Microsoft Agent Framework Workflows Orchestrations - Handoff

Set Up the Azure OpenAI Client

using System;
using System.Collections.Generic;
using System.Threading.Tasks;
using Azure.AI.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI.Workflows;
using Microsoft.Extensions.AI;
using Microsoft.Agents.AI;

// 1) Set up the Azure OpenAI client
var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ??
    throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
var client = new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential())
    .GetChatClient(deploymentName)
    .AsIChatClient();

Define Your Specialized Agents

Create domain-specific agents and a triage agent for routing:

// 2) Create specialized agents
ChatClientAgent historyTutor = new(client,
    "You provide assistance with historical queries. Explain important events and context clearly. Only respond about history.",
    "history_tutor",
    "Specialist agent for historical questions");

ChatClientAgent mathTutor = new(client,
    "You provide help with math problems. Explain your reasoning at each step and include examples. Only respond about math.",
    "math_tutor",
    "Specialist agent for math questions");

ChatClientAgent triageAgent = new(client,
    "You determine which agent to use based on the user's homework question. ALWAYS handoff to another agent.",
    "triage_agent",
    "Routes messages to the appropriate specialist agent");

Configure Handoff Rules

Define which agents can hand off to which other agents:

// 3) Build handoff workflow with routing rules
var workflow = AgentWorkflowBuilder.StartHandoffWith(triageAgent)
    .WithHandoffs(triageAgent, [mathTutor, historyTutor]) // Triage can route to either specialist
    .WithHandoff(mathTutor, triageAgent)                  // Math tutor can return to triage
    .WithHandoff(historyTutor, triageAgent)               // History tutor can return to triage
    .Build();

Run Interactive Handoff Workflow

Handle multi-turn conversations with dynamic agent switching:

// 4) Process multi-turn conversations
List<ChatMessage> messages = new();

while (true)
{
    Console.Write("Q: ");
    string userInput = Console.ReadLine()!;
    messages.Add(new(ChatRole.User, userInput));

    // Execute workflow and process events
    StreamingRun run = await InProcessExecution.StreamAsync(workflow, messages);
    await run.TrySendMessageAsync(new TurnToken(emitEvents: true));

    List<ChatMessage> newMessages = new();
    await foreach (WorkflowEvent evt in run.WatchStreamAsync().ConfigureAwait(false))
    {
        if (evt is AgentResponseUpdateEvent e)
        {
            Console.WriteLine($"{e.ExecutorId}: {e.Data}");
        }
        else if (evt is WorkflowOutputEvent outputEvt)
        {
            newMessages = (List<ChatMessage>)outputEvt.Data!;
            break;
        }
    }

    // Add new messages to conversation history
    messages.AddRange(newMessages.Skip(messages.Count));
}

Sample Interaction

Q: What is the derivative of x^2?
triage_agent: This is a math question. I'll hand this off to the math tutor.
math_tutor: The derivative of x^2 is 2x. Using the power rule, we bring down the exponent (2) and multiply it by the coefficient (1), then reduce the exponent by 1: d/dx(x^2) = 2x^(2-1) = 2x.

Q: Tell me about World War 2
triage_agent: This is a history question. I'll hand this off to the history tutor.
history_tutor: World War 2 was a global conflict from 1939 to 1945. It began when Germany invaded Poland and involved most of the world's nations. Key events included the Holocaust, Pearl Harbor attack, D-Day invasion, and ended with atomic bombs on Japan.

Q: Can you help me with calculus integration?
triage_agent: This is another math question. I'll route this to the math tutor.
math_tutor: I'd be happy to help with calculus integration! Integration is the reverse of differentiation. The basic power rule for integration is: ∫x^n dx = x^(n+1)/(n+1) + C, where C is the constant of integration.

Define a few tools for demonstration

@ai_function
def process_refund(order_number: Annotated[str, "Order number to process refund for"]) -> str:
    """Simulated function to process a refund for a given order number."""
    return f"Refund processed successfully for order {order_number}."

@ai_function
def check_order_status(order_number: Annotated[str, "Order number to check status for"]) -> str:
    """Simulated function to check the status of a given order number."""
    return f"Order {order_number} is currently being processed and will ship in 2 business days."

@ai_function
def process_return(order_number: Annotated[str, "Order number to process return for"]) -> str:
    """Simulated function to process a return for a given order number."""
    return f"Return initiated successfully for order {order_number}. You will receive return instructions via email."

Set Up the Chat Client

from agent_framework.azure import AzureOpenAIChatClient
from azure.identity import AzureCliCredential

# Initialize the Azure OpenAI chat client
chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())

Define Your Specialized Agents

Create domain-specific agents with a coordinator for routing:

# Create triage/coordinator agent
triage_agent = chat_client.as_agent(
    instructions=(
        "You are frontline support triage. Route customer issues to the appropriate specialist agents "
        "based on the problem described."
    ),
    description="Triage agent that handles general inquiries.",
    name="triage_agent",
)

# Refund specialist: Handles refund requests
refund_agent = chat_client.as_agent(
    instructions="You process refund requests.",
    description="Agent that handles refund requests.",
    name="refund_agent",
    # In a real application, an agent can have multiple tools; here we keep it simple
    tools=[process_refund],
)

# Order/shipping specialist: Resolves delivery issues
order_agent = chat_client.as_agent(
    instructions="You handle order and shipping inquiries.",
    description="Agent that handles order tracking and shipping issues.",
    name="order_agent",
    # In a real application, an agent can have multiple tools; here we keep it simple
    tools=[check_order_status],
)

# Return specialist: Handles return requests
return_agent = chat_client.as_agent(
    instructions="You manage product return requests.",
    description="Agent that handles return processing.",
    name="return_agent",
    # In a real application, an agent can have multiple tools; here we keep it simple
    tools=[process_return],
)

Configure Handoff Rules

Build the handoff workflow using HandoffBuilder:

from agent_framework import HandoffBuilder

# Build the handoff workflow
workflow = (
    HandoffBuilder(
        name="customer_support_handoff",
        participants=[triage_agent, refund_agent, order_agent, return_agent],
    )
    .with_start_agent(triage_agent) # Triage receives initial user input
    .with_termination_condition(
        # 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.
        lambda conversation: len(conversation) > 0 and "welcome" in conversation[-1].text.lower()
    )
    .build()
)

By default, all agents can handoff to each other. For more advanced routing, you can configure handoffs:

workflow = (
    HandoffBuilder(
        name="customer_support_handoff",
        participants=[triage_agent, refund_agent, order_agent, return_agent],
    )
    .with_start_agent(triage_agent) # Triage receives initial user input
    .with_termination_condition(
        # 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.
        lambda conversation: len(conversation) > 0 and "welcome" in conversation[-1].text.lower()
    )
    # Triage cannot route directly to refund agent
    .add_handoff(triage_agent, [order_agent, return_agent])
    # Only the return agent can handoff to refund agent - users wanting refunds after returns
    .add_handoff(return_agent, [refund_agent])
    # All specialists can handoff back to triage for further routing
    .add_handoff(order_agent, [triage_agent])
    .add_handoff(return_agent, [triage_agent])
    .add_handoff(refund_agent, [triage_agent])
    .build()
)

Run Handoff Agent Interaction

Unlike other orchestrations, handoff is interactive because an agent may not decide to handoff after every turn. If an agent doesn't handoff, human input is required to continue the conversation. See Autonomous Mode for bypassing this requirement. In other orchestrations, after an agent responds, the control either goes to the orchestrator or the next agent.

When an agent in a handoff workflow decides not to handoff (a handoff is triggered by a special tool call), the workflow emits a RequestInfoEvent with a HandoffAgentUserRequest payload containing the agent's most recent messages. The user must respond to this request to continue the workflow.

from agent_framework import RequestInfoEvent, HandoffAgentUserRequest, WorkflowOutputEvent

# Start workflow with initial user message
events = [event async for event in workflow.run_stream("I need help with my order")]

# Process events and collect pending input requests
pending_requests = []
for event in events:
    if isinstance(event, RequestInfoEvent) and isinstance(event.data, HandoffAgentUserRequest):
        pending_requests.append(event)
        request_data = event.data
        print(f"Agent {event.source_executor_id} is awaiting your input")
        # The request contains the most recent messages generated by the
        # agent requesting input
        for msg in request_data.agent_response.messages[-3:]:
            print(f"{msg.author_name}: {msg.text}")

# Interactive loop: respond to requests
while pending_requests:
    user_input = input("You: ")

    # Send responses to all pending requests
    responses = {req.request_id: HandoffAgentUserRequest.create_response(user_input) for req in pending_requests}
    # You can also send a `HandoffAgentUserRequest.terminate()` to end the workflow early
    events = [event async for event in workflow.send_responses_streaming(responses)]

    # Process new events
    pending_requests = []
    for event in events:
        # Check for new input requests

Autonomous Mode

The Handoff orchestration is designed for interactive scenarios where human input is required when an agent decides not to handoff. However, as an experimental feature, you can enable "autonomous mode" to allow the workflow to continue without human intervention. In this mode, when an agent decides not to handoff, the workflow automatically sends a default response (e.g.User did not respond. Continue assisting autonomously.) to the agent, allowing it to continue the conversation.

Autonomous Mode is enabled by calling with_autonomous_mode() on the HandoffBuilder. This configures the workflow to automatically respond to input requests with a default message, allowing the agent to continue without waiting for human input.

workflow = (
    HandoffBuilder(
        name="autonomous_customer_support",
        participants=[triage_agent, refund_agent, order_agent, return_agent],
    )
    .with_start_agent(triage_agent)
    .with_autonomous_mode()
    .build()
)

You can also enable autonomous mode on only a subset of agents by passing a list of agent instances to with_autonomous_mode().

workflow = (
    HandoffBuilder(
        name="partially_autonomous_support",
        participants=[triage_agent, refund_agent, order_agent, return_agent],
    )
    .with_start_agent(triage_agent)
    .with_autonomous_mode(agents=[triage_agent])  # Only triage_agent runs autonomously
    .build()
)

You can customize the default response message.

workflow = (
    HandoffBuilder(
        name="custom_autonomous_support",
        participants=[triage_agent, refund_agent, order_agent, return_agent],
    )
    .with_start_agent(triage_agent)
    .with_autonomous_mode(
        agents=[triage_agent],
        prompts={triage_agent.name: "Continue with your best judgment as the user is unavailable."},
    )
    .build()
)

You can customize the number of turns an agent can run autonomously before requiring human input. This can prevent the workflow from running indefinitely without user involvement.

workflow = (
    HandoffBuilder(
        name="limited_autonomous_support",
        participants=[triage_agent, refund_agent, order_agent, return_agent],
    )
    .with_start_agent(triage_agent)
    .with_autonomous_mode(
        agents=[triage_agent],
        turn_limits={triage_agent.name: 3},  # Max 3 autonomous turns
    )
    .build()
)

Advanced: Tool Approval in Handoff Workflows

Handoff workflows can include agents with tools that require human approval before execution. This is useful for sensitive operations like processing refunds, making purchases, or executing irreversible actions.

Define Tools with Approval Required

from typing import Annotated
from agent_framework import ai_function

@ai_function(approval_mode="always_require")
def process_refund(order_number: Annotated[str, "Order number to process refund for"]) -> str:
    """Simulated function to process a refund for a given order number."""
    return f"Refund processed successfully for order {order_number}."

Create Agents with Approval-Required Tools

from agent_framework import ChatAgent
from agent_framework.azure import AzureOpenAIChatClient
from azure.identity import AzureCliCredential

client = AzureOpenAIChatClient(credential=AzureCliCredential())

triage_agent = chat_client.as_agent(
    instructions=(
        "You are frontline support triage. Route customer issues to the appropriate specialist agents "
        "based on the problem described."
    ),
    description="Triage agent that handles general inquiries.",
    name="triage_agent",
)

refund_agent = chat_client.as_agent(
    instructions="You process refund requests.",
    description="Agent that handles refund requests.",
    name="refund_agent",
    tools=[process_refund],
)

order_agent = chat_client.as_agent(
    instructions="You handle order and shipping inquiries.",
    description="Agent that handles order tracking and shipping issues.",
    name="order_agent",
    tools=[check_order_status],
)

Handle Both User Input and Tool Approval Requests

from agent_framework import (
    FunctionApprovalRequestContent,
    HandoffBuilder,
    HandoffAgentUserRequest,
    RequestInfoEvent,
    WorkflowOutputEvent,
)

workflow = (
    HandoffBuilder(
        name="support_with_approvals",
        participants=[triage_agent, refund_agent, order_agent],
    )
    .with_start_agent(triage_agent)
    .build()
)

pending_requests: list[RequestInfoEvent] = []

# Start workflow
async for event in workflow.run_stream("My order 12345 arrived damaged. I need a refund."):
    if isinstance(event, RequestInfoEvent):
        pending_requests.append(event)

# Process pending requests - could be user input OR tool approval
while pending_requests:
    responses: dict[str, object] = {}

    for request in pending_requests:
        if isinstance(request.data, HandoffAgentUserRequest):
            # Agent needs user input
            print(f"Agent {request.source_executor_id} asks:")
            for msg in request.data.agent_response.messages[-2:]:
                print(f"  {msg.author_name}: {msg.text}")

            user_input = input("You: ")
            responses[request.request_id] = HandoffAgentUserRequest.create_response(user_input)

        elif isinstance(request.data, FunctionApprovalRequestContent):
            # Agent wants to call a tool that requires approval
            func_call = request.data.function_call
            args = func_call.parse_arguments() or {}

            print(f"\nTool approval requested: {func_call.name}")
            print(f"Arguments: {args}")

            approval = input("Approve? (y/n): ").strip().lower() == "y"
            responses[request.request_id] = request.data.create_response(approved=approval)

    # Send all responses and collect new requests
    pending_requests = []
    async for event in workflow.send_responses_streaming(responses):
        if isinstance(event, RequestInfoEvent):
            pending_requests.append(event)
        elif isinstance(event, WorkflowOutputEvent):
            print("\nWorkflow completed!")

With Checkpointing for Durable Workflows

For long-running workflows where tool approvals may happen hours or days later, use checkpointing:

from agent_framework import FileCheckpointStorage

storage = FileCheckpointStorage(storage_path="./checkpoints")

workflow = (
    HandoffBuilder(
        name="durable_support",
        participants=[triage_agent, refund_agent, order_agent],
    )
    .with_start_agent(triage_agent)
    .with_checkpointing(storage)
    .build()
)

# Initial run - workflow pauses when approval is needed
pending_requests = []
async for event in workflow.run_stream("I need a refund for order 12345"):
    if isinstance(event, RequestInfoEvent):
        pending_requests.append(event)

# Process can exit here - checkpoint is saved automatically

# Later: Resume from checkpoint and provide approval
checkpoints = await storage.list_checkpoints()
latest = sorted(checkpoints, key=lambda c: c.timestamp, reverse=True)[0]

# Step 1: Restore checkpoint to reload pending requests
restored_requests = []
async for event in workflow.run_stream(checkpoint_id=latest.checkpoint_id):
    if isinstance(event, RequestInfoEvent):
        restored_requests.append(event)

# Step 2: Send responses
responses = {}
for req in restored_requests:
    if isinstance(req.data, FunctionApprovalRequestContent):
        responses[req.request_id] = req.data.create_response(approved=True)
    elif isinstance(req.data, HandoffAgentUserRequest):
        responses[req.request_id] = HandoffAgentUserRequest.create_response("Yes, please process the refund.")

async for event in workflow.send_responses_streaming(responses):
    if isinstance(event, WorkflowOutputEvent):
        print("Refund workflow completed!")

Sample Interaction

User: I need help with my order

triage_agent: I'd be happy to help you with your order. Could you please provide more details about the issue?

User: My order 1234 arrived damaged

triage_agent: I'm sorry to hear that your order arrived damaged. I will connect you with a specialist.

support_agent: I'm sorry about the damaged order. To assist you better, could you please:
- Describe the damage
- Would you prefer a replacement or refund?

User: I'd like a refund

triage_agent: I'll connect you with the refund specialist.

refund_agent: I'll process your refund for order 1234. Here's what will happen next:
1. Verification of the damaged items
2. Refund request submission
3. Return instructions if needed
4. Refund processing within 5-10 business days

Could you provide photos of the damage to expedite the process?

• Dynamic Routing: Agents can decide which agent should handle the next interaction based on context
• AgentWorkflowBuilder.StartHandoffWith(): Defines the initial agent that starts the workflow
• WithHandoff() and WithHandoffs(): Configures handoff rules between specific agents
• Context Preservation: Full conversation history is maintained across all handoffs
• Multi-turn Support: Supports ongoing conversations with seamless agent switching
• Specialized Expertise: Each agent focuses on their domain while collaborating through handoffs

• Dynamic Routing: Agents can decide which agent should handle the next interaction based on context
• HandoffBuilder: Creates workflows with automatic handoff tool registration
• with_start_agent(): Defines which agent receives user input first
• add_handoff(): Configures specific handoff relationships between agents
• Context Preservation: Full conversation history is maintained across all handoffs
• Request/Response Cycle: Workflow requests user input, processes responses, and continues until termination condition is met
• Tool Approval: Use @ai_function(approval_mode="always_require") for sensitive operations that need human approval
• FunctionApprovalRequestContent: Emitted when an agent calls a tool requiring approval; use create_response(approved=...) to respond
• Checkpointing: Use with_checkpointing() for durable workflows that can pause and resume across process restarts
• Specialized Expertise: Each agent focuses on their domain while collaborating through handoffs