Multi-Agent Document Intelligence: RAG Meets Autonomous Workflows in Azure AI Foundry

​Most RAG implementations today are still single-shot - user asks a question, system fetches some chunks, model spits out an answer. That works for simple lookups, but falls apart when you need to do something more involved, like pulling a document, summarizing the key clauses, checking them against compliance rules, and then presenting a coherent answer to an underwriter. No single prompt chain handles that well.

This session is about breaking that problem into specialized agents each one responsible for a specific job. One agent handles retrieval. Another focuses on summarization. A third does compliance or rule checking. And a coordinator agent manages the handoff between them. Azure AI Foundry's Agent Service gives you the building blocks for this: tool definitions, function calling, and the ability to chain agents together without duct-taping everything with custom code.

I will walk through how this plays out in practice using a document intelligence use case from the insurance domain - ingesting claims documents, breaking them down into clause-level chunks, running hybrid search across them, and layering a conversational interface on top. The goal isn't to show a polished demo - it's to show the architecture decisions, the trade-offs, and the places where things get messy in real enterprise deployments.