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// Copyright (c) Microsoft. All rights reserved.
// This sample demonstrates how to integrate AI agents into a workflow pipeline.
// Three translation agents are connected sequentially to create a translation chain:
// English → French → Spanish → English, showing how agents can be composed as workflow executors.
using Azure.AI.AgentServer.AgentFramework.Extensions;
using Azure.AI.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Agents.AI.Workflows;
using Microsoft.Extensions.AI;
// 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";
IChatClient chatClient = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
.GetChatClient(deploymentName)
.AsIChatClient();
// Create agents
AIAgent frenchAgent = GetTranslationAgent("French", chatClient);
AIAgent spanishAgent = GetTranslationAgent("Spanish", chatClient);
AIAgent englishAgent = GetTranslationAgent("English", chatClient);
// Build the workflow and turn it into an agent
AIAgent agent = new WorkflowBuilder(frenchAgent)
.AddEdge(frenchAgent, spanishAgent)
.AddEdge(spanishAgent, englishAgent)
.Build()
.AsAgent();
await agent.RunAIAgentAsync();
static ChatClientAgent GetTranslationAgent(string targetLanguage, IChatClient chatClient) =>
new(chatClient, $"You are a translation assistant that translates the provided text to {targetLanguage}.");