Why is approval authority a useful workflow for training or evaluating models?

**Why is this workflow useful for training or evaluating models?** It forces a model to combine documents, records and judgement. The answer is rarely in one place: the policy gives the limit, the delegation register says who holds the role today, and the request carries the amount and risk rating.

**Isn't this just a rules engine problem?** Partly, and that is the point. Rules engines handle clean cases. Real requests arrive with a missing field, an expired delegation, a policy revised mid-quarter or a note saying the approver is acting. Models are asked to cope with these, and a task pack can test where they stop coping.

**What does a failure look like?** Common failures include approving above limit, accepting a delegation that has ended, ignoring a conflict of interest between requester and approver, and escalating cases that were within limit. Each is a different behaviour and can be scored separately.

**Does it only apply to finance?** No. Procurement in retail operations, claims settlement in insurance, work orders in utilities, rebate approval in logistics and write-offs in healthcare administration all depend on the same structure of limits and roles.

**Is the data real?** No. MirrorFirm output is generated and informed by direct observation of how businesses operate. It is not real business records, and buyers should inspect any sample for grounding and disclosure risk.