Control Flow

What this lesson demonstrates

Often the next executor depends on the data. That’s a switch-case edge group: from one source, a message is routed to the first Case whose predicate is true, else to the Default. This lesson routes even numbers to a “fast lane” executor and odds to a “review lane” — pure executors, so it runs with zero credentials. It’s the first branching primitive after the straight-line graph workflow.

The code

The branching lives entirely in add_switch_case_edge_group:

workflow = (
    WorkflowBuilder(start_executor=intake)
    .add_switch_case_edge_group(
        intake,
        [
            Case(condition=lambda x: x % 2 == 0, target=even),
            Default(target=odd),
        ],
    )
    .build()
)
result = await workflow.run(n)
return result.get_outputs()[0]

Each executor is a small class with a @handler; the even/odd handlers call ctx.yield_output(...) with a routed message.

What to notice

• Cases are evaluated in order. The message goes to the first Case whose condition returns true; if none match, Default catches it. Order matters when predicates overlap.
• Predicates are plain callables. lambda x: x % 2 == 0 — the condition is ordinary Python over the message, so routing logic stays in your code, not the model’s.
• Output types are declared on the context. WorkflowContext[Never, str] says a terminal executor yields a str and sends no further messages; WorkflowContext[int] says it forwards an int.

The gotcha

Every switch-case group needs a Default. Cases are not exhaustive on their own — a message that matches no Case and has no Default has nowhere to go. Always terminate the group with a Default(target=...) so the routing is total.

How it maps to MAF and Foundry

This is graph-workflow plumbing that sits below the agent layer — no FoundryChatClient, no credentials. But the same edge groups carry agent executors in later lessons: swap the even/odd handlers for agents and the switch-case decides which agent handles a message. Learning routing on pure functions first keeps the mechanics visible before models enter the graph.

Run it

uv run tutorial/03-workflows/01_control_flow.py

Runs offline — no Azure creds. Success: 4 and 10 route to the fast lane, 7 routes to the review lane.

Next: Parallelism