Agentic AI workflows trigger multiple model calls per task, driving token use 5–30× higher than simple chatbots and blowing through pilot budgets in production. Hidden expenses like re-sent context, context rot, tool orchestration, state management, and retries further inflate operating costs. The article breaks down these layers and offers strategies to control spend before your invoice arrives.

• Agentic workflows use 5–30x more tokens per task than simple chatbot queries, so falling per-token prices don't prevent exploding total bills (Uber's engineers burned $500–$2,000/month each after Claude Code rollout).
• 62% of agent inference spend comes from redundantly re-sending the same system prompts, tool definitions, and state histories at every step (Stanford Digital Economy Lab).
• Longer context windows actually degrade model accuracy ("context rot"), forcing costly trimming/segmentation on top of orchestration and state-management overhead.
• Auditing token use, routing simple subtasks to smaller models, and cutting redundant context cut one client's monthly spend from $40,000 to $24,000 with no product changes.