Some Thoughts on the Application Scenarios for Web3 AI Agents
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Some Thoughts on the Application Scenarios for Web3 AI Agents
Whether Agentic or Robotic, fundamentally they must pursue a completely new AI-centric paradigm framework.
Navigating Web3 tides with focused insightsAuthor: Haotian
After further reflection on potential落地 scenarios for web3 AI Agents, I've distilled several forward-looking insights as follows:
1) The most native application function of web3 AI Agents may not be "trading." Although DeFi trading agents are often seen as the endgame for Agent adoption in Crypto, the inherent ambiguity and hallucination tendencies of AI fundamentally contradict the precision and low tolerance for error required in trading environments.
In my view, short-term advantages of web3 AI Agents lie more in "data cleaning" and "intent parsing," rather than immediate deployment into highly accurate asset execution layers. For example: cleaning hybrid on-chain and off-chain applicable data to build effective information graphs; or modeling on-chain user trading behaviors and analyzing risk preferences to create customized Smart Money trading decision assistants.
2) The need for A2A (Agent-to-Agent) communication protocols among web3 AI Agents may outweigh that for MCP. While MCP typically invokes mature functional API interfaces—making it ideal for resolving data silos if a robust Agent ecosystem already exists—if such an ecosystem is immature, standardized MCP interfaces lack practical value.
In contrast, A2A protocols can foster incremental Agent markets, catalyzing the emergence of specialized vertical Agents such as on-chain data analysis Agents, smart contract auditing Agents, or MEV opportunity detection Agents. Built-in features like Agent capability registries and P2P messaging networks under A2A facilitate better interoperability, coordination, and complex composability among these specialized Agents. Relying solely on MCP may leave web3 AI Agents stuck within the limitations of language-based interaction.
3) Demand for infra development > Application deployment. In the web2AI context, prioritizing Agent utility is naturally paramount. However, for web3 AI Agents to build a complete ecosystem, significant gaps in foundational infrastructure must first be addressed—including unified data layers, Oracle layers, intent execution layers, and decentralized consensus layers.
Rather than directly competing with web2 at the application layer—a disadvantageous position—carving out unique paths at the infra level to build web3-differentiated infrastructure is the right strategic direction. Even if application deployment lags behind web2 AI, constructing foundational infra such as decentralized consensus networks for A2A operations or unified interoperability standards enabling MCP functionality aligns deeply with blockchain’s native properties. The urgency of building infra is therefore nearly equivalent to that of launching applications.
4) A mindset shift from Crypto Native to AI Native. Reflecting on years of Crypto history, adherence to just one principle—"decentralization"—has spawned diverse sectors and waves of innovation. In the future AI + Crypto space, we may travel much further along the path of "AI autonomy."
Whether Agentic or Robotic, we ultimately need a new paradigm centered around AI—for instance: an AI Agent cluster capable of autonomous fund management; a smart contract template that self-upgrades based on network conditions and feedback; or a DAO governance framework dynamically optimized according to community contribution levels. Ultimately, moving beyond simple tool-based thinking, empowering AI with self-evolving systems, and enabling AI to drive its own advancement is the fundamental truth.
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Haotian | CryptoInsight
