Microsoft Agent Framework

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Microsoft has had several agent libraries. Semantic Kernel and AutoGen were solving the same problem from two directions. Microsoft Agent Framework, usually shortened to MAF, is where those lines meet. AutoGen is now in maintenance mode, so do not start a new project there.

What actually changes

The difference you will feel immediately is the tool description.

In Module 3 you hand-wrote the JSON schema yourself. In MAF you write an ordinary function and hand over the function:

def check_licence_seats(product: str) -> str:
    "How many seats are left for a product."
    ...

agent = client.create_agent(
    name="helpdesk",
    instructions="...",
    tools=[check_licence_seats],
)

MAF reads the function's name, its parameters, its type hints and its description, and writes that JSON for you.

That is a real convenience, and it is worth knowing it is only a convenience. You wrote that JSON by hand once, so you can picture exactly what the framework generated and debug it when the model calls a tool strangely.

Agent or workflow

Start with a single agent. Reach for a workflow, which is MAF's word for several agents co-ordinating, only when the roles genuinely differ. That is Module 16's argument, and most designs that reach for a workflow wanted one agent with better tools.

What to write

from agent_framework.azure import AzureOpenAIChatClient
from azure.identity import DefaultAzureCredential

1. Build the client with an endpoint, a deployment name and a credential
2. Write check_licence_seats(product) as an ordinary typed function
3. create_agent(...) with that function in tools
4. agent.run(QUESTION) and print the result

The point

The framework's main gift is writing the tool description you already know how to write yourself.

Break it on purpose

Remove the type hint from product. The description still builds and the model has less to go on. Then take the function out of tools and watch the agent lose the ability to answer at all.