AI is transforming on-chain trading, but its true value isn’t about predicting prices. This year, more and more Web3 projects have begun integrating AI features—some offering market analysis, others summarizing news, and a few capable of answering various trading questions. But after exploring @Velvet_Capital, I realized its goal goes beyond being an “AI assistant”—it aims to build an AI system that actively participates in the entire trading process. The team calls it Velvet Unicorn. When most people hear “AI,” they think of chatbots: ask a question, get an answer, and move on. But in Velvet’s design, AI’s role isn’t just to provide information—it connects research, analysis, decision-making, and execution into a seamless workflow. For example, you can simply ask: “What tokens on Base are worth watching today?” “Analyze BTC’s current technical trend.” “Which DeFi pool currently offers the most attractive yields?” The AI doesn’t just organize data—it combines on-chain metrics, market sentiment, social trends, technical indicators, and risk signals so you don’t have to juggle multiple websites to compare information. Even more interesting, Velvet uses a Multi-Agent architecture instead of relying on a single model to handle everything. The system is composed of specialized agents—Researcher, Analyst, Trader, Executor—each with distinct responsibilities that collaborate to complete tasks. This design not only reduces the risk of AI hallucinations but also enables more professional analysis and execution. I find this concept remarkably similar to a trading team: one person gathers market data, another analyzes metrics, a third develops strategy, and finally, a trader executes the order. What once required an entire team is now being attempted by AI working in concert. Another feature that impressed me is Velvet Unicorn’s ability to learn continuously. Every analysis result and trade execution creates a feedback loop that refines the agents’ prompts, decision-making processes, and execution quality. In other words, it doesn’t just give fixed answers—it evolves by learning from market data and real-world outcomes. I believe the real value of AI in on-chain trading isn’t about guaranteeing profits or magically predicting the next 100x coin. It’s about helping us cut through information overload, identify key insights faster, and reclaim time previously spent on repetitive research—so we can focus more on strategy and risk management. If AI can eventually integrate research, analysis, trading, and even portfolio management into one cohesive system, its role will no longer be merely a tool—but rather an intelligent, always-on partner for traders. This may well be the direction Velvet aims to build: where AI doesn’t just answer your questions, but actively participates in every on-chain decision you make.