Articles about Azure AI Foundry — exploring patterns, best practices, and real-world implementations in production systems.
10 posts tagged with azure ai foundry. ← All posts
Wrap a typed Go function as a tool, let the model call it, and compose whole agents as tools.
Wrap a typed Go function as a tool with functool.MustNew, let the model call it, and compose whole agents as tools with agenttool.New.
The minimal loop in Go: a Foundry provider, an Agent with instructions, run collected and streamed — and what RunText hands back.
The minimal MAF loop in Go: a foundryprovider agent, DefaultAzureCredential, and one RunText you either Collect or range over as a Go 1.23 iterator.
Why I learned the whole framework in Go by writing one runnable lesson per concept, against Azure AI Foundry, instead of reading the docs top to bottom.
I learned the whole Microsoft Agent Framework in Go by building one runnable lesson per concept against Azure AI Foundry. Here is the 12-track map.
Turn a plain Python function into something the model can call, and watch the tool-call loop close itself.
Turn a plain Python function into a tool the model can call. The @tool decorator, the tool-call loop, multiple tools, and what the model actually sees.
The minimal loop: a Foundry chat client, an Agent with instructions, run non-streaming and streaming — and what actually comes back.
The minimal MAF loop in Python: a FoundryChatClient, an Agent whose instructions are its whole personality, run non-streaming and streaming.
Why I learned the whole framework by writing one runnable lesson per concept, against Azure AI Foundry, instead of reading the docs top to bottom.
I learned the whole Microsoft Agent Framework in Python by building one runnable lesson per concept against Azure AI Foundry. Here is the 12-track map.
How the Foundry provider snaps a project endpoint, a credential, and a model deployment name into a runnable agent.
The Foundry provider is three inputs: a project endpoint, a credential, and a model deployment name. See how foundryprovider.NewAgent snaps them into an agent.
Reach a Foundry-hosted model through the OpenAI-compatible Responses API by configuring a plain openai.Client with three request options.
Reach a Foundry model through the OpenAI-compatible API by configuring an openai.Client with base URL, an Azure token credential, and the ai.azure.com scope.
Point the same agent at an Azure AI Foundry project endpoint using the project Responses API mode.
Run an agent against an Azure AI Foundry project: foundryprovider.NewAgent plus ModelDeployment selects project Responses API mode from the project endpoint.
The two ways to reach a model in an Azure AI Foundry project.
Foundry offers two doors to a deployed model: FoundryChatClient where your app owns the loop, and FoundryAgent where the portal definition wins. This runs the direct path end to end.
All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.