If the A2A and MCP protocols launched by Google and Anthropic become the golden communication standards for the development of web3 AI Agents, what would happen? The intuitive feeling is 'incompatibility.' In my view, there are significant differences between the environments faced by web3 AI Agents and the web2 ecosystem, and the challenges encountered in implementing the core communication protocols are also vastly different.
1) Maturity gap in applications: A2A and MCP have quickly gained popularity in the web2 realm because they serve sufficiently mature application scenarios, essentially acting as 'value amplifiers' rather than value creators. In contrast, most web3 AI Agents remain at the primitive stage of one-click publishing, lacking deep application scenarios (like DeFAI, GameFAI, etc.), making it difficult for these protocols to directly interoperate and realize value.
For example, when users code in Cursor, they can use the MCP protocol as a connector to update and publish the code to Github with one click without leaving their current work environment. The MCP protocol adds significant value. However, if users are in a web3 environment and execute on-chain transactions using locally fine-tuned strategies, they may find themselves confused and lost when trying to analyze on-chain data.
2) Missing foundational infrastructure: To build a complete ecosystem for web3 AI Agents, it is essential to fill the significant gaps in foundational infrastructure, including unified data layers, Oracle layers, intent execution layers, decentralized consensus layers, and so on. In the web2 environment, A2A protocols allow Agents to easily call standardized APIs for functional collaboration, but in the web3 environment, even a simple cross-DEX arbitrage operation faces immense 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 problems, 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 a level of infrastructure maturity that is worlds apart from the web3 environment.
3) Building differentiated demands for web3 AI: If a web3 AI Agent merely applies web2 protocols and functional models, it will struggle to leverage the characteristics of on-chain transaction dynamics, particularly the complex issues of data noise, transaction accuracy, and Router diversity.
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 requires not only understanding the user's intent but also weighing safety, atomicity, and cost friction, while 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 false demand.
The above.
In summary, what I want to express is: The value of A2A and MCP is beyond doubt, but we cannot expect them to directly adapt to the web3 AI Agent landscape without any transformation. The gaps in infrastructure deployment represent opportunities for Builders, don't they?