Your AI Agents Are Writing Checks You Can’t See

In the depths of a company's production environment, two AI agents are secretly conversing, working together without anyone's knowledge. These agents function flawlessly within their boundaries, executing tasks, making API calls, and querying models. Before anyone even realizes there's an issue, thousands of dollars vanish into thin air.

Each individual action might appear rational, and the system behaves precisely as instructed, but there's a crucial question that almost no enterprise can answer: should an agent take a particular action at this cost? As more agents get deployed, this gap transforms into the defining financial risk of the moment. The issue is reminiscent of the early cloud adoption days when companies learned the hard way that spending money could outrun the ability to track it.

Engineers could provision infrastructure in mere minutes, while finance teams didn't notice the costs for months. Though painful, this was manageable in human timeframes. However, autonomous agents don't adhere to human timeframes. They continuously call APIs and provision cloud resources around the clock, without waiting for explicit approval for each action.

A single customer service agent can generate thousands of billable actions daily, while an enterprise running hundreds of agents across various departments faces exposure at an entirely different scale. The stakes are concrete. By late 2025, average planned AI spending per organization reached $124 million, and 88% of executives plan to increase AI budgets in the next year specifically due to agentic AI.

Agent deployment more than doubled among large enterprises last year. Yet, the governance infrastructure to match that growth is missing. Only 42% of organizations are redesigning workflows around agents, despite half of those executives predicting their businesses will change drastically within two years. Clearly, something must change.