agent-framework/dotnet/samples/02-agents/Agents/Agent_Step01_UsingFunctionToolsWithApprovals/Program.cs

// Copyright (c) Microsoft. All rights reserved.

// This sample demonstrates how to use a ChatClientAgent with function tools that require a human in the loop for approvals.
// It shows both non-streaming and streaming agent interactions using menu-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.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Extensions.AI;
using OpenAI.Chat;
using ChatMessage = Microsoft.Extensions.AI.ChatMessage;

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";

// 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.";

// Create the chat client and agent.
// Note that we are wrapping the function tool with ApprovalRequiredAIFunction to require user approval before invoking it.
// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
AIAgent agent = new AzureOpenAIClient(
    new Uri(endpoint),
    new DefaultAzureCredential())
    .GetChatClient(deploymentName)
    .AsAIAgent(instructions: "You are a helpful assistant", tools: [new ApprovalRequiredAIFunction(AIFunctionFactory.Create(GetWeather))]);

// Call the agent and check if there are any function approval requests to handle.
// For simplicity, we are assuming here that only function approvals are pending.
AgentSession session = await agent.CreateSessionAsync();
AgentResponse response = await agent.RunAsync("What is the weather like in Amsterdam?", session);
List<FunctionApprovalRequestContent> approvalRequests = response.Messages.SelectMany(m => m.Contents).OfType<FunctionApprovalRequestContent>().ToList();

// For streaming use:
// var updates = await agent.RunStreamingAsync("What is the weather like in Amsterdam?", session).ToListAsync();
// approvalRequests = updates.SelectMany(x => x.Contents).OfType<FunctionApprovalRequestContent>().ToList();

while (approvalRequests.Count > 0)
{
    // Ask the user to approve each function call request.
    List<ChatMessage> userInputResponses = approvalRequests
        .ConvertAll(functionApprovalRequest =>
        {
            Console.WriteLine($"The agent would like to invoke the following function, please reply Y to approve: Name {functionApprovalRequest.FunctionCall.Name}");
            return new ChatMessage(ChatRole.User, [functionApprovalRequest.CreateResponse(Console.ReadLine()?.Equals("Y", StringComparison.OrdinalIgnoreCase) ?? false)]);
        });

    // Pass the user input responses back to the agent for further processing.
    response = await agent.RunAsync(userInputResponses, session);

    approvalRequests = response.Messages.SelectMany(m => m.Contents).OfType<FunctionApprovalRequestContent>().ToList();

    // For streaming use:
    // updates = await agent.RunStreamingAsync(userInputResponses, session).ToListAsync();
    // approvalRequests = updates.SelectMany(x => x.Contents).OfType<FunctionApprovalRequestContent>().ToList();
}

Console.WriteLine($"\nAgent: {response}");

// For streaming use:
// Console.WriteLine($"\nAgent: {updates.ToAgentResponse()}");