Python: Fix samples (#4980)

* First samples 1st batch

* Fix sample paths

* Fix workflow samples

* Fix workflow dependency

* Correct env vars

* Increase idle timeout

* Fix workflows HIL sample

* Fix more workflow samples

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

@@ -18,7 +18,7 @@ This sample shows how to create agents backed by Azure OpenAI Responses and use
 
 Prerequisites:
 - FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
-- AZURE_AI_MODEL_DEPLOYMENT_NAME must be set to your Azure OpenAI model deployment name.
+- FOUNDRY_MODEL must be the deployment name of a model in your Foundry project.
 - Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 - Basic familiarity with WorkflowBuilder, edges, events, and streaming runs.
 """
@@ -27,7 +27,7 @@ Prerequisites:
 async def main() -> None:
     client = FoundryChatClient(
         project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        model=os.environ["FOUNDRY_MODEL"],
         credential=AzureCliCredential(),
     )
 

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

@@ -39,7 +39,7 @@ Demonstrate:
 
 Prerequisites:
 - FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
-- AZURE_AI_MODEL_DEPLOYMENT_NAME must be set to your Azure OpenAI model deployment name.
+- FOUNDRY_MODEL must be set to your Azure OpenAI model deployment name.
 - Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 - Basic familiarity with agents, workflows, and executors in the agent framework.
 """
@@ -60,7 +60,7 @@ async def intercept_agent_response(
 async def main() -> None:
     client = FoundryChatClient(
         project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        model=os.environ["FOUNDRY_MODEL"],
         credential=AzureCliCredential(),
     )
 

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

@@ -37,7 +37,7 @@ Demonstrates:
 
 Prerequisites:
 - FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
-- Azure OpenAI configured for FoundryChatClient with required environment variables.
+- FOUNDRY_MODEL must be set to your Azure OpenAI model deployment name.
 - Authentication via azure-identity. Run `az login` before executing.
 """
 
@@ -104,7 +104,7 @@ async def main() -> None:
     research_agent = Agent(
         client=FoundryChatClient(
             project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-            model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            model=os.environ["FOUNDRY_MODEL"],
             credential=AzureCliCredential(),
         ),
         name="research_agent",
@@ -116,7 +116,7 @@ async def main() -> None:
     final_editor_agent = Agent(
         client=FoundryChatClient(
             project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-            model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            model=os.environ["FOUNDRY_MODEL"],
             credential=AzureCliCredential(),
         ),
         name="final_editor_agent",

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

@@ -18,7 +18,7 @@ This sample shows how to create AzureOpenAI Chat Agents and use them in a workfl
 
 Prerequisites:
 - FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
-- Azure OpenAI configured for FoundryChatClient with required environment variables.
+- FOUNDRY_MODEL must be set to your Azure OpenAI model deployment name.
 - Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 - Basic familiarity with WorkflowBuilder, edges, events, and streaming runs.
 """
@@ -29,7 +29,7 @@ async def main():
     # Create the agents
     _writer_client = FoundryChatClient(
         project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        model=os.environ["FOUNDRY_MODEL"],
         credential=AzureCliCredential(),
     )
     writer_agent = Agent(
@@ -42,7 +42,7 @@ async def main():
 
     _reviewer_client = FoundryChatClient(
         project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        model=os.environ["FOUNDRY_MODEL"],
         credential=AzureCliCredential(),
     )
     reviewer_agent = Agent(

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

@@ -49,7 +49,7 @@ Demonstrates:
 
 Prerequisites:
 - FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
-- Azure OpenAI configured for FoundryChatClient with required environment variables.
+- FOUNDRY_MODEL must be set to your Azure OpenAI model deployment name.
 - Authentication via azure-identity. Run `az login` before executing.
 """
 
@@ -122,11 +122,7 @@ class Coordinator(Executor):
         # Writer agent response; request human feedback.
         # Preserve the full conversation so the final editor
         # can see tool traces and the initial prompt.
-        conversation: list[Message]
-        if draft.full_conversation is not None:
-            conversation = list(draft.full_conversation)
-        else:
-            conversation = list(draft.agent_response.messages)
+        conversation = list(draft.full_conversation)
         draft_text = draft.agent_response.text.strip()
         if not draft_text:
             draft_text = "No draft text was produced."
@@ -178,7 +174,7 @@ def create_writer_agent() -> Agent:
     return Agent(
         client=FoundryChatClient(
             project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-            model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            model=os.environ["FOUNDRY_MODEL"],
             credential=AzureCliCredential(),
         ),
         name="writer_agent",
@@ -188,7 +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",
+        },
     )
 
 
@@ -197,7 +195,7 @@ def create_final_editor_agent() -> Agent:
     return Agent(
         client=FoundryChatClient(
             project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-            model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            model=os.environ["FOUNDRY_MODEL"],
             credential=AzureCliCredential(),
         ),
         name="final_editor_agent",

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

@@ -25,7 +25,7 @@ Demonstrates:
 
 Prerequisites:
 - FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
-- Azure OpenAI access configured for FoundryChatClient (use az login + env vars)
+- FOUNDRY_MODEL must be set to your Azure OpenAI model deployment name.
 - Familiarity with Workflow events (WorkflowEvent with type "output")
 """
 
@@ -34,7 +34,7 @@ async def main() -> None:
     # 1) Create three domain agents using FoundryChatClient
     client = FoundryChatClient(
         project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        model=os.environ["FOUNDRY_MODEL"],
         credential=AzureCliCredential(),
     )
 
@@ -69,7 +69,7 @@ async def main() -> None:
     workflow = ConcurrentBuilder(participants=[researcher, marketer, legal]).build()
 
     # 3) Expose the concurrent workflow as an agent for easy reuse
-    agent = Agent(client=workflow, name="ConcurrentWorkflowAgent")
+    agent = workflow.as_agent()
     prompt = "We are launching a new budget-friendly electric bike for urban commuters."
 
     agent_response = await agent.run(prompt)

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

@@ -33,7 +33,7 @@ Note: When an agent is passed to a workflow, the workflow wraps the agent in a m
 
 Prerequisites:
 - FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
-- Azure OpenAI configured for FoundryChatClient with required environment variables.
+- FOUNDRY_MODEL must be set to your Azure OpenAI model deployment name.
 - Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 - Basic familiarity with WorkflowBuilder, executors, edges, events, and streaming or non streaming runs.
 """
@@ -54,7 +54,7 @@ class Writer(Executor):
         self.agent = Agent(
             client=FoundryChatClient(
                 project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-                model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+                model=os.environ["FOUNDRY_MODEL"],
                 credential=AzureCliCredential(),
             ),
             instructions=(
@@ -101,7 +101,7 @@ class Reviewer(Executor):
         self.agent = Agent(
             client=FoundryChatClient(
                 project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-                model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+                model=os.environ["FOUNDRY_MODEL"],
                 credential=AzureCliCredential(),
             ),
             instructions=(

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

@@ -32,7 +32,7 @@ async def main() -> None:
         instructions="Gather concise facts that help a teammate answer the question.",
         client=FoundryChatClient(
             project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-            model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            model=os.environ["FOUNDRY_MODEL"],
             credential=AzureCliCredential(),
         ),
     )
@@ -43,14 +43,14 @@ async def main() -> None:
         instructions="Compose clear and structured answers using any notes provided.",
         client=FoundryChatClient(
             project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-            model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            model=os.environ["FOUNDRY_MODEL"],
             credential=AzureCliCredential(),
         ),
     )
 
     _orch_client = FoundryChatClient(
         project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        model=os.environ["FOUNDRY_MODEL"],
         credential=AzureCliCredential(),
     )
 
@@ -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

@@ -31,7 +31,7 @@ them to transfer control to each other based on the conversation context.
 Prerequisites:
     - FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
     - `az login` (Azure CLI authentication)
-    - Environment variables configured for FoundryChatClient (AZURE_AI_MODEL_DEPLOYMENT_NAME)
+    - Environment variables configured for FoundryChatClient (FOUNDRY_MODEL)
 
 Key Concepts:
     - Auto-registered handoff tools: HandoffBuilder automatically creates handoff tools
@@ -159,7 +159,7 @@ async def main() -> None:
     # Initialize the Azure OpenAI chat client
     client = FoundryChatClient(
         project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        model=os.environ["FOUNDRY_MODEL"],
         credential=AzureCliCredential(),
     )
 
@@ -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/magentic_workflow_as_agent.py

@@ -23,7 +23,7 @@ like any other agent while still emitting callback telemetry.
 
 Prerequisites:
 - FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
-- OpenAI credentials configured for `FoundryChatClient` and `FoundryChatClient`.
+- FOUNDRY_MODEL must be set to your Azure OpenAI model deployment name.
 """
 
 
@@ -37,7 +37,7 @@ async def main() -> None:
         # This agent requires the gpt-4o-search-preview model to perform web searches.
         client=FoundryChatClient(
             project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-            model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            model=os.environ["FOUNDRY_MODEL"],
             credential=AzureCliCredential(),
         ),
     )
@@ -45,7 +45,7 @@ async def main() -> None:
     # Create code interpreter tool using instance method
     coder_client = FoundryChatClient(
         project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        model=os.environ["FOUNDRY_MODEL"],
         credential=AzureCliCredential(),
     )
     code_interpreter_tool = coder_client.get_code_interpreter_tool()
@@ -65,7 +65,7 @@ async def main() -> None:
         instructions="You coordinate a team to complete complex tasks efficiently.",
         client=FoundryChatClient(
             project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-            model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            model=os.environ["FOUNDRY_MODEL"],
             credential=AzureCliCredential(),
         ),
     )
@@ -98,7 +98,7 @@ async def main() -> None:
     try:
         # Wrap the workflow as an agent for composition scenarios
         print("\nWrapping workflow as an agent and running...")
-        workflow_agent = Agent(client=workflow, name="MagenticWorkflowAgent")
+        workflow_agent = workflow.as_agent()
 
         last_response_id: str | None = None
         async for update in workflow_agent.run(task, stream=True):

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

@@ -27,7 +27,7 @@ Note on internal adapters:
 
 Prerequisites:
 - FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
-- Azure OpenAI access configured for FoundryChatClient (use az login + env vars)
+- FOUNDRY_MODEL must be set to your Azure OpenAI model deployment name.
 """
 
 
@@ -35,7 +35,7 @@ async def main() -> None:
     # 1) Create agents
     client = FoundryChatClient(
         project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        model=os.environ["FOUNDRY_MODEL"],
         credential=AzureCliCredential(),
     )
 
@@ -55,7 +55,7 @@ async def main() -> None:
     workflow = SequentialBuilder(participants=[writer, reviewer]).build()
 
     # 3) Treat the workflow itself as an agent for follow-up invocations
-    agent = Agent(client=workflow, name="SequentialWorkflowAgent")
+    agent = workflow.as_agent()
     prompt = "Write a tagline for a budget-friendly eBike."
     agent_response = await agent.run(prompt)
 

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

@@ -8,7 +8,6 @@ from dataclasses import dataclass
 from pathlib import Path
 from typing import Any
 
-from agent_framework import Agent
 from agent_framework.foundry import FoundryChatClient
 from azure.identity import AzureCliCredential
 from dotenv import load_dotenv
@@ -50,7 +49,7 @@ to the Worker. The workflow completes when idle.
 
 Prerequisites:
 - FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
-- OpenAI account configured and accessible for FoundryChatClient.
+- FOUNDRY_MODEL must be set to your Azure OpenAI model deployment name.
 - Familiarity with WorkflowBuilder, Executor, and WorkflowContext from agent_framework.
 - Understanding of request-response message handling in executors.
 - (Optional) Review of reflection and escalation patterns, such as those in
@@ -113,14 +112,14 @@ async def main() -> None:
         id="worker",
         client=FoundryChatClient(
             project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-            model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            model=os.environ["FOUNDRY_MODEL"],
             credential=AzureCliCredential(),
         ),
     )
     reviewer = ReviewerWithHumanInTheLoop(worker_id="worker")
 
-    agent = Agent(
-        client=(WorkflowBuilder(start_executor=worker).add_edge(worker, reviewer).add_edge(reviewer, worker).build()),
+    agent = (
+        WorkflowBuilder(start_executor=worker).add_edge(worker, reviewer).add_edge(reviewer, worker).build().as_agent()
     )
 
     print("Running workflow agent with user query...")
@@ -165,7 +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/agents/workflow_as_agent_kwargs.py

@@ -35,7 +35,7 @@ When to use Agent(client=workflow,):
 
 Prerequisites:
 - FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
-- Environment variables configured
+- FOUNDRY_MODEL must be set to your Azure OpenAI model deployment name.
 """
 
 
@@ -89,7 +89,7 @@ async def main() -> None:
     # Create chat client
     client = FoundryChatClient(
         project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        model=os.environ["FOUNDRY_MODEL"],
         credential=AzureCliCredential(),
     )
 
@@ -109,7 +109,7 @@ async def main() -> None:
     workflow = SequentialBuilder(participants=[agent]).build()
 
     # Expose the workflow as an agent Agent(client=using,)
-    workflow_agent = Agent(client=workflow, name="WorkflowAgent")
+    workflow_agent = workflow.as_agent()
 
     # Define custom context that will flow to tools via kwargs
     custom_data = {

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

@@ -6,7 +6,6 @@ from dataclasses import dataclass
 from uuid import uuid4
 
 from agent_framework import (
-    Agent,
     AgentResponse,
     Executor,
     Message,
@@ -41,7 +40,7 @@ Key Concepts Demonstrated:
 
 Prerequisites:
 - FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
-- OpenAI account configured and accessible for FoundryChatClient.
+- FOUNDRY_MODEL must be set to your Azure OpenAI model deployment name.
 - Familiarity with WorkflowBuilder, Executor, WorkflowContext, and event handling.
 - Understanding of how agent messages are generated, reviewed, and re-submitted.
 """
@@ -198,7 +197,7 @@ async def main() -> None:
         id="worker",
         client=FoundryChatClient(
             project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-            model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            model=os.environ["FOUNDRY_MODEL"],
             credential=AzureCliCredential(),
         ),
     )
@@ -206,13 +205,13 @@ async def main() -> None:
         id="reviewer",
         client=FoundryChatClient(
             project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-            model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            model=os.environ["FOUNDRY_MODEL"],
             credential=AzureCliCredential(),
         ),
     )
 
-    agent = Agent(
-        client=(WorkflowBuilder(start_executor=worker).add_edge(worker, reviewer).add_edge(reviewer, worker).build()),
+    agent = (
+        WorkflowBuilder(start_executor=worker).add_edge(worker, reviewer).add_edge(reviewer, worker).build().as_agent()
     )
 
     print("Running workflow agent with user query...")

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

@@ -38,7 +38,7 @@ Use cases:
 
 Prerequisites:
 - FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
-- Environment variables configured for FoundryChatClient
+- FOUNDRY_MODEL must be set to your Azure OpenAI model deployment name.
 """
 
 
@@ -46,7 +46,7 @@ async def main() -> None:
     # Create a chat client
     client = FoundryChatClient(
         project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        model=os.environ["FOUNDRY_MODEL"],
         credential=AzureCliCredential(),
     )
 
@@ -72,7 +72,7 @@ async def main() -> None:
     workflow = SequentialBuilder(participants=[assistant, summarizer]).build()
 
     # Wrap the workflow as an agent
-    agent = Agent(client=workflow, name="ConversationalWorkflowAgent")
+    agent = workflow.as_agent()
 
     # Create a session to maintain history
     session = agent.create_session()
@@ -133,7 +133,7 @@ async def demonstrate_session_serialization() -> None:
     """
     client = FoundryChatClient(
         project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        model=os.environ["FOUNDRY_MODEL"],
         credential=AzureCliCredential(),
     )
 
@@ -144,7 +144,7 @@ async def demonstrate_session_serialization() -> None:
     )
 
     workflow = SequentialBuilder(participants=[memory_assistant]).build()
-    agent = Agent(client=workflow, name="MemoryWorkflowAgent")
+    agent = workflow.as_agent()
 
     # Create initial session and have a conversation
     session = agent.create_session()