Artificial intelligence agents are emerging as a new interaction layer for decentralized finance, with Coinfello co-founder and CEO Jacob C. arguing that they may become essential if DeFi is to scale beyond its current user base. In his view, the shift from manual onchain management to autonomous agents marks the beginning of a crypto “autopilot” era, where software handles many of the monitoring and execution tasks that once demanded constant user attention.
For years, DeFi participation has required users to stay alert to changing gas fees, slippage, liquidation thresholds, and shifts in market conditions. That workflow has favored experienced traders and institutions with the ability to monitor positions around the clock. AI agents, according to Jacob C., are beginning to close that gap by automating tasks that were previously practical only for hedge funds or other professional operators with full-time risk teams.
From Manual DeFi to Autonomous Execution
The central promise of AI agents in DeFi is not just convenience, but persistent oversight. Jacob C. pointed to use cases where agents can monitor pools and react when predefined risks appear. In some cases, an agent may automatically remove liquidity if it detects signs of a potential rug pull, or take defensive action when a stablecoin begins to drift away from its peg. That kind of continuous response system is increasingly seen as a way to reduce the burden on retail users who cannot watch the market every hour of the day.
Beyond execution, Coinfello’s thesis is that AI agents can also improve the way users understand smart contracts. Before such agents, Jacob C. said, users often had to rely on a centralized web interface — the dApp front end — to interpret what a smart contract was doing and to route them to the correct contract. That model introduced multiple layers of trust: users had to trust that the website described the contract honestly, pointed to the legitimate address, and had not itself been compromised by a malicious actor.
AI agents, he argues, can reduce some of that dependency by interfacing more directly with smart contracts, reading their logic, and explaining associated risks to the user. In that sense, the agent serves as a “translation layer” between human intent and smart contract complexity. For DeFi to reach a much broader audience, Jacob C. believes such a layer may become indispensable.
The “Translation Layer” Thesis
The translation-layer concept is important because DeFi remains difficult for mainstream users. Smart contracts may be transparent in theory, but they are rarely intuitive in practice. Most users still rely on interfaces, dashboards, and community explanations to understand what they are signing. AI agents could reframe that experience by turning natural-language instructions into onchain actions while also summarizing the risks involved.
That could significantly lower the usability barrier. Rather than manually navigating several dApps, comparing contract permissions, and tracking exposure across platforms, users might instead instruct an agent to execute a recurring strategy, monitor specific warning signals, or rebalance positions under certain conditions. Jacob C. sees this as a major leap in usability and a foundation for broader DeFi adoption.
At the same time, his comments suggest a future in which the user’s primary relationship is no longer with a traditional dApp interface, but with an intelligent agent capable of acting on their behalf. If that model takes hold, the role of dApps could shift from being the default gateway to becoming back-end infrastructure accessed through AI-driven layers.
New Risks Come With Higher Efficiency
Still, the Coinfello executive does not present AI agents as risk-free. While they can streamline workflows and improve reaction speed, they also introduce new vulnerabilities. One major concern is oracle dependency. If an agent relies on external data feeds and those feeds are inaccurate, manipulated, or delayed, then automated actions may be triggered on false assumptions. In highly volatile markets, flawed oracle inputs can quickly produce harmful outcomes.
Another concern is the gradual erosion of human agency. As decision-making shifts from individuals to algorithms, users may become less aware of what is happening with their capital in real time. Jacob C. warned that users should still be able to verify, inspect, or constrain an AI agent before surrendering meaningful control over funds or transaction authority. In his framing, automation should not become a blind delegation of trust.
He also criticized a common model in the current market: AI products that require users to transfer funds into a wallet fully controlled by the agent. In that setup, users are effectively trusting that the agent will neither make a mistake nor behave maliciously. For a sector built around self-custody and minimizing trusted intermediaries, that trade-off is a serious design issue.
Coinfello’s “Liquidity Sandboxing” Approach
To address that problem, Coinfello uses what Jacob C. described as “liquidity sandboxing”. The idea is to let users approve individual permissions for an AI agent rather than handing over unrestricted access. These permissions can limit which tokens the agent may access and how far its authority extends. According to Coinfello, such guardrails are intended to solve fundamental safety concerns around the use of AI agents in crypto.
This model aligns with a more cautious vision of autonomous finance: agents may act, but only within clearly defined boundaries set by the user. Instead of replacing self-custody, the agent is meant to operate inside it. That distinction matters because the future of AI in DeFi may depend less on how powerful agents become and more on whether users retain meaningful control over assets, permissions, and the ability to intervene.
Coinfello has also launched what it describes as a self-sovereign AI agent that allows users to automate onchain transactions using natural language while maintaining full self-custody. The company is positioning this approach as a path not only for individual users, but also for institutional adoption built on decentralized infrastructure.
A Future Where dApps Are No Longer the Main Interface
Looking ahead, Jacob C. expects AI agents to automate many actions that users either do not have time to monitor or cannot reliably execute on their own. He specifically mentioned workflows such as dollar-cost averaging and the execution of personally defined trading strategies. These are practical examples of how agents could become persistent assistants rather than occasional tools.
His most striking prediction is that by 2030, decentralized applications may decline to the point that they are no longer the primary way people use smart contracts. That does not necessarily mean smart contracts become less important. Instead, it suggests the interface layer could change dramatically. Users may stop interacting through buttons, forms, and browser dashboards, and instead rely on agents that understand intent, translate complexity, and execute within preset boundaries.
If that shift happens, the implications for the DeFi ecosystem would be significant. Product design, security assumptions, compliance tooling, and user education would all have to adapt to a world where AI agents stand between the user and the protocol. The key challenge will be making those agents trustworthy without reintroducing the centralized dependencies that decentralized finance was meant to avoid.
For now, Jacob C.’s argument captures a growing debate inside crypto: whether AI can become the usability breakthrough that DeFi has long needed, and whether that breakthrough can be delivered without sacrificing transparency, self-custody, and user control. The opportunity is considerable, but so are the open questions around safe delegation, oracle integrity, and the limits of algorithmic decision-making.