How are people actually attributing cost to AI agents?

I've been thinking about this because the term "LLM spend" feels like an incomplete way to measure what an agent really costs. Imagine a company has 40 agents spread across 8 teams. An individual agent might have: LLM inference tool or API calls vector DB usage retries browser or compute time human approval or review calls to other agents So if the monthly AI bill is $18k (hypothetically) how do you actually answer: Which agent cost the most? Which team should cover the cost? Which workflow is actually expensive? How much of the cost came from retries or from agents? What should the actual unit of measurement be? Agent / User / Team / Workflow / Task / Outcome The last one seems tricky once agents start calling other agents. I've seen people use things like LiteLLM or Portkey or broader AI infrastructure platforms, like TrueFoundry. Lyzr's Control Plane also has agent-level budget caps and cost attribution as part of the system. I'm curious to know what people are actually doing in life: Do you have a cost model that still works when you have multi-agent workflows or are most teams still just looking at the model-provider bill?