Deploying Microsoft Foundry securely is only the starting point. This post explores how to turn that foundation into an internal AI platform, using practical golden paths for RAG, agents and model APIs. It covers self-service, governance, evaluation and ownership—and how to start small, reduce delivery friction and measure useful outcomes.

Standing up Microsoft Foundry as a public-facing sandbox takes an afternoon; deploying it privately with enterprise-grade security and governance is a proper platform design project. Drawing on a recently completed enterprise design, this post walks through the key decisions — governance boundaries, network isolation, RBAC, models and regions, NFRs and DR — the gotchas that get missed, and a framework for phasing decisions across HLD, LLD, Build and Run.

Azure AI-102 AI Engineer certification study guide