The three major "death blind spots" of A2A and MCP protocols landing Web3 AI Agent

What would happen if the A2A from Google and the MCP protocol from Anthropic become the golden communication standards for the development of web3 AI Agents? The intuitive feeling is that it would be "incompatible with the local environment." In my view, the environment faced by web3 AI Agents is significantly different from the web2 ecosystem, and the challenges faced by the core communication protocols being implemented 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. In contrast, web3 AI Agents mostly remain at the initial stage of one-click agent deployment, lacking deep application scenarios (DeFAI, GameFAi, etc.), which makes it difficult for these protocols to directly interoperate and realize their value.

For example, users can write code in Cursor and use the MCP protocol as a connector, allowing them to update and publish the code to Github with one click without leaving their current working environment. The MCP protocol plays a complementary role. However, if users operate in a web3 environment and execute on-chain transactions using locally trained fine-tuning strategies, they may find themselves confused and lost when trying to parse and analyze on-chain data.

2. Missing Infrastructure Pitfall: For a web3 AI Agent to build a complete ecosystem, it must first fill the severely lacking underlying infrastructure, including unified data layers, Oracle layers, intent execution layers, decentralized consensus layers, and so on. Often, in a web2 environment, A2A protocols allow Agents to easily call standardized APIs to achieve functional collaboration, but in a web3 environment, even 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 is below $1600 and sell after the price rebounds". This seemingly simple operation requires the Agent to address a series of web3-specific issues 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 can achieve functional collaboration simply by calling standardized APIs, with the level of infrastructure completeness being vastly different compared to the web3 environment.

3. Build differentiated demands for web3 AI: If web3 AI Agent simply applies the protocols and functional patterns of web2, it will be difficult to leverage the characteristics of on-chain trading formats, especially the complex issues of data noise, transaction accuracy, and the diversity of Routers.

Taking intent-based trading as an example, in a web2 environment, a user instructs "book the cheapest flight," and 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 wear, while executing a series of complex operations on-chain. In other words, if seemingly convenient operations impose greater security risks on users, then such a convenient experience is meaningless, and that demand is a false demand.

Above.

In conclusion, what I want to express is that the value of A2A and MC is beyond doubt, but we cannot expect them to be directly adapted to the web3 AI Agent track without any modifications. Isn't the gap in infrastructure deployment an opportunity for Builders?