Question

Your team is building an Azure AI Foundry agent that must automatically process supplier invoices that arrive as scanned PDF and image files. The agent needs to extract structured fields (invoice number, line items, totals) from these documents, then reason over the extracted data to flag anomalies before routing them for payment. You need to choose the most appropriate Foundry capability for the document extraction step while keeping the reasoning inside the agent. Which approach should you adopt?

Accepted Answer

Integrate Azure AI Document Intelligence (prebuilt invoice model) as a tool the agent calls to extract structured fields, then pass the results to the LLM for anomaly reasoning

Answer

Send the raw PDF bytes directly to a text-only LLM deployment and prompt it to return the invoice fields as JSON

Answer

Use Azure AI Search with a vector index of the scanned images to retrieve invoice fields for the agent

Answer

Deploy a small language model at the edge to perform OCR and field extraction locally before the agent runs