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<Project Sdk="Microsoft.NET.Sdk">
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<PropertyGroup>
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<OutputType>Exe</OutputType>
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<TargetFrameworks>net10.0</TargetFrameworks>
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<Nullable>enable</Nullable>
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<ImplicitUsings>enable</ImplicitUsings>
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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="Azure.AI.Projects" />
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<PackageReference Include="Azure.Identity" />
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</ItemGroup>
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<ItemGroup>
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<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
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</ItemGroup>
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</Project>
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// Copyright (c) Microsoft. All rights reserved.
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// This sample demonstrates how to use an agent with function tools that require a human in the loop for approvals.
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// It shows both non-streaming and streaming agent interactions using weather-related tools.
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// If the agent is hosted in a service, with a remote user, combine this sample with the Persisted Conversations sample to persist the chat history
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// while the agent is waiting for user input.
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using System.ComponentModel;
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using Azure.AI.Projects;
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using Azure.Identity;
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using Microsoft.Agents.AI;
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using Microsoft.Extensions.AI;
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string endpoint = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_FOUNDRY_PROJECT_ENDPOINT is not set.");
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string deploymentName = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
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// Create a sample function tool that the agent can use.
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[Description("Get the weather for a given location.")]
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static string GetWeather([Description("The location to get the weather for.")] string location)
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=> $"The weather in {location} is cloudy with a high of 15°C.";
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const string AssistantInstructions = "You are a helpful assistant that can get weather information.";
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const string AssistantName = "WeatherAssistant";
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// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
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AIProjectClient aiProjectClient = new(new Uri(endpoint), new AzureCliCredential());
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ApprovalRequiredAIFunction approvalTool = new(AIFunctionFactory.Create(GetWeather, name: nameof(GetWeather)));
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// Create AIAgent directly
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AIAgent agent = await aiProjectClient.CreateAIAgentAsync(name: AssistantName, model: deploymentName, instructions: AssistantInstructions, tools: [approvalTool]);
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// Call the agent with approval-required function tools.
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// The agent will request approval before invoking the function.
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AgentThread thread = await agent.GetNewThreadAsync();
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AgentResponse response = await agent.RunAsync("What is the weather like in Amsterdam?", thread);
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// Check if there are any user input requests (approvals needed).
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List<UserInputRequestContent> userInputRequests = response.UserInputRequests.ToList();
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while (userInputRequests.Count > 0)
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{
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// Ask the user to approve each function call request.
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// For simplicity, we are assuming here that only function approval requests are being made.
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List<ChatMessage> userInputMessages = userInputRequests
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.OfType<FunctionApprovalRequestContent>()
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.Select(functionApprovalRequest =>
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{
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Console.WriteLine($"The agent would like to invoke the following function, please reply Y to approve: Name {functionApprovalRequest.FunctionCall.Name}");
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bool approved = Console.ReadLine()?.Equals("Y", StringComparison.OrdinalIgnoreCase) ?? false;
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return new ChatMessage(ChatRole.User, [functionApprovalRequest.CreateResponse(approved)]);
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})
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.ToList();
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// Pass the user input responses back to the agent for further processing.
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response = await agent.RunAsync(userInputMessages, thread);
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userInputRequests = response.UserInputRequests.ToList();
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}
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Console.WriteLine($"\nAgent: {response}");
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// Cleanup by agent name removes the agent version created.
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await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
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# Using Function Tools with Approvals (Human-in-the-Loop)
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This sample demonstrates how to use function tools that require human approval before execution, implementing a human-in-the-loop workflow.
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## What this sample demonstrates
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- Creating approval-required function tools using ApprovalRequiredAIFunction
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- Handling user input requests for function approvals
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- Implementing human-in-the-loop approval workflows
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- Processing agent responses with pending approvals
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- Managing agent lifecycle (creation and deletion)
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## Prerequisites
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Before you begin, ensure you have the following prerequisites:
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- .NET 10 SDK or later
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- Azure Foundry service endpoint and deployment configured
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- Azure CLI installed and authenticated (for Azure credential authentication)
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**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
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Set the following environment variables:
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```powershell
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$env:AZURE_FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint
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$env:AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME="gpt-4o-mini" # Optional, defaults to gpt-4o-mini
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```
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## Run the sample
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Navigate to the FoundryAgents sample directory and run:
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```powershell
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cd dotnet/samples/GettingStarted/FoundryAgents
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dotnet run --project .\FoundryAgents_Step04_UsingFunctionToolsWithApprovals
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```
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## Expected behavior
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The sample will:
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1. Create an agent named "WeatherAssistant" with an approval-required GetWeather function tool
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2. Run the agent with a prompt asking about weather
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3. The agent will request approval before invoking the GetWeather function
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4. The sample will prompt the user to approve or deny the function call (enter 'Y' to approve)
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5. After approval, the function will be executed and the result returned to the agent
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6. Clean up resources by deleting the agent
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**Note**: For hosted agents with remote users, combine this sample with the Persisted Conversations sample to persist chat history while waiting for user approval.
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