How should security teams budget for AI agents that have both token and runtime costs?

How to budget AI agents as separate token, runtime, and action costs

Budgeting works best when you stop treating an agent like a single AI line item. Token spend is variable consumption, runtime is the cost of keeping a session alive, and tool usage is a third driver because the agent may keep invoking systems long after the model call itself is finished. The practical outcome is a forecast that finance can model and identity teams can govern.

That split matters because each cost behaves differently. Token usage scales with prompts and outputs, runtime scales with concurrency and session length, and tool activity scales with how often the agent is allowed to act, retry, or chain steps. If you compress those into one estimate, you lose the ability to tell whether overrun came from model usage, session sprawl, or excessive automation.

For budgeting, the cleanest unit is often a named agent session with a defined owner, purpose, and expiry. That gives teams a way to assign spend back to a function or product group, then compare actual consumption against expected usage patterns. It also prevents open-ended sessions from becoming a hidden pool of standing cost, especially when agents can resume work across long time windows.

What makes agent runtime cost harder to forecast than token spend?

Token spend is usually easier to estimate because it is tied to observable usage such as prompt length, response length, and call frequency. Runtime is harder because the session can stay open while the agent waits on approvals, external APIs, queue delays, retries, or human intervention. A low-token agent can still become expensive if it holds infrastructure, memory, or orchestration resources for hours.

This is where teams often underestimate the real unit economics. The model call may be cheap, but the surrounding workflow can introduce storage, network, queueing, and platform overhead. A budget that only tracks model inference cost will systematically miss the overhead of keeping autonomous work alive, especially when the agent is designed to coordinate across multiple tools or systems.

To avoid that blind spot, treat runtime as a governance variable, not just an engineering one. Shorter session TTLs, explicit renewal logic, and clear stop conditions reduce waste and also make budgets easier to forecast because the session cannot drift indefinitely. AI Agent Authorisation Guide is useful here because task-scoped access and per-action decisions naturally support bounded sessions and clearer cost ownership.

How do owner, expiry, and tool usage turn spend into something governable?

A named owner turns an agent from an abstract platform feature into a managed service with accountability. Without ownership, it is hard to decide who approves budget overruns, who retires stale sessions, or who validates whether the agent still needs the access and runtime it holds. Expiry conditions do the same work on the access side that budgets do on the finance side: they force a review point before cost and authority accumulate silently.

Tool usage deserves its own budget assumption because it is often the multiplier that changes the economics. An agent that only drafts text is a very different cost profile from one that calls ticketing systems, code repositories, databases, or approval workflows. The more tools it can use, the more you should expect retries, policy checks, and exception handling to affect both runtime and spend.

That is also why budgeting and authorization should be aligned. If an agent can spend money, change systems, or trigger downstream workflows, the budget should reflect not just likely usage but also the blast radius of those actions. Zero Trust for AI Agents is a good fit for that thinking because it ties continuous verification and no standing privilege to a model of bounded, reviewable action.

What should teams monitor so budgets stay accurate over time?

Teams should monitor token volume, session duration, tool call count, and exception rate as separate signals. If token use rises while runtime stays flat, the problem is probably prompt or output inflation. If runtime rises faster than token use, the likely issue is waiting, retry loops, or overlong sessions. If tool calls rise disproportionately, the agent may be doing too much work per task or repeatedly hitting approvals and fallback paths.

These signals are also useful for chargeback and forecasting. They help separate normal growth from inefficient design, and they make it easier to decide whether to optimise prompts, shorten sessions, narrow tool permissions, or retire a workflow entirely. In practice, the best budgets are not just ceilings, they are feedback loops that show whether the agent is still operating within the envelope it was approved for.

Practitioner Guidance: Focus first on the usage pattern that is hardest to reverse, usually long-lived runtime or excessive tool access, because that is where cost and control drift tend to compound. A budget that only watches model spend will miss the more dangerous failure mode, an agent that remains active, useful, and expensive long after its original task should have ended.

Practitioner takeaway: Budget AI agents by separating inference, runtime, and action costs, then bind all three to an owner and expiry so finance, platform, and identity decisions stay aligned.

Related resources from NHI Mgmt Group

• Guide: Red Teaming AI Agents for Identity Abuse
• How should security teams govern AI usage costs when LLM calls, agents, and APIs all consume budget in different ways?
• Why is single-provider AI agent governance not enough for enterprise security?
• How should security teams handle risks from AI browser extensions?
• How should organizations approach the governance of AI agents?