Microsoft Agent Framework-Workflows-Orchestrierungen – Übergabe

Einrichten des Azure OpenAI-Clients

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();

Definieren Sie Ihre spezialisierten Agenten

Erstellen Sie domänenspezifische Agenten und einen Triage-Agenten für das 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");

Konfigurieren von Handoff-Regeln

Definieren Sie, welche Agents an welche anderen Agents übergeben werden können:

// 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();

Interaktiver Übergabeworkflow ausführen

Bearbeitung mehrstufiger Unterhaltungen mit dynamischem Agentenwechsel:

// 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 AgentRunUpdateEvent 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));
}

Beispielinteraktion

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.

Einrichten des Chatclients

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

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

Definieren Sie Ihre spezialisierten Agenten

Erstellen Sie domänenspezifische Agenten mit einem Koordinator für Routing.

# Create triage/coordinator agent
triage_agent = chat_client.create_agent(
    instructions=(
        "You are frontline support triage. Read the latest user message and decide whether "
        "to hand off to refund_agent, order_agent, or support_agent. Provide a brief natural-language "
        "response for the user. When delegation is required, call the matching handoff tool "
        "(`handoff_to_refund_agent`, `handoff_to_order_agent`, or `handoff_to_support_agent`)."
    ),
    name="triage_agent",
)

# Create specialist agents
refund_agent = chat_client.create_agent(
    instructions=(
        "You handle refund workflows. Ask for any order identifiers you require and outline the refund steps."
    ),
    name="refund_agent",
)

order_agent = chat_client.create_agent(
    instructions=(
        "You resolve shipping and fulfillment issues. Clarify the delivery problem and describe the actions "
        "you will take to remedy it."
    ),
    name="order_agent",
)

support_agent = chat_client.create_agent(
    instructions=(
        "You are a general support agent. Offer empathetic troubleshooting and gather missing details if the "
        "issue does not match other specialists."
    ),
    name="support_agent",
)

Konfigurieren von Handoff-Regeln

Erstellen Sie den Übergabeworkflow mithilfe von HandoffBuilder:

from agent_framework import HandoffBuilder

# Build the handoff workflow
workflow = (
    HandoffBuilder(
        name="customer_support_handoff",
        participants=[triage_agent, refund_agent, order_agent, support_agent],
    )
    .set_coordinator("triage_agent")
    .with_termination_condition(
        # Terminate after a certain number of user messages
        lambda conv: sum(1 for msg in conv if msg.role.value == "user") >= 10
    )
    .build()
)

Für erweitertes Routing können Sie Spezialisten-zu-Spezialisten Handoffs konfigurieren:

# Enable return-to-previous and add specialist-to-specialist handoffs
workflow = (
    HandoffBuilder(
        name="advanced_handoff",
        participants=[coordinator, technical, account, billing],
    )
    .set_coordinator(coordinator)
    .add_handoff(coordinator, [technical, account, billing])  # Coordinator routes to all specialists
    .add_handoff(technical, [billing, account])  # Technical can route to billing or account
    .add_handoff(account, [technical, billing])  # Account can route to technical or billing
    .add_handoff(billing, [technical, account])  # Billing can route to technical or account
    .enable_return_to_previous(True)  # User inputs route directly to current specialist
    .build()
)

Interaktiver Übergabeworkflow ausführen

Behandeln von Multi-Turn-Unterhaltungen mit Benutzereingabeanforderungen:

from agent_framework import RequestInfoEvent, HandoffUserInputRequest, 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):
        pending_requests.append(event)
        request_data = event.data
        print(f"Agent {request_data.awaiting_agent_id} is awaiting your input")
        for msg in request_data.conversation[-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: user_input for req in pending_requests}
    events = [event async for event in workflow.send_responses_streaming(responses)]

    # Process new events
    pending_requests = []
    for event in events:
        if isinstance(event, RequestInfoEvent):
            pending_requests.append(event)
        elif isinstance(event, WorkflowOutputEvent):
            print("Workflow completed!")
            conversation = event.data
            for msg in conversation:
                print(f"{msg.author_name}: {msg.text}")

Erweitert: Werkzeuggenehmigung in Handoff-Workflows

Handoff-Workflows können Agenten mit Tools enthalten, die vor der Ausführung eine menschliche Genehmigung erfordern. Dies ist nützlich für sensible Vorgänge wie die Verarbeitung von Rückerstattungen, das Tätigen von Einkäufen oder das Ausführen von unwiderruflichen Aktionen.

Definieren von Tools mit Genehmigung erforderlich

from typing import Annotated
from agent_framework import ai_function

@ai_function(approval_mode="always_require")
def submit_refund(
    refund_description: Annotated[str, "Description of the refund reason"],
    amount: Annotated[str, "Refund amount"],
    order_id: Annotated[str, "Order ID for the refund"],
) -> str:
    """Submit a refund request for manual review before processing."""
    return f"Refund recorded for order {order_id} (amount: {amount}): {refund_description}"

Erstellen Sie Agenten mit Tools, die eine Genehmigung erfordern

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

client = AzureOpenAIChatClient(credential=AzureCliCredential())

triage_agent = client.create_agent(
    name="triage_agent",
    instructions=(
        "You are a customer service triage agent. Listen to customer issues and determine "
        "if they need refund help or order tracking. Use handoff_to_refund_agent or "
        "handoff_to_order_agent to transfer them."
    ),
)

refund_agent = client.create_agent(
    name="refund_agent",
    instructions=(
        "You are a refund specialist. Help customers with refund requests. "
        "When the user confirms they want a refund and supplies order details, "
        "call submit_refund to record the request."
    ),
    tools=[submit_refund],
)

order_agent = client.create_agent(
    name="order_agent",
    instructions="You are an order tracking specialist. Help customers track their orders.",
)

Behandeln von Benutzereingabe- und Toolgenehmigungsanforderungen

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

workflow = (
    HandoffBuilder(
        name="support_with_approvals",
        participants=[triage_agent, refund_agent, order_agent],
    )
    .set_coordinator("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, HandoffUserInputRequest):
            # Agent needs user input
            print(f"Agent {request.data.awaiting_agent_id} asks:")
            for msg in request.data.conversation[-2:]:
                print(f"  {msg.author_name}: {msg.text}")

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

Mit Prüfpunkting für dauerhafte Workflows

Verwenden Sie für lang andauernde Workflows, bei denen die Toolgenehmigung Stunden oder Tage später erfolgen kann, Checkpointing:

from agent_framework import FileCheckpointStorage

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

workflow = (
    HandoffBuilder(
        name="durable_support",
        participants=[triage_agent, refund_agent, order_agent],
    )
    .set_coordinator("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, HandoffUserInputRequest):
        responses[req.request_id] = "Yes, please process the refund."

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

## Sample Interaction

```plaintext
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?

• Dynamisches Routing: Agents können entscheiden, welcher Agent die nächste Interaktion basierend auf dem Kontext verarbeiten soll.
• AgentWorkflowBuilder.StartHandoffWith(): Definiert den ersten Agent, der den Workflow startet.
• WithHandoff() und WithHandoffs(): Konfiguriert Übergaberegeln zwischen bestimmten Agenten
• Kontexterhaltung: Die vollständige Gesprächshistorie wird über alle Übertragungen hinweg beibehalten.
• Multi-Turn-Support: Unterstützt fortlaufende Gespräche mit einem nahtlosen Wechsel zwischen Agenten
• Spezialisierte Expertise: Jeder Agent konzentriert sich auf seine Domäne, während er durch Handoffs zusammenarbeitet

• Dynamisches Routing: Agents können entscheiden, welcher Agent die nächste Interaktion basierend auf dem Kontext verarbeiten soll.
• HandoffBuilder: Erstellt Workflows mit automatischer Handofftoolregistrierung
• set_coordinator(): Definiert, welcher Agent zuerst Benutzereingaben empfängt.
• add_handoff(): Konfiguriert bestimmte Übergabebeziehungen zwischen Agents
• enable_return_to_previous(): Leitet Benutzereingaben direkt an den aktuellen Spezialisten weiter und überspringt die Neubewertung des Koordinators.
• Kontexterhaltung: Die vollständige Gesprächshistorie wird über alle Übertragungen hinweg beibehalten.
• Anforderungs-/Antwortzyklus: Workflow fordert Benutzereingaben an, verarbeitet Antworten und setzt fort, bis die Beendigungsbedingung erfüllt ist.
• Toolgenehmigung: Verwendung @ai_function(approval_mode="always_require") für vertrauliche Vorgänge, die eine menschliche Genehmigung benötigen
• FunctionApprovalRequestContent: Wird ausgegeben, wenn ein Agent ein Tool aufruft, das eine Genehmigung erfordert; verwenden create_response(approved=...) , um zu antworten
• Checkpointing: Wird with_checkpointing() für dauerhafte Workflows verwendet, die über Prozessneustarts hinweg anhalten und fortsetzen können
• Spezialisierte Expertise: Jeder Agent konzentriert sich auf seine Domäne, während er durch Handoffs zusammenarbeitet