Key Takeaways
- The IMF predicts that a shift to agentic AI will trigger a radical increase in the velocity of money.
- Sydney Huang warns that the projected $236 billion agentic market by 2034 requires machine-speed regulatory oversight.
- Future stability for Human API and global banks depends on embedding framework mandates into code.
The End of Policy ‘Lag’
According to an April 2026 International Monetary Fund (IMF) report, the world is rapidly exiting the era of “click-to-pay” and entering the age of “decide-to-pay.” But as humans step out of the loop, an important question emerges: Can our financial guardrails survive a machine-speed economy?
The IMF report notes that agentic artificial intelligence (AI) is set to radically increase the velocity of money. By removing human “friction,” capital will circulate through the global economy at unprecedented speeds. Sydney Huang, CEO of Human API, suggests that we could see a 10-fold increase in the velocity of money. While this sounds like a productivity miracle, it presents a nightmare for central banks. Traditional monetary policy is built on “lag.” When a central bank raises interest rates, it takes months for that decision to filter through human institutions. In an AI-to-AI economy, that lag disappears.
“A 10-fold increase in the velocity of money driven by AI-to-AI commerce would require regulators to adopt tools that operate at machine speed,” Huang warns. Without these capabilities, a machine-speed inflation spike or a global flash crash could occur before a human regulator even receives a dashboard alert.
To prevent cascading failures, Huang argues that regulators must stop being spectators and become part of the code itself. “This includes real-time monitoring systems, programmable compliance embedded directly into financial infrastructure, and automated circuit breakers to prevent cascading failures,” she said. This vision aligns with the IMF’s proposed Three-Layer Framework, which suggests that the authorization layer of every transaction must have embedded, human-defined mandates.
Huang suggests that “regulators may also need to express policies in machine-readable formats that can be enforced at the transaction level.” Agentic commerce also requires automated circuit breakers at the transaction level so that when agents begin exhibiting highly correlated behavior, autonomous “fuses” must blow to stop the chain reaction.
The IMF report highlights that “agentic systems can interpret objectives and monitor activity in real-time.” This means know-your-customer and anti-money-laundering checks are programmed directly into the AI agent’s DNA.
Proving Decision Provenance
Perhaps one of the most complex challenges for regulators in this new era is the “invisible” marketplace. In a world where agents do not use human language to coordinate, the question arises: How do we distinguish between a bot simply optimizing and a fleet of bots colluding to fix prices?
Huang notes that this requires a shift from analyzing communication to analyzing behavior.
“Regulators will need to examine patterns such as synchronized actions, shared data dependencies and statistical anomalies,” she said. The solution may lie in “decision provenance.” Huang suggests a future where agents are required 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, there is the matter of how these agents actually talk to one another. Huang points out that safe agent-to-agent negotiation requires universal standards for identity, communication, and enforcement.
“Agents must be able to verify each other’s identity and authorization, operate within shared negotiation frameworks, and attach verifiable guarantees to their actions,” Huang said. This shift moves trust away from individual counterparties and places it into the system’s guarantees. By using emerging standards like the agent payments protocol (AP2) and the model context protocol (MCP), businesses can ensure that an agent from Company A can negotiate safely with an agent from Company B without a proprietary middleman.
As more governance is delegated to these digital proxies, a new human risk emerges: atrophy. If an agent manages a company’s treasury for five years without human intervention, will the human treasurer still know how to handle a crisis if the system goes dark?
Huang warns that as governance is increasingly delegated, there is a serious risk that human operators will lose the ability to intervene effectively. “Maintaining operational readiness is as important as building fallback mechanisms,” she said.
Combatting Human Skill Atrophy
To mitigate this, she argues that systems must hold regular drills where humans take the wheel and incorporate modes where humans simulate agent actions to compare logic. There is also a need to ensure the “kill switch” is a practiced pathway. “The goal,” Huang said, “is to ensure that human oversight remains functional and practiced, rather than theoretical.”
As the world moves toward a projected $236 billion agentic market by 2034, the definition of a “market participant” is changing. It is no longer just regulating people but the so-called “super-individuals” powered by thousands of autonomous bots.
The decide-to-pay revolution offers a world of frictionless efficiency, but it demands a total redesign of the global 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.