Artificial intelligence (AI) agents are reshaping crypto trading, decentralized finance (DeFi), and more. AI agents are no longer just a concept, but rather a common feature being leveraged for a variety of crypto-focused use cases. AI agents also differ from traditional rule-based bots. Rather than following simple prompts, agents are able to continuously learn from market movements, sentiment and liquidity conditions to execute trades with greater precision. This is why AI agents are being leveraged more often in the crypto sphere. David Sneider, CEO and co-founder of Lit Protocol, told Cryptonews that any strategy a person or organization executes manually in DeFi today can be automated through AI agents. Sneider added that beyond saving time, these models provide entirely new access. “One person can craft a strategy, while others simply enroll to benefit, removing the barrier of technical sophistication that previously limited who could participate in advanced crypto strategies,” he said. AI Agents for Trading and Yield Optimization To put this in perspective, Sneider explained how retail investors use AI agents through Lit Protocol and Vincent, an automation platform layer on Lit that powers a wide variety of crypto trading agents. “Within autonomous crypto agents, we see two broad categories: ‘user-configured’ agents and ‘set-and-forget’ agents,” Sneider mentioned. According to Sneider, user-configured agents allow crypto investors to have direct strategy input. A leading example of this is Lit’s “Agent Hustle,” where users interact through a chat interface and provide prompts like the one below:“Execute a mixed trading strategy: allocate 80% to conservative blue-chip and stablecoin yield positions, 10% to aggressive perp trading with dynamic leverage and a max 2% drawdown per trade, and 10% to trending meme tokens using sentiment and social signals. Rebalance automatically, manage risk tightly, and maximize overall portfolio performance.”“The agent drafts, refines with user feedback, and then executes the strategy,” Sneider said. Users can also leverage set-and-forget agents to run established strategies that are optimized over time. Sneider pointed out examples of these being deployed through Vincent: Perpetual futures hedging: Monitoring exposures and rebalancing leverage automatically. Yield optimization: Shifting stablecoins between lending markets and vaults to secure the best rates. Trader Agents: Executing momentum, mean reversion, options spreads, or cross-chain arbitrage strategies under a defined mandate. AI Agents for Token Discovery AI agents are also helping crypto users with token discovery. Jake Gallen, CEO at agenthustle.ai (Hustle), told Cryptonews that the platform helps users discover tokens intelligently and trade autonomously. “Hustle’s Memory, Toolbox, and Conditional trading engine are the three pillars that separate this agent from competition, making him one of the most unique products on the market,” Gallen said. “We combine these apps, leverage it with the Emblem Vault multichain wallet, and allow the Agent to interact with any blockchain.” Gallen pointed out that Hustle’s primary use cases include token discovery and automated trading. “Within a single prompt, Hustle can find a token based on the context a user presents, then buy these assets, and set up an advanced entry and exit order. From start to finish, this can be accomplished in 30 seconds,” Gallen commented. Hustle also helps with users seeking pocket analysis. “Users can combine Hustle’s memory and toolbox to utilize just his alpha aggregation, news reporting, and contextual outputs. They do not trade and use him simply as a pocket analyst,” Gallen said. AI Agents Within Crypto Exchanges While AI agents can help crypto investors trade intelligently, popular U.S.-based crypto exchange Coinbase has also started to explore these models. Dan Kim, head of strategy at Coinbase Developer Platform, told Cryptonews that Coinbase is currently focused on building infrastructure to allow AI agents to operate safely and autonomously. “This includes giving agents wallets, the ability to transact on-chain, and tools to charge or pay for services programmatically,” Kim said. He added that the infrastructure behind these models, like x402 and AgentKit, allows AI agents to interact with DeFi, pay for services, and perform economic activities safely across Coinbase. “This is essentially preparing the groundwork for future AI-native payments,” Kim noted. AI Agents Can Make Mistakes Although the potential behind AI agents and crypto use cases is huge, these models are far from perfect. While AI agents can ensure efficiency, accessibility, and risk discipline, Sneider explained, there are downfalls to consider: Data fragility: This is where poor inputs or unreliable oracles can cascade into bad trades. Overfitting: Agents trained on narrow historical data underperform in black swan events. Execution errors: We’ve seen cases where AI models hallucinate or misinterpret instructions. For instance, Sentient recently shared an example of an AI agent getting “stuck” in a transaction loop, firing the same order over and over. Without circuit breakers, this kind of loop can spiral quickly. Latency: Agents that depend on off-chain inference sometimes miss optimal execution windows. Gallen added that the most common mistake AI agents make when it comes to trading is purchasing “fake tokens.” “When a token is pumping, there are multiple copycat tokens that pop up. Even more so, these copycat tokens are artificially inflated to appear as the organic runner. AI can be tricked to buy these because they consider the on-chain volume as real,” Gallen explained. Although there are multiple safety mechanisms set in place, as well as checks and balances, Gallen noted that these occurrences happen every so often. Additionally, Gallen mentioned that API and tooling inconsistencies can be challenging. “This can happen when someone uses one API for conditional trading to execute trades, while using another API to source real-time data. One API can consider Market Cap and FDV as the same thing, while the other provider is much more meticulous in their classifications. This can cause AI to close trades early or result in another variety of outputs that can cause these models to fail at what they were intended to accomplish.” AI Agents Won’t Replace Human Traders Yet Although the potential behind AI crypto trading agents is massive, these models are not yet ready to replace humans entirely. According to Sneider, the beginning of “agentic finance” is just now taking place. However, he said that today’s early products show both promise and pitfalls. “AI can act as a co-pilot, but it must operate inside secure rails,” he said. As such, Sneider believes that AI agents won’t replace human traders, but rather they’ll extend them. “They’ll automate execution across both DeFi and TradFi, but always anchored in user-defined authority,” Sneider saidArtificial intelligence (AI) agents are reshaping crypto trading, decentralized finance (DeFi), and more. AI agents are no longer just a concept, but rather a common feature being leveraged for a variety of crypto-focused use cases. AI agents also differ from traditional rule-based bots. Rather than following simple prompts, agents are able to continuously learn from market movements, sentiment and liquidity conditions to execute trades with greater precision. This is why AI agents are being leveraged more often in the crypto sphere. David Sneider, CEO and co-founder of Lit Protocol, told Cryptonews that any strategy a person or organization executes manually in DeFi today can be automated through AI agents. Sneider added that beyond saving time, these models provide entirely new access. “One person can craft a strategy, while others simply enroll to benefit, removing the barrier of technical sophistication that previously limited who could participate in advanced crypto strategies,” he said. AI Agents for Trading and Yield Optimization To put this in perspective, Sneider explained how retail investors use AI agents through Lit Protocol and Vincent, an automation platform layer on Lit that powers a wide variety of crypto trading agents. “Within autonomous crypto agents, we see two broad categories: ‘user-configured’ agents and ‘set-and-forget’ agents,” Sneider mentioned. According to Sneider, user-configured agents allow crypto investors to have direct strategy input. A leading example of this is Lit’s “Agent Hustle,” where users interact through a chat interface and provide prompts like the one below:“Execute a mixed trading strategy: allocate 80% to conservative blue-chip and stablecoin yield positions, 10% to aggressive perp trading with dynamic leverage and a max 2% drawdown per trade, and 10% to trending meme tokens using sentiment and social signals. Rebalance automatically, manage risk tightly, and maximize overall portfolio performance.”“The agent drafts, refines with user feedback, and then executes the strategy,” Sneider said. Users can also leverage set-and-forget agents to run established strategies that are optimized over time. Sneider pointed out examples of these being deployed through Vincent: Perpetual futures hedging: Monitoring exposures and rebalancing leverage automatically. Yield optimization: Shifting stablecoins between lending markets and vaults to secure the best rates. Trader Agents: Executing momentum, mean reversion, options spreads, or cross-chain arbitrage strategies under a defined mandate. AI Agents for Token Discovery AI agents are also helping crypto users with token discovery. Jake Gallen, CEO at agenthustle.ai (Hustle), told Cryptonews that the platform helps users discover tokens intelligently and trade autonomously. “Hustle’s Memory, Toolbox, and Conditional trading engine are the three pillars that separate this agent from competition, making him one of the most unique products on the market,” Gallen said. “We combine these apps, leverage it with the Emblem Vault multichain wallet, and allow the Agent to interact with any blockchain.” Gallen pointed out that Hustle’s primary use cases include token discovery and automated trading. “Within a single prompt, Hustle can find a token based on the context a user presents, then buy these assets, and set up an advanced entry and exit order. From start to finish, this can be accomplished in 30 seconds,” Gallen commented. Hustle also helps with users seeking pocket analysis. “Users can combine Hustle’s memory and toolbox to utilize just his alpha aggregation, news reporting, and contextual outputs. They do not trade and use him simply as a pocket analyst,” Gallen said. AI Agents Within Crypto Exchanges While AI agents can help crypto investors trade intelligently, popular U.S.-based crypto exchange Coinbase has also started to explore these models. Dan Kim, head of strategy at Coinbase Developer Platform, told Cryptonews that Coinbase is currently focused on building infrastructure to allow AI agents to operate safely and autonomously. “This includes giving agents wallets, the ability to transact on-chain, and tools to charge or pay for services programmatically,” Kim said. He added that the infrastructure behind these models, like x402 and AgentKit, allows AI agents to interact with DeFi, pay for services, and perform economic activities safely across Coinbase. “This is essentially preparing the groundwork for future AI-native payments,” Kim noted. AI Agents Can Make Mistakes Although the potential behind AI agents and crypto use cases is huge, these models are far from perfect. While AI agents can ensure efficiency, accessibility, and risk discipline, Sneider explained, there are downfalls to consider: Data fragility: This is where poor inputs or unreliable oracles can cascade into bad trades. Overfitting: Agents trained on narrow historical data underperform in black swan events. Execution errors: We’ve seen cases where AI models hallucinate or misinterpret instructions. For instance, Sentient recently shared an example of an AI agent getting “stuck” in a transaction loop, firing the same order over and over. Without circuit breakers, this kind of loop can spiral quickly. Latency: Agents that depend on off-chain inference sometimes miss optimal execution windows. Gallen added that the most common mistake AI agents make when it comes to trading is purchasing “fake tokens.” “When a token is pumping, there are multiple copycat tokens that pop up. Even more so, these copycat tokens are artificially inflated to appear as the organic runner. AI can be tricked to buy these because they consider the on-chain volume as real,” Gallen explained. Although there are multiple safety mechanisms set in place, as well as checks and balances, Gallen noted that these occurrences happen every so often. Additionally, Gallen mentioned that API and tooling inconsistencies can be challenging. “This can happen when someone uses one API for conditional trading to execute trades, while using another API to source real-time data. One API can consider Market Cap and FDV as the same thing, while the other provider is much more meticulous in their classifications. This can cause AI to close trades early or result in another variety of outputs that can cause these models to fail at what they were intended to accomplish.” AI Agents Won’t Replace Human Traders Yet Although the potential behind AI crypto trading agents is massive, these models are not yet ready to replace humans entirely. According to Sneider, the beginning of “agentic finance” is just now taking place. However, he said that today’s early products show both promise and pitfalls. “AI can act as a co-pilot, but it must operate inside secure rails,” he said. As such, Sneider believes that AI agents won’t replace human traders, but rather they’ll extend them. “They’ll automate execution across both DeFi and TradFi, but always anchored in user-defined authority,” Sneider said