A model you can actually check
Monthly cost for one automated workflow is roughly: runs per month × (input tokens per run × input price + output tokens per run × output price). Divide by the number of employees whose work it supports to get a per-employee figure. The arithmetic is trivial; the discipline is in recording which model, which prices, which date, and whether the token counts came from real runs or from an estimate.
per-employee = monthly cost ÷ employees supported
Always record model name, price, price date, and whether tokens were measured or estimated.
Why per-employee is the wrong denominator
Headcount is a convenient denominator because finance already tracks it, but automation cost does not scale with people. It scales with events: tickets filed, deals reviewed, builds run, invoices processed. A team of five that automates a high-frequency process can spend more than a team of fifty that automates a rare one. Per-employee is fine for a budget line; per-run and per-outcome are what you need to make a decision.
The part of the bill that is pure repetition
Split each workflow's spend into two buckets: tokens spent making decisions that genuinely varied between runs, and tokens spent re-establishing a procedure that did not vary. The second bucket is the addressable one. In a mature, frequently-run workflow it is usually the larger of the two, and it is the only part that can be removed rather than merely discounted.
What to do with the number once you have it
Rank workflows by total monthly spend and by how settled their procedure is. A workflow that is expensive and still changing weekly should stay an agent, because you are paying for flexibility you are actually using. A workflow that is expensive and has not changed in a month is the candidate: compile it into code, keep the genuinely ambiguous steps as typed model calls, and measure the same cases before and after.
Honest caveats
Token prices change and vary by model and by cache state. Retries, failed runs, and human rework are real costs that a naive model omits. Some workflows are worth an uneconomical price because of what they prevent. And a compiled workflow does not reach zero cost: retained judgment nodes, external systems, and runtime still cost something. Publish the assumptions alongside the number so a reader can disagree with the inputs rather than the conclusion.
Frequently asked questions
How much do AI agents cost per employee per month?
There is no defensible industry number, because the cost depends on how often automation runs rather than on how many people are employed. Model it directly: runs per month × tokens per run × current model price, divided by the employees the workflow supports. Any figure quoted without those inputs is a guess wearing a decimal point.
Why is our AI spend growing faster than our headcount?
Because spend tracks repetitions, not people. As automation succeeds it gets applied to more events and given more context to work with, and both multiply the per-run cost. Adding no one to the team can still double the bill.
What is the fastest way to cut AI agent spend without losing capability?
Find the workflows with the highest run counts and the most settled procedures, and remove model calls from the steps that no longer involve a decision. This preserves the judgment you are actually paying for while eliminating the repetition you are not getting value from.
Compile a workflow
Inspect the open-source CLI or run a compilation in Rote Cloud.