A guided introduction to the Microsoft Agent Framework in Python, building up the core concepts one focused lesson at a time.
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.
The minimal loop: a Foundry chat client, an Agent with instructions, run non-streaming and streaming — and what actually comes back.
The minimal Microsoft Agent Framework loop in Python: a FoundryChatClient, an Agent whose instructions are its whole personality, run non-streaming and streaming.
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.
How a stateless agent remembers: sessions carry one conversation, context providers carry knowledge across all of them.
Microsoft Agent Framework agents are stateless. Sessions carry one conversation; context providers carry memory across all of them. Here is the mental model in real code.
Typed results from `response_format`, consuming a stream event by event, and sending an image alongside text.
Three dials on one run() call: typed results via response_format, consuming a stream event by event, and sending an image alongside text.
Wrapping an agent run with async seams that log, time, guard, and short-circuit — without touching the agent's logic.
Wrapping an Microsoft Agent Framework agent run in Python with async middleware seams — timing, logging, and a guardrail that short-circuits a tool call before it runs.
Turn agent runs into OpenTelemetry spans, block prompt injection with information-flow control, and swap model providers behind one Agent API.
Turn Microsoft Agent Framework agent runs into OpenTelemetry spans, block prompt injection with information-flow control, and swap model providers behind one Agent API.
The graph model underneath every multi-agent app: executors as nodes, edges as data flow, and typed events streaming out as it runs.
The Microsoft Agent Framework workflow model in Python: executors as nodes, edges as data flow, switch-case routing, and typed streaming events - learned model-free.
Agents as graph nodes: switch-case routing, fan-out/fan-in, and mixing plain functions with agent steps in one workflow.
Agents are just workflow executors: switch-case routing, fan-out/fan-in concurrency, and mixing plain function nodes with agent nodes in one graph.
Five prebuilt multi-agent shapes — Sequential, Concurrent, Group Chat, Handoff, Magentic — and when each beats hand-wiring a graph.
Sequential, Concurrent, Group Chat, Handoff, Magentic — the five prebuilt Microsoft Agent Framework orchestrations in Python and when each beats hand-wiring a graph.
Durable workflows in Python: checkpoint and resume, pause for a human with request_info, and package a workflow as an agent.
Durable Microsoft Agent Framework workflows in Python: checkpoint and resume every superstep, suspend on request_info for a human decision, and package a workflow as an agent.
Turning agents into a service you can run and expose, then a full DocQA app that ties the whole series together.
Host Microsoft Agent Framework agents with DevUI, A2A, MCP, and AG-UI, then build DocQA — a grounded, cited multi-agent app that ties the whole Python series together.
This series is part of a larger body of work by Pratik Dhanave, an Agentic AI Architect writing about production AI systems, distributed systems, and cloud-native engineering. Explore all course series, browse every post, or find topics via the tag index.