The current financial landscape is witnessing a significant shift with the emergence of AI agents that are capable of executing transactions, marking the beginning of 'agentic finance.' As these autonomous systems continue to evolve, crypto is becoming the essential financial infrastructure for their operation. In this newsletter, Vincent Chok from First Digital explores the concept of agentic finance, where AI agents move beyond providing advice to executing financial transactions, with crypto serving as the critical financial backend.
The newsletter also features an 'Ask an Expert' segment, where three leading AI models - Grok, Gemini, and Claude - share their perspectives on AI payment use cases and the necessary steps for scalability. It's crucial to note that the responses from these AI models should not be considered as financial or legal advice.
The rise of AI agents in crypto is a topic that advisors need to be aware of, given the explosive growth of these agents over the last year. According to a recent PwC survey, 79% of companies are already adopting AI agents in some form, reflecting a broader shift from advisory roles to execution roles. Initially, AI systems were deployed for chatbot services and copiloting roles, but they are now actively planning, deciding, and acting on predefined parameters set by humans, including financial transactions.
This has led to the early formation of 'agentic finance,' a new concept where AI agents execute financial actions within predefined rules such as limits, permissions, and goals. Agentic finance can be understood in three layers: the agentic commerce layer, which focuses on discovery and decision-making; the agentic payments layer, which handles execution; and the asset management layer, which represents the full stack, where the agent can manage portfolios, handle payments, and dynamically optimize financial strategies based on real-time market trends.
While this may seem like giving AI agents full autonomy, it's actually conditional delegation, where users retain control through constraints while offloading execution. Theoretically, AI agents have a use case in the financial space, but they don't fit neatly into existing traditional financial infrastructure.
Structurally, AI agents lack direct access to global banking rails and are designed to operate 24/7, which is where crypto comes into play. Stablecoins offer AI agents access to programmable, always-on money, blockchains enable instant and global settlement, and crypto wallets provide permissionless access to funds.
Essentially, these components form a financial layer that is better suited to machine-driven activity, making crypto increasingly 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 an integral part in driving new investable categories as well as demand for crypto itself.
As AI agents can't 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, there are risks and limitations associated with AI agents, with security being 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, it's essential to build trust for users, which comes through regulatory clarity from all involved stakeholders, so projects can build with clarity and confidence while safeguarding user funds and interests. Over the next twelve months, this technology will continue to grow and mature, with signals that matter including growth in agent-driven transaction volume, emergence of agent-native wallets and payments protocols, and deeper integration between stablecoins and AI-driven systems. Finally, regulatory clarity will heavily shape the pace and scope of adoption across different industries and fields. In conclusion, AI agents are not a theoretical concept; they are already executing transactions in limited environments.
As the 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 the 'Ask an Expert' segment, three leading AI models were asked about the present and future of AI payments.
The first question was about the AI payment use cases being seen today. Grok xAI responded that fraud detection is the most mature use case, with AI spotting anomalies that rules-based systems miss. Intelligent payment routing dynamically selects the best rail by cost and speed, while emerging agentic payments let autonomous AI handle B2B treasury tasks and machine-to-machine micropayments using stablecoins, with Visa, PayPal, and Mastercard beginning to verify these agents.
The second question was about what's needed for AI payments to scale. Grok xAI replied that three gaps need closing: standardized agent identity with cryptographic proof of authorization and clear liability rules; governance and explainability for regulators, including AML compliance in automated wealth flows; and high-quality real-time data infrastructure bridging fiat and crypto. Advisors must demonstrate that AI augments rather than replaces fiduciary duty.