The recent surge in AI agents has led to the emergence of 'agentic finance,' a novel concept where AI systems execute financial transactions, making crypto the essential financial infrastructure for autonomous systems. In this article, Vincent Chok from First Digital delves into the world of AI agents in crypto, discussing their growth, use cases, and the necessary steps for scalability. We also posed questions to leading AI models, Grok, Gemini, and Claude, about AI payment use cases and the requirements for widespread adoption. The responses, generated by AI assistants, provide valuable insights into the potential of AI-driven finance.
The explosive growth of AI agents, with 79% of companies already adopting them, reflects a broader shift towards machine-driven economies. Initially used for advisory roles, AI systems are now actively planning, deciding, and acting on predefined parameters, including financial transactions. This has led to the formation of 'agentic finance,' a new paradigm where AI agents execute financial actions within predefined rules. Agentic finance can be broken down into 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.
Theoretically, AI agents have a use case in the financial space, but they don't fit neatly into traditional financial infrastructure. Crypto, with its programmable money, instant and global settlement, and permissionless access to funds, is increasingly becoming the infrastructure for autonomous systems. Early implementations of AI agents are already visible, with machine-to-machine payments, autonomous commerce, and trading agents being widely deployed.
Beyond use cases, AI agents are driving new investable categories and 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. Despite the momentum, there are risks and limitations, particularly around security, authorization, liability, and regulatory treatment. For widespread adoption, building trust for users is crucial, which comes through regulatory clarity and safeguarding user funds and interests.
Over the next twelve months, the technology will continue to grow and mature, with signals including growth in agent-driven transaction volume, emergence of agent-native wallets and payments protocols, and deeper integration between stablecoins and AI-driven systems. 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, and advisors should track it as a next-wave driver of crypto utility.