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Agentic AI changed the cost equation. Multi-step reasoning, tool calls, & agent fan-out consume tokens with no natural ceiling - & 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, & what it takes to run agentic AI at a fixed, predictable cost.
What we will cover
Why agentic consumption outruns falling token prices
The difference between maxing tokens & optimizing them
How teams protect gross margin while scaling agentic products
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 & conversation. Intentionally small - built for real discussion, not a crowd.
Who should attend
Engineering & AI leaders running agentic workloads in production, & 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 & want to share your perspective, reach out - we are still shaping the conversation.
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