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MuleRun CTO: The Moat of Agents Lies in Data Density and User Memory

In a speech titled "Handing AI's Keys to the On-Chain Controllers," MuleRun CTO Shu Junliang discussed the evolution and security of AI Agents in finance and Web3. He outlined six dimensions for a complete AI assistant: dialogue, data input, agent capability, execution environment, user memory, and continuous learning. MuleRun's product integrates these through features like multi-platform IM bots, real-time multi-asset data, smart model routing, cloud sandboxes, persistent user profiles, and a shared knowledge network. Shu emphasized that while AI Agents are advancing from assisting to autonomously executing decisions—potentially enabling individuals to operate like small funds—safety remains paramount. He detailed MuleRun's security measures, including local key handling, isolated sandboxes, full audit trails, and strict permission controls. However, he acknowledged persistent risks like data exposure, model hallucinations, prompt injection, and the "black box" nature of AI decisions, advising manual confirmation for financial operations. He identified key trends: the shift from human-led to Agent-led on-chain interactions requiring infrastructure redesign; the erosion of information advantages replaced by competition in execution speed and strategy; and the balancing effect of Agents, which democratize access but ultimately advantage those with superior judgment. Shu concluded that an Agent's true moat lies in data density and accumulated user memory, not easily replicable technology, and that while Agents will reshape finance and Web3, human oversight over critical decisions must remain.

marsbit05/14 08:50