Every UBL invoice a freight company receives carries one field that decides everything: the transport reference, the tracking number that ties the bill to the shipment it pays for. Find it and the invoice reconciles itself. Miss it and a person goes hunting through the document by hand. At the volume a logistics operator runs, that hunt is a full-time job for a lot of people, and it's the kind of work nobody signed up to do.
The usual fix is to prompt a large language model once and hope. Show it the invoice, ask for the reference, take what comes back. That holds up until the format drifts, the field moves, or the document runs long, and in logistics all three happen constantly. A single prompt has no memory of the last thousand invoices it read. So it repeats the same mistake on the thousand-and-first.
MATRIX is our answer. The name unpacks to Memory-Augmented agent Training through Reasoning and Iterative eXploration. Here's the short version a whiteboard would give you: teach the agent from the invoices it has already seen, then let it read the next batch with that experience in hand.