Azure Multi-Agent Intelligence & RAG Platform

An AI-infrastructure pattern that connects specialist agents to governed data, semantic retrieval, shared model services, and deployable application components.

Anonymized architecture case study

Architecture diagram for an Azure multi-agent intelligence and RAG platform — Architecture view with client and environment identifiers removed.

Case Study Overview

A research and intelligence platform needed to combine external news, social, and financial data with structured analysis, semantic retrieval, and specialist agent workflows without creating disconnected data or model silos.

Outcome

A reusable architecture for multi-agent applications that keeps data, retrieval, models, and specialist workflows understandable, governable, and independently evolvable.

Architecture Approach

1Separate ingestion, structured data, unstructured content, search indexes, graph relationships, and agent responsibilities.
2Use Azure AI Search for purpose-specific indexes and Azure OpenAI for chat and embedding workloads.
3Containerize the application path and keep authentication, DNS, data, retrieval, and model interactions explicit.
4Give each agent a bounded responsibility while sharing governed retrieval and model services.

Role & Scope

AI platform architecture, retrieval design, and service integration

Technology Stack

Azure OpenAI

Azure AI Search

RAG

Multi-Agent Systems

Embeddings

Neo4j

Docker

Azure VM

Azure DNS

SQL

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