While security and data privacy are huge risks with AI agents, the most immediate and tangible pain point for businesses is cost. An unexpectedly large bill from a runaway agent is often the catalyst for seeking a governance solution, which then leads to addressing deeper security issues.

The jump to capable AI agents has shifted enterprise cost structures. AI is no longer a predictable per-seat software license but a variable consumption cost, akin to labor. This explains why companies are suddenly "torching" their budgets—they were budgeting for tools, not autonomous workers.

Similar to "Shadow IT," employees are using powerful, unmanaged AI agent tools without corporate oversight. These "shadow agents" can gain the same system access as a powerful employee but without any identity, limits, or oversight, creating a significant and often invisible risk for CISOs and CTOs.

An anecdote about an engineer spending $100M in a month on AI tokens reveals a core enterprise issue. For Lenovo's CFO, the problem isn't the amount but its lack of planning and clear ROI. This signals a shift from predictable software subscriptions to volatile, usage-based AI compute costs.

Microsoft's new autonomous AI agents, like Scout, operate continuously in the background, creating a major risk of uncontrolled token consumption and budget overruns for enterprise customers. While control tools exist, the fundamental model presents a new financial challenge for IT departments.

The shift to agentic AI means costs are no longer predictable per-seat subscriptions but variable expenses based on usage (tokens, compute). This requires managing AI like a capital allocation or a new form of labor, not just another software tool, a reality that early adopters are now grappling with.

The move away from seat-based licenses to consumption models for AI tools creates a new operational burden. Companies must now build governance models and teams to track usage at an individual employee level—like 'Bob in accounting'—to control unpredictable costs.

To avoid chaotic spending, enterprises must replicate their "Cloud 2.0" governance models for AI. This means establishing a central platform engineering team to broker access to models, set budgets, and control the tools agents can use. This prevents runaway costs and security risks from decentralized AI development.

Heavy use of AI agents and API calls is generating significant costs, with some agents costing $100,000 annually. This creates a new financial reality where companies must budget for 'tokens' per employee, potentially making the AI's cost more than the human's salary.