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<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Azure.AI.Projects" />
<PackageReference Include="Azure.Identity" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
</ItemGroup>
</Project>

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// Copyright (c) Microsoft. All rights reserved.
// This sample demonstrates how to use an agent with function tools that require a human in the loop for approvals.
// It shows both non-streaming and streaming agent interactions using weather-related tools.
// 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
// while the agent is waiting for user input.
using System.ComponentModel;
using Azure.AI.Projects;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Extensions.AI;
string endpoint = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_FOUNDRY_PROJECT_ENDPOINT is not set.");
string deploymentName = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
// Create a sample function tool that the agent can use.
[Description("Get the weather for a given location.")]
static string GetWeather([Description("The location to get the weather for.")] string location)
=> $"The weather in {location} is cloudy with a high of 15°C.";
const string AssistantInstructions = "You are a helpful assistant that can get weather information.";
const string AssistantName = "WeatherAssistant";
// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
AIProjectClient aiProjectClient = new(new Uri(endpoint), new AzureCliCredential());
ApprovalRequiredAIFunction approvalTool = new(AIFunctionFactory.Create(GetWeather, name: nameof(GetWeather)));
// Create AIAgent directly
AIAgent agent = await aiProjectClient.CreateAIAgentAsync(name: AssistantName, model: deploymentName, instructions: AssistantInstructions, tools: [approvalTool]);
// Call the agent with approval-required function tools.
// The agent will request approval before invoking the function.
AgentThread thread = await agent.GetNewThreadAsync();
AgentResponse response = await agent.RunAsync("What is the weather like in Amsterdam?", thread);
// Check if there are any user input requests (approvals needed).
List<UserInputRequestContent> userInputRequests = response.UserInputRequests.ToList();
while (userInputRequests.Count > 0)
{
// Ask the user to approve each function call request.
// For simplicity, we are assuming here that only function approval requests are being made.
List<ChatMessage> userInputMessages = userInputRequests
.OfType<FunctionApprovalRequestContent>()
.Select(functionApprovalRequest =>
{
Console.WriteLine($"The agent would like to invoke the following function, please reply Y to approve: Name {functionApprovalRequest.FunctionCall.Name}");
bool approved = Console.ReadLine()?.Equals("Y", StringComparison.OrdinalIgnoreCase) ?? false;
return new ChatMessage(ChatRole.User, [functionApprovalRequest.CreateResponse(approved)]);
})
.ToList();
// Pass the user input responses back to the agent for further processing.
response = await agent.RunAsync(userInputMessages, thread);
userInputRequests = response.UserInputRequests.ToList();
}
Console.WriteLine($"\nAgent: {response}");
// Cleanup by agent name removes the agent version created.
await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);

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# Using Function Tools with Approvals (Human-in-the-Loop)
This sample demonstrates how to use function tools that require human approval before execution, implementing a human-in-the-loop workflow.
## What this sample demonstrates
- Creating approval-required function tools using ApprovalRequiredAIFunction
- Handling user input requests for function approvals
- Implementing human-in-the-loop approval workflows
- Processing agent responses with pending approvals
- Managing agent lifecycle (creation and deletion)
## Prerequisites
Before you begin, ensure you have the following prerequisites:
- .NET 10 SDK or later
- Azure Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (for Azure credential authentication)
**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).
Set the following environment variables:
```powershell
$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
$env:AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME="gpt-4o-mini" # Optional, defaults to gpt-4o-mini
```
## Run the sample
Navigate to the FoundryAgents sample directory and run:
```powershell
cd dotnet/samples/GettingStarted/FoundryAgents
dotnet run --project .\FoundryAgents_Step04_UsingFunctionToolsWithApprovals
```
## Expected behavior
The sample will:
1. Create an agent named "WeatherAssistant" with an approval-required GetWeather function tool
2. Run the agent with a prompt asking about weather
3. The agent will request approval before invoking the GetWeather function
4. The sample will prompt the user to approve or deny the function call (enter 'Y' to approve)
5. After approval, the function will be executed and the result returned to the agent
6. Clean up resources by deleting the agent
**Note**: For hosted agents with remote users, combine this sample with the Persisted Conversations sample to persist chat history while waiting for user approval.