BEHIND THE FIELD TEST
HOW EXCEPTION ROOM WAS DESIGNED
A compact simulation of the work between an AI recommendation and an accountable operational decision.
What this game tests
Exception Room tests whether a player can review evidence, respect decision authority, protect service deadlines, and use limited capacity well. Picking the right action is not enough. The game records whether the required evidence was actually reviewed.
Why evidence and authority are separate
Some recommendations are safe to approve because two current sources agree. Some contain a bounded error that the operator can correct. Others have complete evidence but still require a named policy owner. The cases teach that evidence can support a decision without granting the authority to make it.
How the queue creates pressure
Twelve synthetic cases arrive across three shifts. Approve, Correct, and Escalate spend different amounts of review capacity. Cases also have deadlines, consequence levels, and carryover rules. The pressure comes from prioritization, not a countdown clock.
How the debrief stays accountable
Safety is weighted by consequence and required-evidence coverage. Service reflects safe, timely outcomes. Capacity rewards proportionate review effort. A decision made with missing evidence remains visible in the final trace and cannot earn the strongest operator profile.
How it was built
The simulation is deterministic and runs entirely in the browser. Every case, deadline, action cost, outcome, and learning destination is authored and testable. Progress stays local. The scenarios are synthetic and contain no customer or production data.