AI Agent Human Review Queue Staffing Calculator
An approval-gated ERP agent only saves money if the human review queue behind it is smaller than the manual work it replaces. This calculator turns your auto-approve rate, review minutes and rework rate into reviewer headcount and net monthly saving.
Your numbers
Total items the agent processes: PO acknowledgements, invoice codings, ticket triage and similar.
Share of tasks above your confidence threshold that go through without a human.
Time to read the agent's proposal, check the evidence and approve.
Share of reviewed tasks where the reviewer corrects or redoes the work.
What the task costs today with no agent.
Tokens, GPU share or API fees per task.
Your results
Planning estimate. It assumes review time is flat per task; real review time drops with good evidence display and rises for hard cases.
Reviewer FTE needed vs auto-approved share
Sweeps Auto-approved share from 0 to 100, holding your other inputs fixed.
Review the queue design for your agent
Optional: send us your volumes and we will sanity check the threshold and review flow.
Optional. The tool and every export work without this. No spam.
How to use it
Enter monthly task volume and the share your confidence threshold lets through untouched. The remaining tasks land in a queue; each costs review time, and a fraction costs extra rework time. Compare the resulting labor plus run cost against the fully manual baseline. Then drag the auto-approved share down to see what happens if you tighten the threshold for a risky workflow such as vendor bank detail changes or price updates.
The formula
Reviewed tasks = volume x (1 - auto share). Human hours = reviewed x review minutes / 60 + reviewed x rework rate x rework minutes / 60. Reviewer FTE = human hours / productive hours per reviewer. Net saving = manual cost - (human hours x rate + volume x run cost). Note the model charges review time on flagged tasks only. If your policy samples auto-approved tasks for audit, add that sample as a separate line.
- Auto-approved share is a policy choice, not a model property.
- Tightening the threshold raises accuracy but moves work into the queue.
- Rework is usually the hidden cost.
Reading the result and common mistakes
If net saving is small or negative at your planned auto-approve share, the workflow is a poor automation candidate or the evidence shown to reviewers is too thin. A common mistake is using the model's average accuracy as the auto-approve rate: they are different numbers. Another is ignoring queue peaks at month end, when review volume can triple. Size for the peak week, not the monthly average, and keep write-back actions behind approval until the measured error rate supports loosening.
Frequently Asked Questions
What auto-approve rate is realistic?
It depends entirely on the workflow and your risk tolerance. Low-risk read-only classification can run high, while anything that writes to the ERP or moves money usually stays mostly gated at first. Measure on a pilot sample before setting a number.
Should reviewed tasks include audit samples?
Yes, if you sample auto-approved items for quality control. Add the sample share to the flagged volume, or run the calculator twice with the audit share added to the reviewed percentage.
Why can net saving be negative?
When review plus rework time per task approaches the manual time, the agent adds cost without removing work. This usually means low confidence calibration or evidence that is hard for reviewers to verify.
Does this include the cost to build the agent?
No. It covers monthly operating cost only. Compare the net monthly saving against build and licence cost in a separate payback calculation.
Netray builds approval-gated agents on top of ERP data with the query and evidence shown to the reviewer. If you want the review queue modeled against your real volumes, we can walk through it.
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