337 posts · Page 8 of 29. ← Blog
How to collapse a whole multi-agent workflow into a single agent so callers use it exactly like any leaf agent.
A concurrent French+English workflow is hosted as one agent via agentworkflow.NewAgent, with IncludeOutputsInResponse surfacing the merged output.
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.
An `LlmAgent`, a `Runner`, a `Session`, and a CLI that runs it all — the four pieces the other 25 concepts sit on top of.
The smallest ADK agent and the machinery around it: an LlmAgent, the Runner, a Session, and the adk CLI (adk web / adk run) that runs your agent with a dev UI or REPL — no server code.
How a multi-agent group chat pauses to ask a human for approval before one gated tool is allowed to run.
A QA and DevOps agent collaborate in a group chat where DeployToProduction is gated by tool.ApprovalRequiredFunc, pausing the workflow for human approval.
How a stateless agent remembers: sessions carry one conversation, context providers carry knowledge across all of them.
MAF agents are stateless. Sessions carry one conversation; context providers carry memory across all of them. Here is the mental model in real code.
How two Foundry agents cooperate inside a cyclic graph workflow where one of them owns the loop control.
Two Foundry agents cooperate in a cyclic workflow: a SloganWriter drafts, a FeedbackProvider critiques and owns loop control via YieldOutput vs SendMessage.
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.
This lesson teaches how to build a cyclic workflow where a Writer and Critic loop until approval, using `AddSwitch` to route on structured output.
A cyclic Writer-Critic-Summary workflow: the Critic emits a structured CriticDecision, AddSwitch routes on Approved, and Context state caps the revision loop at maxIterations.
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.
This lesson teaches how deterministic function executors and agent-backed executors compose in one graph with the same `AddEdge` wiring.
One workflow that mixes deterministic executors with two Foundry agent nodes for jailbreak detection and response, joined by the same AddEdge wiring and TurnToken triggering.
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.
This lesson teaches how to embed a whole built workflow as a single executor inside a larger one — workflows compose.
Embed an entire built workflow as one executor with inproc.BindSubworkflowAsExecutor, composing a Prefix to SubWorkflow to PostProcess parent graph that runs fully offline.