The current financial landscape is witnessing a significant shift with the emergence of AI agents that are transforming the way financial transactions are executed. According to a recent PwC survey, a staggering 79% of over 300 companies are already leveraging AI agents in some capacity, marking a substantial evolution from their initial advisory roles to execution roles. This phenomenon has given birth to 'agentic finance,' a novel concept where AI agents perform financial actions within predefined parameters, including limits, permissions, and goals, set by humans.
To grasp the concept of agentic finance, it's essential to break it down into three distinct layers: the agentic commerce layer, which focuses on discovery and decision-making; the agentic payments layer, responsible for handling execution; and the asset management layer, representing the full stack where agents can manage portfolios, handle payments, and dynamically optimize financial strategies based on real-time market trends. Although this may seem like granting full autonomy to AI agents, it's actually a form of conditional delegation, where users retain control through constraints while offloading execution to the agents. Theoretically, AI agents have a viable use case in the financial space, but they don't seamlessly integrate with traditional financial infrastructure. They lack direct access to global banking rails and are designed to operate around the clock, creating a structural mismatch.
This is where crypto comes into play, offering AI agents access to programmable, always-on money through stablecoins, enabling instant and global settlement via blockchains, and providing permissionless access to funds through crypto wallets. Essentially, these components form a financial layer that is better suited to machine-driven activity, positioning crypto as the infrastructure for autonomous systems rather than just an asset class. Early implementations of AI agents are already visible, with machine-to-machine payments powered by API access and data providers making inter-merchant rails stronger and faster.
In the consumer context, autonomous commerce has allowed users to optimize retail research, using agents to get the best deals for travel, subscriptions, and shopping. Meanwhile, in crypto-native environments, trading agents are widely deployed for portfolio management, yield optimization, and trading strategies. On the enterprise side, supply chain management and vendor payments have been easily automated via AI agents, cutting down on errors and resource expenditure. At this stage, most activity remains business-to-business and infrastructure-driven rather than consumer-facing.
Beyond use cases, AI agents also play a crucial role in driving new investable categories as well as demand for crypto itself. As AI agents cannot operate on existing infrastructure rails, demand is growing for agent-native wallets, stablecoin payment rails, and data or compute marketplaces. For instance, Coinbase has launched x402, an open payments protocol designed for agent-native transactions, which is particularly relevant for micropayments where high transaction volumes and low value make traditional rails inefficient. For the first time, non-human users are participating in the financial system and driving activity, with AI agents becoming a new class of 'user' for crypto networks.
Despite the momentum, we are still in the early stages, and there are risks and limitations. Security is the primary concern, particularly around rogue or exploited agents executing unintended transactions. Questions around authorization, liability, and regulatory treatment are still under scrutiny and are being actively defined.
For widespread adoption, building trust for users is crucial, which comes through regulatory clarity from all involved stakeholders, allowing projects to build with clarity and confidence while safeguarding user funds and interests. Over the next twelve months, this technology will continue to grow and mature. Key signals to watch include growth in agent-driven transaction volume, the emergence of agent-native wallets and payments protocols, and deeper integration between stablecoins and AI-driven systems. Regulatory clarity will heavily shape the pace and scope of adoption across different industries and fields.
In conclusion, AI agents are not just a theoretical concept; they are already executing transactions in limited environments. As this trend develops, crypto is increasingly emerging as the financial backend for machine-driven economies.
For now, this is an infrastructure and long-term thematic play; however, that is changing with rising adoption rates. Advisors should track it as a next-wave driver of crypto utility. In a unique approach, we posed two questions to three leading AI models - Grok, Gemini, and Claude - about the present and future of AI payments, exploring what AI payment use cases are currently visible and what is needed for AI payments to scale.
The responses highlighted common themes, such as the need for standardized agent identity, governance, and high-quality real-time data infrastructure, as well as distinct differences in their perspectives, making for a thought-provoking insight into the future of AI in finance.