Can A2A and MCP become the gold standard for Web3 AI Agents?

Author: Haotian 

What will happen if Google's A2A and Anthropic's MCP protocol become the golden communication standard for the development of web3 AI Agent? The intuitive feeling is "not acclimatized". In my opinion, the environment faced by web3 AI Agent is significantly different from the web2 ecosystem, and the challenges faced by the implementation of core communication protocols are also completely different:

1) Application maturity gap: A2A and MCP can be quickly popularized in the web2 field because they serve sufficiently mature application scenarios. In essence, they are "value amplifiers" rather than value creators. However, most web3 AI Agents remain in the primary stage of one-click release of Agents, lacking in-depth application scenarios (DeFAI, GameFAi, etc.), making it difficult for these protocols to be directly used in series to exert their value.

For example, when users compile code in Cursor, they can use the MCP protocol as a connector, and publish code updates to Github with one click without leaving the current working environment. The MCP protocol has the effect of icing on the cake. However, if users use local feeding and fine-tuning strategies to execute on-chain transactions in the web3 environment, they may be confused when they reach out to parse and analyze the on-chain data.

2) Infrastructure missing pit: If web3 AI Agent wants to build a complete ecosystem, it must first fill in the severely missing underlying infrastructure, including a 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, but in the web3 environment, a simple cross-DEX arbitrage operation faces huge challenges.

Imagine a scenario where the user instructs the AI ​​Agent to "buy from Uniswap when the price of ETH is less than $1,600 and sell after the price rebounds." The seemingly simple operation Agent needs to solve a series of web3-specific problems at the same time, such as real-time analysis of on-chain data, dynamic optimization of gas fees, slippage control, MEV protection, etc. However, the web2 AI Agent only needs to call standardized APIs to achieve functional collaboration, and the degree of perfection of its infrastructure is simply a world apart from the web3 environment.

3) Build differentiated requirements for web3 AI: If the web3 AI Agent simply applies the protocols and functional modes of web2, it is difficult to give full play to the characteristics of the on-chain transaction format, especially complex issues such as data noise, transaction accuracy, and Router diversity.

Take intentional transactions as an example. In the web2 environment, when a user instructs "book the cheapest flight", the A2A protocol allows multiple agents to collaborate easily to complete it; but in the web3 environment, when a user expects to "cross-chain my USDC to Solana at the lowest cost and participate in liquidity mining", it is necessary not only to understand the user's intention, but also to 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.

That's all.

In short, what I want to express is: the value of A2A and MCP is unquestionable, but we cannot expect them to adapt directly to the web3 AI Agent track without any modification. Isn't the vacant infra deployment gap an opportunity for builders?