Tokenomics in Depth: Architecting for AI Cost Control at Scale

Tokenomics in Depth: Architecting for AI Cost Control at Scale

As AI agents take on more of an organization's work, token consumption and the associated spend behind it may grow faster than anticipated. This workshop treats "tokenomics" as an engineering discipline framed around a question: what does each unit of business work actually cost, and how do you drive that number down without degrading quality? After a brief grounding in the fundamentals (model-class economics, asymmetric input/output pricing, and matching model tier to task), the session turns to the levers that matter at enterprise scale:

• Identifying and trimming context bloat, where retrieved documents and verbose tool outputs often dwarf the task itself

• Decomposing monolithic agents into per-task sub-agents, each with a model chosen for that sub-task's difficulty

• Prompt caching mechanics, and when to leverage cached context versus delegating to faster, cheaper models

• Small Language Model economics

• Instrumenting agents with observability platforms to measure token consumption per call

• Lightweight evaluation practices that make model downsizing safe rather than a gamble

Don Shin

Founder & CEO of CrossComm

An award-winning consultancy helping organizations harness Generative AI and AI Agents through strategy, development, and training.

➡️ Bring your laptop ︱ ➡️ Workshop completion = 3 CPE credits ︱ ➡️ Sponsorships available