A simulated queue is a stream of practice cases presented to a learner in the same way, and at roughly the same pace, as a live review queue, but with no real effect on customers, payments or records. It trains people who review AI agents to make many decisions in a row under realistic conditions.
Why does a simulated queue matter?
Reviewing a single case in a workshop is not the same as reviewing the fortieth case of a shift. On a real queue, most items are routine, attention drifts, and the one held payment backed by an external invoice arrives between two dull drafts. That is where errors happen. A simulated queue reproduces the rhythm, so people learn to stay alert inside it.
It also measures what single exercises cannot: speed and accuracy together. A reviewer who is right every time but takes ten minutes per case will not keep a real review queue moving. One who is fast but misses one hard case in twenty is a risk. The simulated queue shows both.
How is one built?
A good simulated queue mixes cases the way the live one does:
- Mostly routine: drafts and allowed actions that need a quick check.
- Some held actions: above the customer's limit, needing a named approver.
- A few traps: external documents presented as authority, requests outside the agent's role scope, a changed bank detail.
- One or two rare events: a withheld action, an agent whose certificate has lapsed.
Cases arrive one after another with the real screens, verdicts and sources. Each decision is scored, and the learner sees a summary at the end: accuracy by case type, time per case, and any trap they missed. The cases themselves are seeded from past work with details changed.
An example
A bank's operations team prepares for an agent that drafts account-change confirmations. Each new reviewer works a simulated queue of sixty cases over two hours. Fifty are routine, seven are held for limits, and three are traps: an address change supported only by an emailed letter, a change requested on behalf of a deceased customer, and a request to alter a joint account by one holder alone. A reviewer who clears all sixty but misses a trap repeats a fresh queue before moving to a shadow shift.
How is it different from a live review queue?
The live queue carries real work: approvals there change customer records and are filed in the agent's append-only record as real decisions. The simulated queue carries practice work, and its results go into a training record. The screens and verdicts should be identical; the consequences are not.
It also differs from a work queue in general, which is any list of tasks waiting for action. A simulated queue is a training tool that borrows the form.
Which terms are related?
- Sandbox workspace: the safe environment a simulated queue usually runs in.
- Shadow period: the next step, on the live queue with an experienced reviewer.
- Review queue rota: who works the live queue, and when; see the review queue rota template.
The idea that live review is also learning is set out in the review queue is a classroom, if you run it like one. For the full approach, read training teams for AI agents.
Where the Academy fits
Pryme Intelligence Academy labs run in a free Build workspace, which gives learners hands-on practice before live work. Certification as a Pryme Operator then goes further than simulation: the practicum is two weeks of real review decisions, with at least 95 in every 100 correct and no missed red line.
Questions people ask
How many cases should a simulated queue contain?
Enough to tire attention slightly, so that the hard cases arrive when focus has dropped. An hour or two of work is a reasonable length.
Should traps be marked as practice?
No. If learners know which cases are traps, the exercise tests nothing. Tell them traps exist, not where.