PrompterOne: Agent for Claude Code

Orchestrate durable agents

This tutorial shows you how to orchestrate multiple durable AI agents using the fan-out/fan-in patterns. You'll extend the durable agent from the Create and run a durable agent tutorial to create a multi-agent system that processes a user's question, then translates the response into multiple languages concurrently.

This orchestration pattern demonstrates how to:

• Reuse the durable agent from the first tutorial.
• Create additional durable agents for language translation.
• Fan out to multiple agents for concurrent processing.
• Fan in results and return them as structured JSON.

Prerequisites

Before you begin, you must complete the Create and run a durable agent tutorial. This tutorial extends the project created in that tutorial by adding orchestration capabilities.

Understanding the orchestration pattern

The orchestration you'll build follows this flow:

1. User input - A question or message from the user
2. Main agent - The MyDurableAgent from the first tutorial processes the question
3. Fan-out - The main agent's response is sent concurrently to both translation agents
4. Translation agents - Two specialized agents translate the response (French and Spanish)
5. Fan-in - Results are aggregated into a single JSON response with the original response and translations

This pattern enables concurrent processing, reducing total response time compared to sequential translation.

Register agents at startup

To properly use agents in durable orchestrations, register them at application startup. They can be used across orchestration executions.

::: zone pivot="programming-language-csharp"

Update your Program.cs to register the translation agents alongside the existing MyDurableAgent:

using System;
using Azure.AI.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Agents.AI.Hosting.AzureFunctions;
using Microsoft.Azure.Functions.Worker.Builder;
using Microsoft.Extensions.Hosting;
using OpenAI;
using OpenAI.Chat;

// Get the Azure OpenAI configuration
string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
    ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT")
    ?? "gpt-4o-mini";

// Create the Azure OpenAI client
AzureOpenAIClient client = new(new Uri(endpoint), new DefaultAzureCredential());
ChatClient chatClient = client.GetChatClient(deploymentName);

// Create the main agent from the first tutorial
AIAgent mainAgent = chatClient.AsAIAgent(
    instructions: "You are a helpful assistant that can answer questions and provide information.",
    name: "MyDurableAgent");

// Create translation agents
AIAgent frenchAgent = chatClient.AsAIAgent(
    instructions: "You are a translator. Translate the following text to French. Return only the translation, no explanations.",
    name: "FrenchTranslator");

AIAgent spanishAgent = chatClient.AsAIAgent(
    instructions: "You are a translator. Translate the following text to Spanish. Return only the translation, no explanations.",
    name: "SpanishTranslator");

// Build and configure the Functions host
using IHost app = FunctionsApplication
    .CreateBuilder(args)
    .ConfigureFunctionsWebApplication()
    .ConfigureDurableAgents(options =>
    {
        // Register all agents for use in orchestrations and HTTP endpoints
        options.AddAIAgent(mainAgent);
        options.AddAIAgent(frenchAgent);
        options.AddAIAgent(spanishAgent);
    })
    .Build();

app.Run();