What is Token Usage?
The main driver of AI agent cost: what it is, why agents amplify it, and how to track and control it.
Token usage is the number of tokens an AI agent consumes to handle a request: the primary driver of what that agent costs to run. Because agents are multi-step and call models repeatedly, their token usage (and cost) can be many times a single chat completion. Tracking it per agent and per session is what makes cost attribution, budgets and anomaly detection possible.
Why does token usage matter for AI agents?
A single chat completion uses tokens once. An agent reasons, calls tools, re-reads context and retries across many steps, so one task can spend tokens dozens of times, and a small per-step inefficiency multiplies across a fleet. Token usage is therefore the metric that decides whether an agent is economically viable at scale, and a sudden spike is often the first sign something has gone wrong (a loop, a runaway retry, a bloated context).
How do you track and control agent token usage and cost?
Track it as telemetry: capture tokens consumed per session, per agent and per task, so cost can be attributed rather than guessed. Then control it: set per-agent and per-workflow budgets, cache repeated calls, trim context, and alert or cut off when usage crosses a threshold. The same per-session data powers anomaly detection: a session using ten times the expected tokens is flagged before the bill arrives.
Token usage vs agent cost attribution: how do they relate?
Token usage is the raw signal; cost attribution is what you do with it. Usage is tokens consumed; attribution maps that (and other costs) back to a specific agent, team, customer or workflow so you know where the spend is going and who owns it. You need the per-session usage data first to attribute cost meaningfully, which is why token usage sits in the observability layer, feeding the cost-attribution view.
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