The International Monetary Fund (IMF) warned in an April 2026 report that the world is rapidly moving from a 'click-to-pay' era to a 'decide-to-pay' age, raising concerns about whether financial safeguards can withstand a machine-speed economy. The IMF predicts that agentic AI will radically increase the velocity of money, eliminating human 'friction' and enabling capital to circulate at unprecedented speeds.
Sydney Huang, CEO of Human API, warns that money velocity could increase tenfold, creating a nightmare for central banks. Traditional monetary policy relies on 'lag' — it takes months for interest rate changes to filter through human institutions. In an AI-to-AI economy, that lag disappears. 'A 10-fold increase in velocity driven by AI-to-AI commerce would require regulators to adopt machine-speed tools,' she said. Without these, a machine-speed inflation spike or flash crash could occur before a human regulator even sees a dashboard alert.
Embedding Regulation into Code
To prevent cascading failures, Huang argues regulators must stop being spectators and become part of the code itself. 'This includes real-time monitoring, programmable compliance embedded in financial infrastructure, and automated circuit breakers to prevent cascading failures.' This vision aligns with the IMF's Three-Layer Framework, which mandates that the authorization layer of every transaction must carry human-defined mandates. Huang also suggests that regulators may need to express policies in machine-readable formats enforceable at the transaction level. Agentic commerce requires automated circuit breakers at the transaction level so that when agents begin exhibiting highly correlated behavior, autonomous 'fuses' blow to stop chain reactions.
The IMF report highlights that 'agentic systems can interpret objectives and monitor activity in real time,' meaning know-your-customer and anti-money-laundering checks are programmed directly into the AI agent's DNA.
Proving Decision Provenance
One of the most complex challenges is the 'invisible' marketplace. When agents don't use human language to coordinate, how do regulators distinguish between optimization and collusion? Huang says regulators must shift from analyzing communication to analyzing behavior: 'They need to look for synchronized actions, shared data dependencies, and statistical anomalies.' The solution may lie in 'decision provenance' — requiring agents to provide verifiable proof that decisions were made independently under a declared policy. By proving how a decision was reached, agents can demonstrate they were not secretly coordinating with competitors.
Beyond regulation, safe agent-to-agent negotiation requires universal standards for identity, communication, and enforcement. 'Agents must verify each other's identity and authorization, operate within shared negotiation frameworks, and attach verifiable guarantees to their actions.' This trust shift relies on emerging standards like the agent payments protocol (AP2) and model context protocol (MCP), allowing Company A's agent to negotiate safely with Company B's agent without a proprietary middleman.
Human Skill Atrophy Risk
As governance is delegated to digital proxies, a new risk emerges: atrophy. If an agent manages a company's treasury for five years without human intervention, will the human treasurer know how to handle a crisis if the system goes dark? Huang warns that as governance is delegated, there is a serious risk human operators will lose the ability to intervene effectively. 'Maintaining operational readiness is as important as building fallback mechanisms.' She recommends regular drills where humans take the wheel, and modes where humans simulate agent actions to compare logic. The 'kill switch' must be a practiced pathway. 'The goal is to ensure human oversight remains functional and practiced, rather than theoretical.'
With the agentic market projected to reach $236 billion by 2034, the definition of a 'market participant' is changing. It's no longer just regulating people, but 'super-individuals' powered by thousands of autonomous bots. The decide-to-pay revolution offers frictionless efficiency but demands a total redesign of financial architecture. As Huang puts it, to govern a machine-speed economy, the law itself must become machine-speed. If we fail to embed the human-in-the-loop at the architectural level, we risk building an economy that moves too fast for its creators to control. Blockchain analytics firm Nansen predicts that by 2028, most people will invest not by picking coins but through AI agent dominance.