Where agent systems quietly waste spend once they move past demos

I have been thinking about a pattern that shows up when AI agents move from prototype to something closer to production. The cost problem is not always a single bad model call. It is usually a stack of small leaks: Agents resend too much context. Simple tasks get routed to expensive models. Failed or low-quality outputs trigger retries. Teams do not have a clean receipt for what happened. Nobody knows which agent paths are actually worth the spend. The uncomfortable part is that this can still look like the system is working. The output may be acceptable. The demo may look impressive. But under the hood, the workflow is burning tokens in places nobody is measuring. The question I keep coming back to: What should the runtime layer actually decide before an agent calls a model? My current list: task type required reasoning level context size model routing rule governance rule fallback path quality threshold cost receipt Curious how other teams are thinking about this. Are you routing agent calls manually, using a gateway, building custom logic, or just eating the model spend for now?