Governing the Agentic Horizon

Generative AI is crossing a structural threshold. Corporations are moving from experimental models embedded in isolated workflows to agentic systems that can plan, use tools, and act across the business. Capital expenditure is following: in 2025 alone, hyperscale firms committed nearly $400 billion to AI data centers and hardware. Yet most enterprises report no measurable productivity uplift from their AI programs.

This is the new productivity paradox. The core problem is not the quality of models, but the governance logic around them. Most organizations still treat AI as a set of discrete tools to be supervised one task at a time—a “Limited Reserves” mindset inherited from traditional IT. Every automatic action is treated as a unit of work that must be watched, approved, or manually routed. At small scale this works; at agentic scale it becomes a systemic bottleneck.

The right mental model comes from an unexpected place: central banking. After the 2008 crisis, the U.S. Federal Reserve shifted from a world of scarce reserves—fine-tuning the quantity of money day by day—to a “Floor System” operating in an environment of ample reserves. Instead of micromanaging every transaction, the Fed began administering a set of rates and backstops that shape behavior system‑wide.