When the Bill Arrives: Cost Engineering for Production AI Agents in Enterprise Analytics

Most practitioners instrumenting AI agents in production analytics workflows start by tracking token consumption — and discover too late that token spend tells you what you spent, not whether it was worth it. This talk walks through the operational reframe underway in financial services and adjacent regulated industries: from cost-per-token to cost-per-resolved-task as the primary economic unit, with call-level attribution as the foundation that makes everything else possible.

I’ll cover four cost engineering patterns that hold up in production analytics environments: (1) instrumentation at the call level — what to log, where it lives, who owns it; (2) model tiering and routing — when a frontier model earns its premium and when a smaller fine-tuned model wins; (3) the output-token asymmetry that makes structured outputs and chain-of-thought hygiene more economically consequential than they appear; and (4) cost containment as an engineering practice with runtime guardrails, not a finance review after the fact.

I’ll also be honest about what doesn’t generalize: caching strategies that worked for one workload and broke another, routing logic that added more latency than it saved, and the political dimension of asking analysts to justify their agent spend. Attendees will leave with a maturity model for agent cost engineering, a concrete instrumentation checklist they can apply next week, and a vocabulary for the cost conversation that bridges engineering and finance — because in regulated industries, that’s the conversation that decides whether agents scale or stall.