One failed AI workflow is annoying. One successful workflow that quietly costs more than the customer paid is worse.
That is the uncomfortable gap many builders hit after the demo works. The agent can search, retrieve, call tools, draft outputs, and recover from errors. But before a user clicks Run, the product often has no honest answer to a simple question: How much could this job cost?
This guide shows how to build AI agent cost forecasting into your product workflow before spend hurts pricing, reliability, or trust.
The goal is not to make every token predictable. The goal is to make cost visible enough that your app can choose safer routes before money disappears.
Why Cost Forecasting Is Becoming a Product Feature
