Are AI agent costs stopping you from scaling?

As AI agents move from experiments into everyday operations, token costs become a recurring business expense. A workflow that looks affordable in a demo can be difficult to justify at scale.

I’m building ContextOS to make AI agent execution cheaper while preserving useful outcomes. I’d love to understand where cost becomes a constraint for you:

• Have you limited or abandoned an agent workflow because it was too expensive?

• Is the bigger challenge understanding costs, predicting them, or reducing them?

• What would lower execution costs let you build or deploy?