AI agents behave very differently from human users. A single agent task can trigger 50+ cascading API calls, retry failures, and recurse based on results.

- Uncontrolled fan-out (one goal → 50 parallel requests) - Legacy SOAP/XML responses eating 5000+ tokens - No way to group agent requests into logical "goals" - Rate limiters built for humans failing on agent bursts

Is this actually a problem you're facing? How common is this in production? or i'm not seeing this problem common because most of the AI agent still not in production and just in pilot or testing phase?