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A 50% drop in token price does not lead to a 50% drop in agent cost. This linearity fallacy often creates a 40-60% TCO underestimation gap.

Most people treat LLMs like vending machines where you pay for a single output. Enterprise agents act more like project managers.

They do not just predict the next word. They plan, execute tools, observe results, and iterate.

One user request can trigger ten LLM calls. Each turn in that loop consumes tokens.

Then there is the invisible token tax. Every single call carries the weight of system instructions, tool definitions, and conversation history. The actual user query is often the smallest part of the prompt.

For an ops leader, this means the cost floor is much higher than the model's sticker price. The budget breaks when CRM sync costs or orchestration overhead outweigh the base token spend.

I condensed the math behind these hidden drivers into a 12-page visual field guide.

The full breakdown including the iteration cycle diagram is in the guide below.

How are you accounting for iteration loops and prompt overhead in your current AI budget?

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