In this article, you will learn how token costs silently compound in agentic AI loops, and what architectural patterns you can use to control them before they escalate.
Topics we will cover include:
Why token costs compound non-linearly in multi-step agentic workflows, and how the distinction between state and context is central to controlling them.
Five distinct failure modes — from O(N²) context accumulation to static system prompt duplication — that account for the bulk of runaway token spend in production deployments.
Practical mitigations for each trap, including context compaction, circuit breakers, payload filtering, dynamic model routing, and runtime prompt injection.
