This article describes how to use contemporary APIs (OpenAI, Azure OpenAI, Hugging Face, and Self-hosted LLMs) to integrate AI agents with ASP.NET MVC.

What Is an AI Agent?

• An AI Agent is an autonomous component capable of:
• Understanding user input
• Taking decisions
• Calling tools (APIs, DB, services)
• Updating its memory
• Producing actions or responses
• Triggering workflows

In an MVC project, an AI Agent often acts as:

• Chatbot
• Automated email writer
• Code generator
• Ticket classification bot
• Data extraction worker
• Knowledge base assistant

Project Structure (MVC)
Your MVC app will use:
Controllers/
    AiAgentController.cs
Services/
    AiAgentService.cs
Models/
    AiRequest.cs
    AiResponse.cs
Views/
    AiAgent/
        Index.cshtml

Step 1: Install Required Nuget Packages
For OpenAI-compatible agents:
Install-Package OpenAI
Install-Package Newtonsoft.Json

Step 2: Create Your AI Agent Service (Backend Logic)
Create: Services/AiAgentService.cs
using OpenAI.Chat;
using OpenAI;
using System.Threading.Tasks;

namespace YourApp.Services
{
    public class AiAgentService
    {
        private readonly OpenAIClient _client;

        public AiAgentService(string apiKey)
        {
            _client = new OpenAIClient(apiKey);
        }

        public async Task<string> AskAgentAsync(string userInput)
        {
            var chat = _client.GetChatClient("gpt-4o-mini");

            var response = await chat.CompleteAsync(
                userInput
            );

            return response.Content[0].Text;
        }
    }
}

Step 3: Add the Service to Dependency Injection
Open Global.asax.cs (or Program.cs for .NET 6+ MVC)
For .NET 4.8 MVC
In UnityConfig.cs or Autofac:
container.RegisterType<AiAgentService>(
    new InjectionConstructor("YOUR_OPENAI_API_KEY")
);

For .NET 6/7 MVC
Program.cs
builder.Services.AddSingleton<AiAgentService>(new AiAgentService("YOUR_API_KEY"));

Step 4: Create Controller to Call the AI Agent
Controllers/AiAgentController.cs

using System.Threading.Tasks;
using System.Web.Mvc;
using YourApp.Services;

namespace YourApp.Controllers
{
    public class AiAgentController : Controller
    {
        private readonly AiAgentService _ai;

        public AiAgentController(AiAgentService ai)
        {
            _ai = ai;
        }

        [HttpGet]
        public ActionResult Index()
        {
            return View();
        }

        [HttpPost]
        public async Task<ActionResult> Index(string userMessage)
        {
            var aiResponse = await _ai.AskAgentAsync(userMessage);
            ViewBag.Response = aiResponse;
            return View();
        }
    }
}

Step 5: Create Razor View for Chat UI
Views/AiAgent/Index.cshtml
@{
    ViewBag.Title = "AI Agent Chat";
}

<h2>AI Agent in MVC</h2>

<form method="post">
    <textarea name="userMessage" class="form-control" rows="4" placeholder="Ask anything..."></textarea>
    <br />
    <button class="btn btn-primary">Send</button>
</form>

@if (ViewBag.Response != null)
{
    <div class="alert alert-info" style="margin-top:20px;">
        <strong>AI Agent Reply:</strong>
        <p>@ViewBag.Response</p>
    </div>
}

Your First AI Agent Is Ready
You can now run:
/AiAgent/Index

Type a message:
    “Summarize this text”

    “Generate email template for refund request”

    “Write C# code for a stored procedure call”

    “Fix my SQL query”

The agent instantly responds.

Advanced: Add Tools (Function Calling)
Agents become powerful when they can call functions inside your MVC app.
Example: Agent gets order status from your database.

Step 1: Add a Tool Method
public string GetOrderStatus(int orderId)
{
    return "Order " + orderId + " is in Packaging Stage.";
}

Step 2: Expose Tool to Agent
Most AI SDKs support function-calling like:
var response = await chat.CompleteAsync(
    messages: userInput,
    functions: new[]
    {
        new FunctionDefinition(
            "get_order_status",
            "Get order status using order ID",
            new { orderId = "number" }
        )
    });

Result:
Agent decides to call your function → Your C# method runs → Response returned back.

Real-World AI Agent Use Cases in MVC
1. Customer Support Assistant
Automatically understands user message → replies or creates ticket.

2. Form Auto-Generation
User describes what form they need → agent builds HTML form dynamically.

3. Code Generator Inside Admin Panel
Generate C# classes, views, DB queries on the fly.

4. Workflow Automation
User enters command → agent runs server-side tasks.

5. Knowledge Base Search Agent
AI agent + vector database → semantic search.

Advanced: Adding Memory to AI Agent
Short-term memory → history stored in session
Long-term memory → store in DB or vector DB (like Qdrant, Pinecone)

Session["history"] += userMessage + aiResponse;

Performance Tips & Best Practices

• Cache frequently used prompts

Use IMemoryCache or Redis

• Avoid sending huge previous chat

Compress or summarize conversation

• Always use streaming for faster response

Most SDKs support streaming tokens

• Background agents for heavy tasks

Use IHostedService or Windows Service

Common Mistakes Developers Make

Final Thoughts
AI Agents will become a core part of all ASP.NET MVC applications.
With just a few steps, you can:

• Add smart chatbots
• Automate workflows
• Enhance admin panels
• Add dynamic intelligence
• Build modern AI-driven enterprise apps