Why "search" isn't enough anymore A plain RAG pipeline — embed a query, hit a vector...
Microsoft Foundry's function calling routes queries between semantic search (Azure AI Search for unstructured documents) and structured lookups (Cosmos DB for orders/metadata), returning always-cited answers. Grounded agents solve core RAG gaps—multi-turn memory, routing decisions, verifiable citations—enabling production support and internal tools without hallucinations.
Why "search" isn't enough anymore
A plain RAG pipeline — embed a query, hit a vector index, stuff the top-k chunks into a prompt — works fine for a single-turn FAQ bot. It falls apart the moment you need:
Multi-turn memory ("what about last quarter?" needs to know what "last quarter" refers to)
A decision about whether to search at all (some questions are structured lookups, not document retrieval)
Citations the caller can verify
Most "RAG tutorials" stop at a single embedding query against a single index. That works for a demo...
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