Unlocking the Potential of Crypto for AI-Powered Financial Systems

The rise of AI agents has transformed the financial sector, with these agents evolving from advisory roles to executing financial transactions, thus forming the foundation of 'agentic finance'. A recent survey by PwC revealed that 79% of over 300 companies have adopted AI agents in some capacity, underscoring the rapid growth of this technology. Initially, AI systems were used for chatbot services and copiloting roles, but they now actively plan, decide, and act on predefined parameters, including financial transactions, under the guidance of human-set rules such as limits, permissions, and goals. This has led to the emergence of 'agentic finance', a new paradigm where AI agents execute financial actions within predefined rules. The concept of 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, enabling agents to manage portfolios, handle payments, and dynamically optimize financial strategies based on real-time market trends. While this may seem like granting AI agents full autonomy, it is actually a form of conditional delegation, where users retain control through constraints while offloading execution to the agents. Theoretically, AI agents have a use case in the financial space, but they do not fit neatly into traditional financial infrastructure. Structurally, AI agents lack direct access to global banking rails and are designed to operate 24/7, creating a structural mismatch that crypto can address. Crypto offers AI agents access to programmable, always-on money through stablecoins, enables instant and global settlement via blockchains, and provides permissionless access to funds through crypto wallets. Essentially, these components form a financial layer better suited to machine-driven activity, making crypto 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. Autonomous commerce has allowed users to optimize retail research, using agents to get the best deals for travel, subscriptions, and shopping. 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 a crucial role 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. 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. Despite the momentum, there are risks and limitations, 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, building trust for users is crucial, which comes through regulatory clarity from all involved stakeholders, enabling 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, 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. 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, but that is changing with rising adoption rates. Advisors should track it as a next-wave driver of crypto utility. In a unique experiment, a panel of AI experts, including Grok xAI, Gemini, and Claude, were asked about the present and future of AI payments. The experts highlighted the most mature use case of fraud detection, where AI spots anomalies that rules-based systems miss, and emerging agentic payments that let autonomous AI handle B2B treasury tasks and machine-to-machine micropayments using stablecoins. They also identified gaps that need to be closed for AI payments to scale, including standardized agent identity with cryptographic proof of authorization and clear liability rules, governance and explainability for regulators, and high-quality real-time data infrastructure bridging fiat and crypto. Advisors must demonstrate that AI augments rather than replaces fiduciary duty.