How to Build AI Support Agents with Microsoft Agent Framework in .NET 10

Introduction

AI development in .NET is evolving fast. For years, developers used libraries like Semantic Kernel and AutoGen to build AI-powered assistants and multi-agent systems. But now Microsoft has introduced something much bigger: "Microsoft Agent Framework"

This new framework unifies the best parts of AutoGen’s multi-agent orchestration and Semantic Kernel’s enterprise-ready capabilities into a single SDK.

With .NET 10, it becomes even more exciting because we can now build:

intelligent copilots
autonomous workflow agents
multi-step reasoning pipelines
tool-using enterprise assistants
MCP-powered business workflows

In this article, we’ll build a Customer Support AI Agent using Microsoft Agent Framework in .NET 10.

Why Microsoft Agent Framework?

The framework provides two major building blocks:

Agents → intelligent LLM-based assistants
Workflows → graph-based orchestration for multi-step processes

This means you can start with a simple chatbot and later evolve it into:

approval workflow
document processor
customer support pipeline
billing automation
DevOps incident triage system

without rewriting everything.

Real-World Example

Now, let’s take an example of customer support ticket agent. Imagine your SaaS platform receives customer issues like: “My payment failed but money was deducted.” . Instead of routing this manually, the AI agent can:

Understand the issue
Categorize ticket severity
Query customer data
Suggest resolution
Escalate when needed
Save conversation history

This is where Microsoft Agent Framework shines.

Customer Support Ticket Agent Demo

Now let’s go for demo of Customer Support Ticket Agent in .NET using Micorsoft Agent framework.

Step 1: Create .NET 10 Project

dotnet new console -n SupportAgentDemo
cd SupportAgentDemo

Install package:

dotnet add package Microsoft.Agents.AI --prerelease

The framework officially supports .NET as one of its primary SDKs.

Step 2: Build the Support Agent

Add folowing code to the Program.cs

using Microsoft.Extensions.DependencyInjection;
using Microsoft.Extensions.Hosting;
using Microsoft.Agents;
using Microsoft.Agents.AI;
using Microsoft.Agents.AI.OpenAI;

var builder = Host.CreateApplicationBuilder(args);

builder.Services.AddAgentFramework();

var app = builder.Build();

var agent = new ChatAgent(
    name: "SupportAgent",
    instructions: """
        You are a customer support assistant.
        Classify issue severity, suggest solution steps,
        and escalate billing issues immediately.
    """);

Console.WriteLine("Customer Support Agent Started");
Console.WriteLine("--------------------------------");

while (true)
{
    Console.Write("Customer: ");
    var input = Console.ReadLine();

    if (string.IsNullOrWhiteSpace(input))
        break;

    var result = await agent.RunAsync(input);

    Console.WriteLine($"Agent: {result}");
}

Step 3: Add Tool Support

Now let’s make it smarter by giving it a tool.

Let’s add class TicketLookupTool.cs and the following code in this class.

public static class TicketLookupTool
{
    public static string GetCustomerStatus(string customerId)
    {
        return customerId switch
        {
            "CUST-100" => "Premium customer with payment issue history",
            "CUST-200" => "Trial customer",
            _ => "Unknown customer"
        };
    }
}

Now let’s register Tool in Agent

var agent = new ChatAgent(
    name: "SupportAgent",
    instructions: """
        You are a support assistant.
        Use tools when customer account lookup is needed.
    """,
    tools: [TicketLookupTool.GetCustomerStatus]);

Now the AI can call real .NET methods as tools.

This is ideal for:

CRM access
payment status
Azure monitoring
order lookup
inventory status

Step 4: Add Workflow for Escalation

This is where Agent Framework becomes more powerful than a normal chatbot.

Let’s create a workflow like below:

var workflow = new WorkflowBuilder<string, string>()
    .AddStep("Analyze", async issue =>
    {
        return issue.Contains("payment", StringComparison.OrdinalIgnoreCase)
            ? "Escalate to Billing"
            : "Handle in Support";
    })
    .Build();

Now the system can orchestrate business flows using graph-based execution.

Best Use Cases in Enterprise .NET Apps

This framework is perfect for:

1) Customer Support Automation

AI ticket triage and routing.

2) DevOps Incident Response

AI agents can analyze Azure logs and raise incidents.

3) Finance Approval Systems

Human-in-the-loop approval workflows.

4) HR Assistants

Policy Q&A, leave approvals, onboarding.

5) MCP-Based Business Tools

Integrate internal APIs and external systems safely.

Why .NET Developers Should Learn This in 2026

Microsoft Agent Framework is becoming the future direction for agentic AI in .NET, merging years of learnings from AutoGen and Semantic Kernel.

If you already know:

ASP.NET Core
Clean Architecture
MediatR
Azure Functions
Semantic Kernel

then this framework will feel very natural.

The biggest advantage is that your AI logic becomes: "structured, testable, orchestrated, and production-ready" instead of scattered prompt code.

Conclusion

Microsoft Agent Framework is shaping the future of agentic AI development in the .NET ecosystem. With a unified approach to agents, workflows, tool calling, memory, and orchestration, it gives .NET developers a clean and scalable way to build intelligent business applications using familiar C# patterns.

In this article, we explored how to build a Customer Support AI Agent in .NET 10, integrate tool-based account lookups, and design a simple escalation workflow that reflects real enterprise scenarios.

The biggest advantage of this framework is that it moves AI development beyond simple prompt-based chatbots and into structured, production-ready workflows that can support customer service, DevOps, finance, HR, and automation use cases. For developers already working with ASP.NET Core, Azure, Clean Architecture, and Semantic Kernel, Microsoft Agent Framework feels like a natural next step toward building enterprise-grade autonomous systems.

As AI agents continue to become a core part of modern software platforms, learning this framework now will help .NET developers stay ahead in building the next generation of smart, workflow-driven applications.

The future of .NET is no longer just APIs and microservices, it is intelligent agents working alongside your business workflows.

Example code sample can be found in the repository here.