.NET - Reorder Agents samples to start from Step01 instead of Step04 (#4110)

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

@@ -0,0 +1,68 @@
+// 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()}");