional collaboration. However, in the web3 environment, a simple cross DEX arbitrage operation faces significant challenges. Imagine a scenario where a user instructs an AI agent to "buy from Uniswap when the ETH price drops below $1600 and sell after the price rebounds." This seemingly simple operation requires the agent to simultaneously solve a series of web3 specific problems such as real-time data parsing on the chain, dynamic optimization of gas fees, slippage control, and MEV protection. And the web2 AI agent only needs to call standardized APIs to achieve functional collaboration, and its infrastructure level is vastly different from that of the web3 environment. 3) Building differentiated requirements for web3 AI: If the web3 AI agent simply applies the protocol and functional mode of web2, it is difficult to fully utilize the characteristics of on chain transaction formats, especially complex issues such as data noise, transaction accuracy, and router diversity. Taking intentional transactions as an example, in the web2 environment, when a user instructs to "book the cheapest flight," the A2A protocol allows multiple agents to easily collaborate to complete the task; But in the web3 environment, when users expect to "cross chain my USDC to Solana and participate in liquidity mining at the lowest cost," they not only need to understand user intentions, but also weigh security, atomicity, and cost wear and tear, and perform a series of complex operations on the chain. In other words, if seemingly convenient operations expose users to greater security risks, then such a convenient experience is meaningless, and the demand is also a pseudo demand. above. In short, what I want to express is that the value of A2A and MCP is beyond doubt, but we cannot expect them to be directly adapted to the web3 AI Agent race without any modifications. The blank deployment of Infra in this gap is exactly the opportunity for Builders, isn't it?