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The article shows how real-world agentic AI deployments can blow through budgets because multi-step workflows use 5–30× more tokens per task than simple chatbots. It breaks down four hidden cost layers—LLM inference with re-sent context, context rot, tool orchestration, and infrastructure—and offers strategies to curb runaway spending before your production bill arrives.
- Agentic workflows use 5–30× more tokens per task than simple chatbot queries because each task triggers 10–20 model calls that reprocess the same context each time.
- Uber's Claude Code rollout to 5,000 engineers burned through the company's entire annual AI budget in one month, costing $500–$2,000 per engineer.
- Stanford research attributes 62% of agent inference costs to re-sending the same prompts and history on every call, and larger contexts also degrade model accuracy ("context rot"), triggering more retries.
- Routing simpler queries to cheaper, smaller models cut one team's monthly bill from $40,000 to $24,000, though falling per-token prices won't offset runaway consumption growth.
agentic-ai
+ token-economics
+ context-management
ai-costs
+ inference-efficiency
+ context-rot
+ inference