The current financial landscape is witnessing a significant shift with the emergence of AI agents, which are transforming the way transactions are executed. In this article, Vincent Chok from First Digital delves into the concept of 'agentic finance,' where AI agents are moving beyond providing advice to executing financial transactions, making crypto the essential financial backend for this machine-driven economy.
In a recent survey of over 300 companies by PwC, 79% of respondents stated that they are already adopting AI agents in some form. This growth reflects a broader shift, where AI agents are evolving from advisory roles to execution roles. Initially deployed to assist with chatbot services and copiloting roles, AI systems are now actively planning, deciding, and acting on predefined parameters set by humans, including financial transactions.
The result is the early formation of 'agentic finance,' a new paradigm wherein 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. Theoretically, AI agents have a use case in the financial space; however, they do not fit neatly into existing traditional financial infrastructure. Structurally, AI agents lack direct access to global banking rails and are designed to operate 24/7.
This structural mismatch is where crypto comes into play, offering AI agents access to programmable, always-on money, blockchains enabling instant and global settlement, and crypto wallets providing permissionless access to funds. Early implementations of AI agents are already visible, with machine-to-machine payments powered by API access and data providers making the 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. Beyond use cases, AI agents also play an integral part in driving new investable categories and 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. Despite the momentum, there are risks and limitations associated with AI agents, particularly around security, authorization, liability, and regulatory treatment.
For widespread adoption, it is 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. 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.