Develop an AI agent with the Microsoft Agent Framework | AI-103 | Episode 13

Microsoft Agent Framework provides a code-first abstraction for building agents across models, providers, tools, conversations, and workflows. It brings together concepts from Semantic Kernel and AutoGen behind a more consistent agent programming model.

Core model to remember: Provider → Agent → Tools → Run. A provider connects the agent to an underlying model; the agent combines instructions + tools; and run() executes the interaction. Built-in capabilities include file search, web search, conversation management, and workflow orchestration.

from agent_framework import Agent, tool

@tool
def submit_claim(subject: str, body: str):
    print(subject, body)

agent = Agent(
    client=model_client,
    instructions="Create expense claims using the available tool.",
    tools=[submit_claim]
)

result = await agent.run("Submit my expenses")

The important part is what is missing: no manual function-call dispatch loop. The framework can recognize the model's tool request, invoke the registered function, return its result, and continue execution automatically.

Key concepts

• Agent abstraction → allows different model/chat providers behind a common interface.

• @tool → exposes Python functions as agent tools; metadata can be inferred instead of manually constructing tool schemas.

• Tool approval → controls whether execution requires approval.

• Agent thread → manages conversation state and stored messages.

• Authentication → prefer token-based authentication; local development may use Azure CLI credentials, while hosted applications typically require an appropriate workload identity such as managed identity.

Remember: the framework handles much of the plumbing between LLM → agent → tool → result, letting application code focus on agent behavior rather than dispatch infrastructure.