How does the A2A protocol solve the dilemma of implementing Web3 AI Agent?

I further thought about the direction of the web3 AI Agent landing scenario and refined some forward-looking thoughts, as follows:

1. The most native application function of web3 AI Agent may not be "transaction". Although DeFi transaction Agent has always been regarded as the Endgame pattern for Agent to land Crypto. However, AI itself has fuzzy reasoning and hallucination processes, which is naturally contrary to the accuracy and low fault tolerance required by trading scenarios.

In my opinion, the advantages of short-term web3 AI Agent are in the "data cleaning" and "intention analysis" levels, rather than being implemented at an absolutely accurate asset transaction execution level at once. For example: clean up the on-chain + off-chain applicability data, build an effective information map; and for example: expand the modeling and risk preference analysis of on-chain user transaction behavior, customize the Smart Money transaction decision-making assistant, etc.;

2. The web3 AI Agent's requirement for A2A's Agent communication protocol function may be greater than that of MCP. Because MCP calls are relatively mature functional API interfaces, if there is a mature Agent application ecosystem, MCP can perfectly solve the data island problem. On the contrary, if the application format itself is immature, MCP's standardized interface lacks its place to be used.

In contrast, the A2A protocol can create a certain incremental market for Agents, which will give rise to a number of professional division of labor and vertical agents that appear first, such as on-chain data analysis agents, smart contract audit agents, MEV opportunity capture agents, etc. Conditions such as A2A's built-in Agent capability registry and P2P messaging network will prompt various vertical agents to better adapt to the value of linkage and complex interaction combinations. If it only stays at the MCP protocol level, it is probably difficult for web3 AI Agent to break through the limitations of the language interaction level.

3. The demand for infra construction by web3 AI Agent > Application implementation. In the context of web2AI, the pursuit of the practical value of Agents is naturally the highest priority, but if web3 AI Agent wants to build a complete ecosystem, it must fill in the seriously missing underlying infrastructure, including the unified data layer, Oracle layer, intention execution layer, decentralized consensus layer, etc.

Compared to fighting against web2 in the application layer (destined to suffer losses), it is the right way to find a different approach in the infra layer and build an infra with the advantages of web3 differentiation. Although the application implementation is lagging behind web2 AI, building a decentralized consensus network for A2A operation and building a unified interoperable operation standard for MCP to play its role naturally has a high degree of compatibility with the native characteristics of blockchain, and the urgency of building an infra is not much worse than that of application implementation.

4. The fixed transformation of build thinking from Crypto Native to AI Native, looking back at the past few years of Crypto history, the adherence to the "decentralization" framework has led to a rich and diverse track and innovation wave. In the future, the AI ​​+ Crypto field may go a further path around "AI autonomous".

Whether it is Agentic or Robotic, we must essentially pursue a new AI-centric paradigm framework, such as an AI Agent cluster with self-fund management capabilities, a smart contract template that can be upgraded based on the network environment and feedback, and a DAO governance framework that is dynamically adjusted and optimized based on community contribution. Ultimately, it is the hard truth to withdraw from simple tool application thinking, let AI have its own evolution system, and let AI drive AI progress.