You deploy a four-agent pipeline that should cost about $0.80 per run. By end of day it has burned through $47 on a single stuck researcher loop. Sound familiar?
If you're running AI agents in production, cost blowups are not a question of if but when. 57% of organizations already have agents in production, yet 90% of agent projects fail within 30 days — and runaway LLM costs are the number one pain point.
The core problem: agents make autonomous decisions about how many LLM calls to issue. A retry loop, an overly verbose chain-of-thought, or a stuck tool call can silently 10x your bill before you notice.
The Current State of Agent Cost Control
Most teams handle this with one of three approaches, all of which fall short:
