Agentic AI changed the cost equation. Multi-step reasoning, tool calls, and agent fan-out consume tokens with no natural ceiling — and under per-token pricing, your bill grows with every request. Cheaper tokens do not help when your agents consume them faster than prices fall.
Teams moving agentic workloads into production keep hitting the same wall: the cost structure breaks right as the product starts working. Token maxing was never a strategy — it is just what unmanaged agentic spend becomes.
This evening is about the other path: optimizing tokens instead of maxing them, and what it takes to run agentic AI at a fixed, predictable cost.
What we will cover
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Why agentic consumption outruns falling token prices
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The difference between maxing tokens and optimizing them
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How teams protect gross margin while scaling agentic products
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Practical approaches to predictable cost: reserved capacity, model routing, open-source models
Format
A short framing talk, a panel with operators living the agentic-cost problem firsthand, then drinks and conversation. Intentionally small — built for real discussion, not a crowd.
Who should attend
Engineering and AI leaders running agentic workloads in production, and the finance leaders responsible for the economics. Built for teams scaling from experiment to production.
Speakers
Panel to be announced. If you are living the agentic-cost problem and want to share your perspective, reach out — we are still shaping the conversation.
