Fix more workflow samples

python/samples/03-workflows/agents/azure_chat_agents_tool_calls_with_feedback.py

@@ -184,7 +184,9 @@ def create_writer_agent() -> Agent:
             "produce a 3-sentence draft."
         ),
         tools=[fetch_product_brief, get_brand_voice_profile],
-        tool_choice="required",
+        default_options={
+            "tool_choice": "required",
+        },
     )
 
 

python/samples/03-workflows/agents/group_chat_workflow_as_agent.py

@@ -72,7 +72,7 @@ async def main() -> None:
     print(f"Input: {task}\n")
 
     try:
-        workflow_agent = Agent(client=workflow, name="GroupChatWorkflowAgent")
+        workflow_agent = workflow.as_agent()
         agent_result = await workflow_agent.run(task)
 
         if agent_result.messages:

python/samples/03-workflows/agents/handoff_workflow_as_agent.py

@@ -174,21 +174,20 @@ async def main() -> None:
     #   Without this, the default behavior continues requesting user input until max_turns
     #   is reached. Here we use a custom condition that checks if the conversation has ended
     #   naturally (when one of the agents says something like "you're welcome").
-    agent = Agent(
-        client=(
-            HandoffBuilder(
-                name="customer_support_handoff",
-                participants=[triage, refund, order, support],
-                # Custom termination: Check if one of the agents has provided a closing message.
-                # This looks for the last message containing "welcome", which indicates the
-                # conversation has concluded naturally.
-                termination_condition=lambda conversation: (
-                    len(conversation) > 0 and "welcome" in conversation[-1].text.lower()
-                ),
-            )
-            .with_start_agent(triage)
-            .build()
-        ),
+    agent = (
+        HandoffBuilder(
+            name="customer_support_handoff",
+            participants=[triage, refund, order, support],
+            # Custom termination: Check if one of the agents has provided a closing message.
+            # This looks for the last message containing "welcome", which indicates the
+            # conversation has concluded naturally.
+            termination_condition=lambda conversation: (
+                len(conversation) > 0 and "welcome" in conversation[-1].text.lower()
+            ),
+        )
+        .with_start_agent(triage)
+        .build()
+        .as_agent()
     )
 
     # Scripted user responses for reproducible demo
@@ -226,7 +225,7 @@ async def main() -> None:
             responses = {req_id: HandoffAgentUserRequest.create_response(user_response) for req_id in pending_requests}
 
         function_results = [
-            Content.from_function_result(call_id=req_id, result=response) for req_id, response in responses.items()
+            Content("function_result", call_id=req_id, result=response) for req_id, response in responses.items()
         ]
         response = await agent.run(Message("tool", function_results))
         pending_requests = handle_response_and_requests(response)

python/samples/03-workflows/agents/workflow_as_agent_human_in_the_loop.py

@@ -164,7 +164,8 @@ async def main() -> None:
         human_response = ReviewResponse(request_id=request_id, feedback="", approved=True)
 
         # Create the function call result object to send back to the agent.
-        human_review_function_result = Content.from_function_result(
+        human_review_function_result = Content(
+            "function_result",
             call_id=human_review_function_call.call_id,  # type: ignore
             result=human_response,
         )

python/samples/03-workflows/declarative/deep_research/main.py

@@ -29,10 +29,11 @@ from agent_framework import Agent
 from agent_framework.declarative import WorkflowFactory
 from agent_framework.foundry import FoundryChatClient
 from azure.identity import AzureCliCredential
+from dotenv import load_dotenv
 from pydantic import BaseModel, Field
 
-# Copyright (c) Microsoft. All rights reserved.
-
+# Load environment variables from .env file
+load_dotenv()
 
 # Agent Instructions
 RESEARCH_INSTRUCTIONS = """In order to help begin addressing the user request, please answer the following pre-survey to the best of your ability.

python/samples/03-workflows/declarative/student_teacher/main.py

@@ -27,8 +27,10 @@ from agent_framework import Agent
 from agent_framework.declarative import WorkflowFactory
 from agent_framework.foundry import FoundryChatClient
 from azure.identity import AzureCliCredential
+from dotenv.main import load_dotenv
 
-# Copyright (c) Microsoft. All rights reserved.
+# Load environment variables from .env file
+load_dotenv()
 
 
 STUDENT_INSTRUCTIONS = """You are a curious math student working on understanding mathematical concepts.