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 that it 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 the core communication protocol 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 and are essentially "value amplifiers" rather than value creators. However, most web3 AI Agents are still in the primary stage of one-click release of Agents and lack deep 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 to publish code updates to Github with one click without leaving the current working environment. The MCP protocol is 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 on-chain data.
2) Infrastructure Lack: If web3 AI Agent wants to build a complete ecosystem, it must first fill in the severely missing underlying infrastructure, including unified data layer, Oracle layer, intent execution layer, decentralized consensus layer, etc. 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 a user instructs an AI Agent to "buy from Uniswap when the ETH price is below $1,600 and sell after the price rebounds." This seemingly simple operation requires the Agent to simultaneously solve a series of web3-specific problems, including real-time analysis of on-chain data, dynamic optimization of gas fees, slippage control, and MEV protection. However, web2 AI Agent only needs to call standardized APIs to achieve functional collaboration, and the level of its infrastructure perfection is a far cry from that of the web3 environment.
3) Building differentiated needs for web3 AI: If web3 AI Agent simply applies the protocols and functional modes of web2, it will be difficult to give full play to the characteristics of on-chain transaction formats, 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 indicates "book the cheapest flight", the A2A protocol allows multiple agents to collaborate easily to complete the task. However, 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 a seemingly convenient operation exposes 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 say is: the value of A2A and MCP is unquestionable, but we cannot expect them to be directly adapted to the web3 AI Agent track without any modification. Isn’t the vacant infra deployment gap an opportunity for builders?
