As agentic AI workflows scale, token consumption — not model pricing — is emerging as the dominant cost driver. A moderately complex agent request can consume 20,000–60,000 tokens, while nontrivial engineering tasks can burn 150,000–200,000. Multi-agent architectures compound this further, as each handoff encodes state and instructions into downstream context windows. Three practical mitigation strategies are outlined: (1) context compression with a compact memory layer to preserve reasoning without replaying full history, (2) hierarchical model routing that assigns routine subtasks to cheaper, smaller models, and (3) semantic caching using embeddings to reuse prior reasoning for similar requests. The broader point is that architectural decisions around context management, task decomposition, and work reuse are becoming as important as inference pricing — and the systems that scale best will be those that waste the fewest tokens, not necessarily those using the cheapest models.
