Sohrab Hosseini (co-founder of Orq.ai) argues that traditional FinOps — designed for deterministic workloads with infrastructure-driven costs — is structurally insufficient for AI agents, whose spend depends on runtime behavior. A single user request can trigger variable sequences (retrieval, tool calls, model routing, retries, escalations), such that "two requests that appear identical to a user can produce very different token usage" with no visible change in functionality.
The diagnosis is backed by statistics: 80% of enterprises use GenAI in 2026, but less than 30% have monitoring that links cost to value; only 27% allocate cloud costs in real time, less than 25% have standardized AI governance, and 74% struggle to industrialize their pilots.
The proposed response is a conceptual shift toward "cost per outcome": measuring cost per ticket resolved, lead qualified, task completed, or hour saved — not tokens or infrastructure usage. The relevant question is no longer "how many resources" but "whether the consumption produced value": a token-efficient agent that fails costs more than a token-hungry agent that succeeds at a complex task.
To achieve this, Agent FinOps integrates three layers of signals: cost signals (model usage, tokens, API spend, budgets), operational signals (traces, retries, routing decisions, evaluation results), and business signals (resolution rate, completion, conversion, time saved).
This integration unfolds across a four-phase lifecycle. Experiment: bounded experimentation with budgets, cost-per-evaluation, and unit economics established before production. Deploy: guarded releases with routing policies, token limits, and timeouts ensuring economically predictable behavior. Operate: visibility into retries, escalations, and consumption to proactively adjust routing and constraints. Improve: evaluations guide prompt refinement, workflow redesign, model selection, and the retirement of underperforming automations.
Operational levers — guardrails, intelligent model routing, workflow budgeting, context control, and behavioral observability — converge into a centralized layer, the Control Tower (unified agent inventory, cost rollups, governance). Marker phrases: "A single agent is a feature. A collection of agents becomes an operational environment" and "Enterprises aren't struggling because they can't build agents. They're struggling because they can't coordinate them." The final principle: agentic FinOps does not scale by tracking spend more aggressively, but "by shaping agent behavior at every stage."