Building one AI agent is easy; engineering a platform for thousands is another story. Which department is burning the most tokens? Does every agent in production follow your safety guidelines? A reference architecture for a centrally governed AI platform on Azure — token rate limiting, semantic caching and security through API Management and Microsoft Foundry, agent identity in Entra ID, deployed as a hub-and-spoke landing zone in code.

Building a single AI agent is easy, but engineering a platform that can support thousands of them is a different story. Have you ever tried to work out which department is spending the most on tokens, or how to make sure every agent that reaches production follows your safety guidelines? If you are struggling with shadow AI, you are not alone.

An AI engineer and an Azure platform engineer team up to walk through a reference architecture for a centrally governed AI platform on Azure. It handles token rate limiting, semantic caching and security through API Management and Microsoft Foundry. You see how to manage agent identity with Entra ID, and how to deploy the whole setup as a hub-and-spoke landing zone using Infrastructure as Code.

On the practical side: implementing an AI gateway on API Management and setting up agent identity with Entra — everything deployed as code.

Come and see how to move from scattered AI projects to a platform your organisation can actually trust.