The math isn't complicated. It's just that nobody runs it until they get the bill. An AI agent handling a 10-turn workflow — reading files, calling tools, revising output — doesn't cost 10x a single query. Because transformer inference processes the entire context on every call, cost compounds with each additional turn. The tenth turn carries everything that preceded it: the original file reads, every tool call and its return payload, every intermediate plan and revision. A team that models agent cost as "turns × average cost per turn" will consistently underprice their system by 3x to 5x. This is the context window cost problem. It is structural, not anecdotal. And in 2026, with context windows exceeding 200,000 tokens and frontier model input pricing in the range of $2.50–$5 per million tokens, it has become one of the most significant and least-governed cost drivers in production AI systems. Why Context Compounds Transformer-based language models have no native memory across turns.…