How to Cut GenAI and Agent Token Spend Without Cutting Capability

This article provides a detailed guide on optimizing token spend in GenAI and agent workflows without sacrificing capability. It emphasizes moving beyond simple per-call metrics to a holistic 'cost per successful task' approach, tracking input, output, cached, and reasoning tokens alongside agent turns, retries, and validation calls. Key strategies include building a token control plane with admission policies, context builders, routers, and output contracts; separating token reduction from rate reduction; and using run-level telemetry to enforce cost limits. The piece is aimed at developers and engineering teams deploying production AI agents, offering actionable techniques to reduce context, model calls, loops, and unit costs while preserving task quality.