---
title: "Tokenomics for Agentic AI: Planning, Monitoring, and Controlling What Your Agents Spend."
description: Inference is now the largest line in most enterprise AI budgets. Five architecture controls that hold agentic AI to a budget, plus a live demo.
---

[Skip to content](https://meet.aeratechnology.com/tokenomics-for-agentic-ai-planning-monitoring-and-controlling-what-your-agents-spend-july02#main-content)

[![Aera Technology logo](https://meet.aeratechnology.com/hs-fs/hubfs/Aera%20Technology%20logo-1.png?width=113&height=48&name=Aera%20Technology%20logo-1.png "Aera Technology logo")](https://www.aeratechnology.com/)

[![Aera Technology logo](https://meet.aeratechnology.com/hs-fs/hubfs/Aera%20Technology%20logo.png?width=1000&height=424&name=Aera%20Technology%20logo.png)](https://www.aeratechnology.com/)

# Tokenomics for Agentic AI: Planning, Monitoring, and Controlling What Your Agents Spend.

Watch On-Demand

![Tokenomics for Agentic AI: Planning, Monitoring, and Controlling What Your Agents Spend.](https://meet.aeratechnology.com/hs-fs/hubfs/Landing%20page%20and%20blog%20recap%20cover%20(16).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.")

## 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.

**Speakers:**

![Aruna Goli](https://meet.aeratechnology.com/hs-fs/hubfs/1516237895527.jpeg?width=147&height=147&name=1516237895527.jpeg)

**Aruna Goli**

Senior Vice President, Head of Engineering

at Aera Technology

![Ram Krishnan](https://meet.aeratechnology.com/hs-fs/hubfs/ram-krishnan.jpg?width=147&height=150&name=ram-krishnan.jpg)

**Ram Krishnan**

SVP, Platform Product Marketing

at Aera Technology

 

---

Privacy Policy · Legal · © 2026. All rights reserved.

![](https://dc.ads.linkedin.com/collect/?pid=605076&fmt=gif)