From Token Runaway to Cost-Native: How AI Studios Can Cut Agent Operating Expenses 10x

As autonomous AI agents move from experimentation to production deployment, token costs emerge as the primary operational constraint—not model capability. This case reveals a critical architectural flaw: stateless LLMs retransmit full conversation history on every invocation, especially costly when prompt caches expire between timer-based wake-ups. The solution—routing tasks by complexity to appropriate models and eliminating timer-triggered self-invocation of expensive models—applies to every organization scaling autonomous agent systems.