About this webinar
Running AI models in production, what the industry calls inference, is now the largest line item in most enterprise AI budgets. Agentic workloads are the primary driver: a single agent task can consume five to thirty times the tokens of a simple chat reply because loops and retries call the model repeatedly at each step.
Gartner has a name for the problem: "runaway AI costs."
In this session, we discuss agentic AI as a system you budget and govern. The approach rests on five practical controls, each one sitting within the architecture itself. You'll see all four controls plus a validation step in a live walkthrough of Aera's Agentic Decision Intelligence Platform, which configures a model per agent, sets a token budget for each LLM connection, runs in hybrid mode, and monitors consumption in real time.
Join us to see how a disciplined, architecture-level approach turns unpredictable inference spend into a cost you plan, monitor, and control.
You'll learn how to:
- Set a token budget for each LLM connection, so spend is capped by design rather than discovered after the fact
- Match the right model to each task, and validate outputs with a cheaper model to keep quality high and costs low
- Stay model-agnostic with pass-through flexibility, so you route work to the best-fit model for the job
- Default to deterministic engines for automation, reserving agentic reasoning for the exceptions that truly need it
Together, these controls form a repeatable approach you can apply across your own agentic workloads, giving you the confidence to scale agentic AI without losing sight of what it costs.
.jpg?width=1000&height=750&name=Landing%20page%20and%20blog%20recap%20cover%20(16).jpg "Tokenomics for Agentic AI: Planning, Monitoring, and Controlling What Your Agents Spend.")
