Cut AI Agent Costs with TokenOps
TokenOps frameworks can reduce AI agent costs 10x by optimizing cached context in agentic loops
“The output is actually at five times the cost of what you would think a typical token would cost”
Microsoft Frontier Company chief data scientist Gareth Bland introduced 'TokenOps' at Build — a data science discipline for managing LLM token spend in production agentic systems. The core insight is that agentic loops exhibit quadratic cost growth: a 4x increase in turns produces a 16x cost increase due to accumulating uncached context, but aggressive cache utilization can cut input costs to one-tenth of baseline. The talk proposed an 'agentic efficiency score' built from autonomous completion rate and other metrics as the primary operational telemetry for AI agents.