If Google's A2A and Anthropic's MCP protocols become the golden communication standards for the development of web3 AI Agents, what will happen? The intuitive feeling is "not a good fit." In my view, the environment faced by web3 AI Agents is significantly different from the web2 ecosystem, and the challenges of implementing core communication protocols are also distinctly different:

1) Application Maturity Gap: A2A and MCP can quickly gain popularity in the web2 field because they serve sufficiently mature application scenarios, essentially acting as "value amplifiers" rather than value creators. However, most web3 AI Agents are still at the initial stage of one-click Agent deployment, lacking deep application scenarios (such as DeFAI, GameFAI, etc.), making it difficult for these protocols to be directly utilized to realize their value.

For example, when a user is coding in 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, where the MCP protocol plays a supplementary role. But if a user in a web3 environment tries to execute on-chain transactions using locally fine-tuned strategies, they may find themselves lost when trying to parse and analyze on-chain data.

2) Missing Infrastructure Pitfalls: 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, and so on. Often, the A2A protocol in a web2 environment allows Agents to easily call standardized APIs for functional collaboration, but in a web3 environment, even a simple cross-DEX arbitrage operation faces enormous challenges.

Imagine a scenario where a user instructs the AI Agent to "buy from Uniswap when the ETH price is below $1600 and sell when the price rebounds." This seemingly simple operation requires the Agent to simultaneously solve a series of web3-specific issues such as real-time on-chain data parsing, dynamic gas fee optimization, slippage control, and MEV protection. In contrast, a web2 AI Agent can achieve functional collaboration simply by calling standardized APIs, with the level of infrastructure completeness being 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, it will be difficult to leverage the characteristics of on-chain trading, especially with complex issues such as data noise, transaction accuracy, and Router diversity.

Taking intent trading as an example, in a web2 environment, when a user instructs to "book the cheapest flight," the A2A protocol allows multiple Agents to collaborate easily. 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 the user's intent but also weighing security, atomicity, and cost erosion, while executing a series of complex operations on-chain. In other words, if a seemingly convenient operation exposes the user to greater security risks, then such a convenient experience is meaningless, and that demand is a false demand.

In summary, what I want to express is: the value of A2A and MCP is undeniable, but we cannot expect them to be directly adapted to the web3 AI Agent space without any modifications. The gaps in infrastructure deployment present opportunities for Builders, don't they?

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