Free cost planning tool
The agent costs pennies. Supervising it costs the real money.
Two columns of honest math: what a successful agent task really costs once you add human review, rework, and infrastructure, and how much of the promised saving supervision eats. Every default on this page is a labeled assumption, not a benchmark.
The calculator
Run your numbers
How many agent runs you put through review each week. Assumption: 40.
Reading, checking, and signing off on one output. Assumption: 10 min.
Share of tasks that fail review and get retried. Assumption: 20%.
What the model call costs you per run, at your rates. Assumption: C$0.40.
The manual baseline the pitch is measured against. Assumption: 30 min.
Some teams report an agentic run burning 5–30x the tokens of a single direct model call. That range is not a fact about your stack; it only shows how sensitive the math is to token-heavy runs.
Show your work
How the math works
- Reviewer cost
- C$70/hr loadedAssumption
- Tools + infrastructure
- C$0.15 per taskAssumption
- Manual time per task
- 30 min, adjustable aboveAssumption
- Rework
- One retry + one more review per reworked taskAssumption
- Token multiplier 5–30x
- Sensitivity test only, never a fact about your stackAssumption
- Retry success
- Every reworked task succeeds on retryAssumption
- Cost per successful task = (model spend × token multiplier) + tools + infrastructure + human oversight + rework × (one full retry). The multiplier is 1 unless the sensitivity toggle is switched on.
- Human oversight per task = review minutes ÷ 60 × reviewer cost, counted once for the first pass and again for every retry.
- The supposed gain = manual cost per task − machine cost per task. The pitch prices the machine only; supervision is the part it leaves out.
- Share eaten by supervision = (human oversight + rework) ÷ supposed gain per task.
Common questions
Before you decide
- Are these numbers a quote?
- No. Every default on this page is a clearly labeled assumption for illustration, not a benchmark and not a price. Actual costs vary with the task, the reviewer, the model, and the vendor.
- Why is there a 5–30x token toggle?
- Some teams report that an agentic run burns many times the tokens of a single direct model call. That range is not a fact about your stack. The toggle exists only as a sensitivity test, so you can see how much the math moves if your runs turn out token-heavy.
- What counts as supervision here?
- The human minutes spent reviewing each output, the loaded cost of the person doing it, and the retries those reviews trigger. Incident response, audit trails, and liability sit outside this page's math.
- When does the agent still win?
- When review minutes stay low, rework stays rare, and the manual alternative is expensive or slow. Run your own numbers above; the threshold usually shows up in seconds.
Talk it through
If the math makes the business case wobble, bring your agent rollout to a focused conversation. No pitch, no obligation.
Discuss your AI governance needsDisclaimer: all figures are estimates for illustration only, not a quote, offer, or commitment. Actual costs vary by task, reviewer, model, and vendor. Confirm current legal requirements and obtain qualified advice for your facts and jurisdiction. This tool is maintained by Mohammad Movahedi.
If oversight is the real cost, buy a service that includes it, or do not buy the agent.