Best Token Use Path (BTUP): Client-Side Traffic Engineering for Cost-Aware LLM Inference

Large language models select generation behavior against a trained quality objective that contains no representation of token cost — the inference analogue of a link-state routing protocol whose advertisements omit the cost field. Every path looks free, so the protocol systematically prefers verbose, expensive routes: preamble, restatement, hedging, full-file rewrites, and unbounded agent loops. Prior work attacks components of this problem — model cascades and routing, prompt compression, reasoning budgets, length control — but treats each as an isolated optimization. We introduce Best Token Use Path (BTUP): a provider-independent, client-side traffic-engineering framework that represents an AI request as a complete execution path (context policy, retrieval, prompt transform, cache strategy, provider/model, reasoning and output budgets, output contract, tool plan, validation, and retry/escalation policy) and selects the path minimizing expected cost per accepted resolution (CAR) subject to quality, latency, safety, privacy, and budget constraints. We specify the control plane — edge classification, service-class policy, a Path State Database, constrained path computation, validator-driven fast reroute — together with a provider-accounting reconciliation model, privacy-preserving telemetry, and a security model covering denial-of-wallet, tool-loop containment, and refusal handling. We describe a production implementation and report 🔬 [N weeks] of live results: 🔬 [X]% CAR reduction at 🔬 [Y] change in acceptance. We argue the methodology grows in importance regardless of price direction, because agentic workloads multiply tokens-per-task faster than unit prices decline.