Microsoft Agent Framework-Workflows-Orchestrierungen – gleichzeitig

Bei gleichzeitiger Orchestrierung arbeiten mehrere Agents gleichzeitig und unabhängig an derselben Aufgabe und bieten unterschiedliche Perspektiven für die gleiche Eingabe.

Einrichten des Azure OpenAI-Clients

using System;
using System.Collections.Generic;
using System.Linq;
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 Agenten

Erstellen Sie mehrere spezialisierte Agents, die gleichzeitig an derselben Aufgabe arbeiten:

// 2) Helper method to create translation agents
static ChatClientAgent GetTranslationAgent(string targetLanguage, IChatClient chatClient) =>
    new(chatClient,
        $"You are a translation assistant who only responds in {targetLanguage}. Respond to any " +
        $"input by outputting the name of the input language and then translating the input to {targetLanguage}.");

// Create translation agents for concurrent processing
var translationAgents = (from lang in (string[])["French", "Spanish", "English"]
                         select GetTranslationAgent(lang, client));

Einrichten der gleichzeitigen Orchestrierung

Erstellen Sie den Workflow mithilfe von AgentWorkflowBuilder, um Agents parallel auszuführen.

// 3) Build concurrent workflow
var workflow = AgentWorkflowBuilder.BuildConcurrent(translationAgents);

Ausführen des gleichzeitigen Workflows und Sammeln von Ergebnissen

Führen Sie den Workflow aus und verarbeiten Sie Ereignisse von allen Agenten, die gleichzeitig laufen:

// 4) Run the workflow
var messages = new List<ChatMessage> { new(ChatRole.User, "Hello, world!") };

StreamingRun run = await InProcessExecution.StreamAsync(workflow, messages);
await run.TrySendMessageAsync(new TurnToken(emitEvents: true));

List<ChatMessage> result = 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)
    {
        result = (List<ChatMessage>)outputEvt.Data!;
        break;
    }
}

// Display aggregated results from all agents
Console.WriteLine("===== Final Aggregated Results =====");
foreach (var message in result)
{
    Console.WriteLine($"{message.Role}: {message.Content}");
}

Beispielausgabe

French_Agent: English detected. Bonjour, le monde !
Spanish_Agent: English detected. ¡Hola, mundo!
English_Agent: English detected. Hello, world!

===== Final Aggregated Results =====
User: Hello, world!
Assistant: English detected. Bonjour, le monde !
Assistant: English detected. ¡Hola, mundo!
Assistant: English detected. Hello, world!

Wichtige Konzepte

• Parallele Ausführung: Alle Agents verarbeiten die Eingabe gleichzeitig und unabhängig
• AgentWorkflowBuilder.BuildConcurrent(): Erstellt einen gleichzeitigen Workflow aus einer Sammlung von Agents.
• Automatische Aggregation: Ergebnisse aller Agents werden automatisch im Endergebnis erfasst.
• Ereignisstreaming: Echtzeitüberwachung des Agentfortschritts durch AgentRunUpdateEvent
• Vielfältige Perspektiven: Jeder Agent bringt sein einzigartiges Know-how zum gleichen Problem

Agents sind spezialisierte Entitäten, die Aufgaben verarbeiten können. Hier definieren wir drei Agenten: ein Forschungsexperte, ein Marketingexperte und ein Rechtsexperte.

from agent_framework.azure import AzureChatClient

# 1) Create three domain agents using AzureChatClient
chat_client = AzureChatClient(credential=AzureCliCredential())

researcher = chat_client.create_agent(
    instructions=(
        "You're an expert market and product researcher. Given a prompt, provide concise, factual insights,"
        " opportunities, and risks."
    ),
    name="researcher",
)

marketer = chat_client.create_agent(
    instructions=(
        "You're a creative marketing strategist. Craft compelling value propositions and target messaging"
        " aligned to the prompt."
    ),
    name="marketer",
)

legal = chat_client.create_agent(
    instructions=(
        "You're a cautious legal/compliance reviewer. Highlight constraints, disclaimers, and policy concerns"
        " based on the prompt."
    ),
    name="legal",
)

Einrichten der gleichzeitigen Orchestrierung

Mit der ConcurrentBuilder Klasse können Sie einen Workflow erstellen, um mehrere Agents parallel auszuführen. Sie übergeben die Liste der Agenten als Teilnehmer.

from agent_framework import ConcurrentBuilder

# 2) Build a concurrent workflow
# Participants are either Agents (type of AgentProtocol) or Executors
workflow = ConcurrentBuilder().participants([researcher, marketer, legal]).build()

Ausführen des gleichzeitigen Workflows und Sammeln der Ergebnisse

from agent_framework import ChatMessage, WorkflowOutputEvent

# 3) Run with a single prompt, stream progress, and pretty-print the final combined messages
output_evt: WorkflowOutputEvent  | None = None
async for event in workflow.run_stream("We are launching a new budget-friendly electric bike for urban commuters."):
    if isinstance(event, WorkflowOutputEvent):
        output_evt = event

if output_evt:
    print("===== Final Aggregated Conversation (messages) =====")
    messages: list[ChatMessage] | Any = output_evt.data
    for i, msg in enumerate(messages, start=1):
        name = msg.author_name if msg.author_name else "user"
        print(f"{'-' * 60}\n\n{i:02d} [{name}]:\n{msg.text}")

Beispielausgabe

Sample Output:

    ===== Final Aggregated Conversation (messages) =====
    ------------------------------------------------------------

    01 [user]:
    We are launching a new budget-friendly electric bike for urban commuters.
    ------------------------------------------------------------

    02 [researcher]:
    **Insights:**

    - **Target Demographic:** Urban commuters seeking affordable, eco-friendly transport;
        likely to include students, young professionals, and price-sensitive urban residents.
    - **Market Trends:** E-bike sales are growing globally, with increasing urbanization,
        higher fuel costs, and sustainability concerns driving adoption.
    - **Competitive Landscape:** Key competitors include brands like Rad Power Bikes, Aventon,
        Lectric, and domestic budget-focused manufacturers in North America, Europe, and Asia.
    - **Feature Expectations:** Customers expect reliability, ease-of-use, theft protection,
        lightweight design, sufficient battery range for daily city commutes (typically 25-40 miles),
        and low-maintenance components.

    **Opportunities:**

    - **First-time Buyers:** Capture newcomers to e-biking by emphasizing affordability, ease of
        operation, and cost savings vs. public transit/car ownership.
    ...
    ------------------------------------------------------------

    03 [marketer]:
    **Value Proposition:**
    "Empowering your city commute: Our new electric bike combines affordability, reliability, and
        sustainable design—helping you conquer urban journeys without breaking the bank."

    **Target Messaging:**

    *For Young Professionals:*
    ...
    ------------------------------------------------------------

    04 [legal]:
    **Constraints, Disclaimers, & Policy Concerns for Launching a Budget-Friendly Electric Bike for Urban Commuters:**

    **1. Regulatory Compliance**
    - Verify that the electric bike meets all applicable federal, state, and local regulations
        regarding e-bike classification, speed limits, power output, and safety features.
    - Ensure necessary certifications (e.g., UL certification for batteries, CE markings if sold internationally) are obtained.

    **2. Product Safety**
    - Include consumer safety warnings regarding use, battery handling, charging protocols, and age restrictions.

Fortgeschritten: Benutzerdefinierte Agent-Executoren

Parallele Orchestrierung unterstützt benutzerdefinierte Executoren, die Agents mit zusätzlicher Logik versehen. Dies ist nützlich, wenn Sie mehr Kontrolle darüber benötigen, wie Agents initialisiert werden und wie sie Anforderungen verarbeiten:

Definieren von benutzerdefinierten Agent-Executoren

from agent_framework import (
    AgentExecutorRequest,
    AgentExecutorResponse,
    ChatAgent,
    Executor,
    WorkflowContext,
    handler,
)

class ResearcherExec(Executor):
    agent: ChatAgent

    def __init__(self, chat_client: AzureChatClient, id: str = "researcher"):
        agent = chat_client.create_agent(
            instructions=(
                "You're an expert market and product researcher. Given a prompt, provide concise, factual insights,"
                " opportunities, and risks."
            ),
            name=id,
        )
        super().__init__(agent=agent, id=id)

    @handler
    async def run(self, request: AgentExecutorRequest, ctx: WorkflowContext[AgentExecutorResponse]) -> None:
        response = await self.agent.run(request.messages)
        full_conversation = list(request.messages) + list(response.messages)
        await ctx.send_message(AgentExecutorResponse(self.id, response, full_conversation=full_conversation))

class MarketerExec(Executor):
    agent: ChatAgent

    def __init__(self, chat_client: AzureChatClient, id: str = "marketer"):
        agent = chat_client.create_agent(
            instructions=(
                "You're a creative marketing strategist. Craft compelling value propositions and target messaging"
                " aligned to the prompt."
            ),
            name=id,
        )
        super().__init__(agent=agent, id=id)

    @handler
    async def run(self, request: AgentExecutorRequest, ctx: WorkflowContext[AgentExecutorResponse]) -> None:
        response = await self.agent.run(request.messages)
        full_conversation = list(request.messages) + list(response.messages)
        await ctx.send_message(AgentExecutorResponse(self.id, response, full_conversation=full_conversation))

Erstellen eines Workflows mit benutzerdefinierten Executors

chat_client = AzureChatClient(credential=AzureCliCredential())

researcher = ResearcherExec(chat_client)
marketer = MarketerExec(chat_client)
legal = LegalExec(chat_client)

workflow = ConcurrentBuilder().participants([researcher, marketer, legal]).build()

Erweitert: Benutzerdefinierter Aggregator

Standardmäßig aggregiert die gleichzeitige Orchestrierung alle Agentantworten in einer Liste von Nachrichten. Sie können dieses Verhalten mit einem benutzerdefinierten Aggregator überschreiben, der die Ergebnisse auf eine bestimmte Weise verarbeitet:

Definieren eines benutzerdefinierten Aggregators

# Define a custom aggregator callback that uses the chat client to summarize
async def summarize_results(results: list[Any]) -> str:
    # Extract one final assistant message per agent
    expert_sections: list[str] = []
    for r in results:
        try:
            messages = getattr(r.agent_run_response, "messages", [])
            final_text = messages[-1].text if messages and hasattr(messages[-1], "text") else "(no content)"
            expert_sections.append(f"{getattr(r, 'executor_id', 'expert')}:\n{final_text}")
        except Exception as e:
            expert_sections.append(f"{getattr(r, 'executor_id', 'expert')}: (error: {type(e).__name__}: {e})")

    # Ask the model to synthesize a concise summary of the experts' outputs
    system_msg = ChatMessage(
        Role.SYSTEM,
        text=(
            "You are a helpful assistant that consolidates multiple domain expert outputs "
            "into one cohesive, concise summary with clear takeaways. Keep it under 200 words."
        ),
    )
    user_msg = ChatMessage(Role.USER, text="\n\n".join(expert_sections))

    response = await chat_client.get_response([system_msg, user_msg])
    # Return the model's final assistant text as the completion result
    return response.messages[-1].text if response.messages else ""

Erstellen eines Workflows mit benutzerdefiniertem Aggregator

workflow = (
    ConcurrentBuilder()
    .participants([researcher, marketer, legal])
    .with_aggregator(summarize_results)
    .build()
)

output_evt: WorkflowOutputEvent | None = None
async for event in workflow.run_stream("We are launching a new budget-friendly electric bike for urban commuters."):
    if isinstance(event, WorkflowOutputEvent):
        output_evt = event

if output_evt:
    print("===== Final Consolidated Output =====")
    print(output_evt.data)

Beispielausgabe mit benutzerdefiniertem Aggregator

===== Final Consolidated Output =====
Urban e-bike demand is rising rapidly due to eco-awareness, urban congestion, and high fuel costs,
with market growth projected at a ~10% CAGR through 2030. Key customer concerns are affordability,
easy maintenance, convenient charging, compact design, and theft protection. Differentiation opportunities
include integrating smart features (GPS, app connectivity), offering subscription or leasing options, and
developing portable, space-saving designs. Partnering with local governments and bike shops can boost visibility.

Risks include price wars eroding margins, regulatory hurdles, battery quality concerns, and heightened expectations
for after-sales support. Accurate, substantiated product claims and transparent marketing (with range disclaimers)
are essential. All e-bikes must comply with local and federal regulations on speed, wattage, safety certification,
and labeling. Clear warranty, safety instructions (especially regarding batteries), and inclusive, accessible
marketing are required. For connected features, data privacy policies and user consents are mandatory.

Effective messaging should target young professionals, students, eco-conscious commuters, and first-time buyers,
emphasizing affordability, convenience, and sustainability. Slogan suggestion: "Charge Ahead—City Commutes Made
Affordable." Legal review in each target market, compliance vetting, and robust customer support policies are
critical before launch.

Wichtige Konzepte

• Parallele Ausführung: Alle Agents arbeiten gleichzeitig und unabhängig an der Aufgabe.
• Ergebnisaggregation: Ergebnisse werden gesammelt und können entweder vom Standard- oder benutzerdefinierten Aggregator verarbeitet werden.
• Vielfältige Perspektiven: Jeder Agent bringt sein einzigartiges Know-how zum gleichen Problem
• Flexible Teilnehmer: Sie können Agenten direkt verwenden oder in benutzerdefinierte Executoren umschließen
• Benutzerdefinierte Verarbeitung: Überschreiben des Standardaggregators zum Synthetisieren von Ergebnissen auf domänenspezifische Weise