If the A2A from Google and the MCP protocol from Anthropic become the golden communication standards for web3 AI Agent development, what will happen? The intuitive feeling is "not a good fit". In my view, the environment faced by web3 AI Agents is distinctly different from that of the web2 ecosystem, and the challenges faced by core communication protocols are entirely different as well.

1) Application maturity gap: A2A and MCP have rapidly gained popularity in the web2 field because they serve sufficiently mature application scenarios, essentially acting as "value amplifiers" rather than value creators. Most web3 AI Agents remain at the initial stage of one-click publishing, lacking deep application scenarios (DeFAI, GameFAI, etc.), making it difficult for these protocols to directly connect and realize their value.

For example, when users are coding with Cursor, they can use the MCP protocol as a connector to update and publish code to Github with one click without leaving their current working environment, making the MCP protocol an added bonus. However, if users are in a web3 environment and use a locally trained strategy to execute on-chain transactions, they may find themselves confused and lost when reaching out to parse and analyze on-chain data.

2) Missing infrastructure pit: For web3 AI Agents to build a complete ecosystem, they must first fill the severely lacking underlying infrastructure, including unified data layers, Oracle layers, intent execution layers, decentralized consensus layers, etc. Often, A2A protocols in a web2 environment allow Agents to easily call standardized APIs for functional collaboration, but in a web3 environment, even a simple cross-DEX arbitrage operation faces significant challenges.

Imagine a scenario where the user instructs the AI Agent to "buy from Uniswap when ETH price is below $1600 and sell when the price rebounds". This seemingly simple operation requires the Agent to solve a series of web3-specific problems simultaneously, including real-time parsing of on-chain data, dynamic optimization of Gas fees, slippage control, and MEV protection. In contrast, a web2 AI Agent only needs to call standardized APIs to achieve functional collaboration, and the maturity of its infrastructure is worlds apart from that of the web3 environment.

3) Building differentiated demands for web3 AI: If web3 AI Agents merely apply web2 protocols and functional models, they will find it difficult to leverage the characteristics of on-chain trading, especially with complex issues such as data noise, trading accuracy, and the diversity of Routers.

Taking intent-based trading as an example, in a web2 environment, when a user instructs to "book the cheapest flight," the A2A protocol allows multiple Agents to easily collaborate. However, in a web3 environment, when a user expects to "cross-chain my USDC to Solana at the lowest cost and participate in liquidity mining," it not only requires understanding user intent but also weighing security, atomicity, and cost erosion, executing a series of complex operations on-chain. In other words, if an operation that seems convenient exposes the user to greater security risks, then such a convenient experience is meaningless, and that demand is a pseudo-demand.

That's all.

In summary, what I want to express is: the value of A2A and MCP is beyond doubt, but one cannot expect them to directly adapt to the web3 AI Agent track without any transformation. The gaps in infrastructure deployment are precisely the opportunities for Builders, aren't they?