What does an approval workflow with authority limits look like, and why does it matter for AI?

In a real business, many approvals are not a single yes or no. Whether a request proceeds depends on who holds what authority: a team leader may approve a refund up to a set amount, a manager may approve a larger one, and anything flagged high risk goes to a named role regardless of amount. Authority can also be delegated for a period, for example when an approver is on leave, and that delegation has its own limits.

For AI training and evaluation this matters because the correct answer is rarely in the request itself. A model has to read a policy, identify the requester's and approver's roles, compare the amount and risk rating against the limits, and decide whether to approve, escalate or refuse. Systems that skip this step either approve things they should not or escalate everything and become useless.

For a licensed reseller, the workflow is useful because it is simple to describe to downstream customers and simple to test. A release can carry authority matrices, requests, decisions and exceptions as linked records, so your customers can check behaviour against explicit rules.

MirrorFirm output is generated, informed by direct observation of how real businesses operate, and it is never real business records. Your customers should still inspect grounding and disclosure risk before relying on any release.