The Rise of Autonomous Trading: How AI is Powering a New Era in Web3 Markets

AI is the center of the growing transformation of the digital economy, which includes Web3 trading as well. As decentralized markets develop, AI remains not purely a layer of optimization; it is fast becoming the core of trade.

From alpha discovery and automated arbitrage through agent-based execution and intent prediction, AI is becoming engrained into the on-chain fabric of finance. The outcome is a smooth-flowing, hyper-personalized, and autonomous trading experience. However, this power carries a few risks and questions of its own: transparency, fairness, and control.

Autonomous Trading in Web3

In a conversation with Steve Gregory, CEO of VTrader, a trading platform committed to making institutional-grade tools accessible to retail investors, he defines autonomous trading in Web3 as fundamentally different from traditional algorithmic trading. The key differentiator? The absence of centralized execution. “In traditional finance, algorithmic trading relies on centralized venues and FIX APIs to process orders,” he explains. “Even though a trade may be executed in milliseconds, final settlement can take up to a full day, and during that time, the trade can be reversed or impacted by intermediaries.”

By contrast, autonomous trading in Web3 executes directly on-chain via decentralized protocols, where settlement occurs at the next block confirmation. “There are no clearing houses, no trade support desks, and no intermediaries. The system is inherently more efficient—and more final. Record-keeping is immutable, and administrative overhead is effectively eliminated.”

Joining the conversation, Ming Wu, Founder and CEO of RabbitX, elaborated on the concept of autonomous trading by emphasizing the power of permissionless infrastructure. “In Web3, autonomous trading isn’t just about speed, it’s about sovereignty,” he notes. “Decentralized exchanges operate without intermediaries or traditional infrastructure, enabling autonomous agents to trade 24/7 without waiting on centralized counterparties.” 

Wu believes the future of DeFi trading lies in natural language interfaces, where users can execute complex strategies simply by describing their intent. “We see LLMs as the natural evolution for how people interact with DeFi, removing the need for smart contract coding or technical overhead and lowering the barrier to participation.

Bringing Predictive AI

Shashank Sripada, Co-founder and COO at Gaia, highlights how AI is evolving beyond traditional robo-advisors to meet the real-time demands of DeFi. “Our agents automate critical tasks like portfolio rebalancing, yield optimization, governance participation, and protocol monitoring—eliminating the need for 24/7 human oversight,” says Sripada. Gaia’s platform enables developers to build decentralized AI agents that operate transparently via smart contracts on protocols like Uniswap and Aave, using Retrieval-Augmented Generation (RAG) to stay informed with live market data. 

While VTrader is a centralized platform, it is embracing AI to democratize access to predictive analytics. One such innovation is their AI price prediction tool, developed in partnership with a third-party firm. “The tool analyzes crypto news sources and chart data, reviews five days of price action, and forecasts the next three,” says Gregory. “It’s designed to help our users navigate short-term volatility with more confidence.”

How AI powers intent-based trading

Intent-based trading has become a buzzword in the DeFi circles, generally associated with next-gen protocols such as Velora and CowSwap. According to Mounir Benchemled, founder of Velora, intent-based trading via AI can offer maximum efficiency, but this efficiency has to rest on transparency and thus verifiability to be actually trusted in DeFi. 

“AI is powerful because it removes emotion and bias from trading, optimizing decisions based purely on data,” he says. However, he cautions that without proper visibility into how these systems operate, users may find themselves trading on trust rather than truth. “AI in DeFi trading can present risks around trust, transparency, and black-box behavior, depending on whether the AI code is open source and whether it runs on Web2 or Web3 infrastructure,” Benchemled explains. 

At Velora, we advocate for AI agents that run on-chain and are fully open-source, allowing the community to audit their behavior and logic. That’s the only way to ensure accountability and maintain user trust, especially as models evolve and operate in unpredictable markets. Intent-based trading should empower users.

Building on a similar vision, Ming Wu, shared about the shift toward user-friendly, intent-driven automation. “We have built an AI trading agent that is integrated with RabbitX, allowing people to place trades and check their positions via natural language,” Wu shares. “We’ve been testing it internally, and it’s a very exciting project.” Crucially, Wu emphasizes a core evolution: “AI interprets the why behind every trade, not just the what. That shift lets us design systems that anticipate and fulfill complex user goals in a single step.”

How AI Agents Will Trade Tomorrow

Sripada believes that the future of DeFi will be driven by AI agents that take care of tasks like trade execution, optimization, and even governance—so users can focus on their goals and risk preferences instead of complex details. He says, “Expect agents to handle execution, optimization, and even governance across decentralized protocols—while people focus on goals and risk, not minutiae.

The challenge will be keeping these systems open and aligned, not just faster middlemen in code.” However, he warns that the real challenge is ensuring these systems stay open and trustworthy. “We need universal standards for expressing intent, decentralized data that’s actually reliable, and agent frameworks anyone can inspect. The goal: automation. You don’t have to trust blindly because you can verify everything yourself.”

Gregory is optimistic about the future of autonomous trading. “We’re moving toward agent-to-agent economies, where smart contracts and AI agents transact based on programmed logic and live data,” he says. “This fulfills crypto’s original vision—programmable money that works autonomously and efficientcy.”