From a model to a shipped AI product

Understand it one step at a time

The short runs these in order in about 25 seconds. Here they are written out — pick any step to jump the short straight to it.

1. 1

   The demo is easy. Shipping is not

   Models, your data, evaluation, safety and monitoring — five separate tools, glued together by hand.

   prototype stuck in dev

2. 2

   Azure AI Foundry is one platform

   The catalog, the grounding, the agents, the evaluations and the guardrails live in a single project.

   foundry project ready

3. 3

   Pick a model, not a lock-in

   Frontier and open models sit behind one endpoint shape, so changing model is a config change, not a rewrite.

   browsing the model catalog

4. 4

   Ground it in your own data

   Azure AI Search indexes your content and retrieves the right passages, so the answer ships with citations.

   grounding on your content

5. 5

   Agents that actually do the work

   Foundry Agent Service gives the model instructions, tools and a thread — so it can act on your systems, not just chat.

   agent calling a tool

6. 6

   Evaluate before your users do

   Score groundedness, relevance and risk on every change, with content filters and red teaming ahead of release.

   running evaluations

7. 7

   Enterprise controls come built in

   It deploys inside your own identity, network and compliance boundary, with tracing on every request.

   deploying to production

8. 8

   This is what teams ship with it

   Support copilots, document processing, retail assistants, engineering agents — one platform underneath all of them.

   in production