Introduction

AI is now used in almost every modern application. Earlier, AI was mainly limited to chatbots, but today organizations expect features such as summarization, document search, automated workflows, and AI agents that can safely call APIs.

For developers, the challenge is not just calling an AI model. The real challenges are:

This is where Semantic Kernel becomes useful.

What Is Semantic Kernel?

Semantic Kernel (SK) is a lightweight, open-source SDK from Microsoft that helps developers build AI-powered applications and agents using C#, Python, or Java.

Semantic Kernel acts as a middleware layer between:

Instead of writing custom glue code, Semantic Kernel provides a structured approach to managing prompts, functions, plugins, and memory.

Why Is Semantic Kernel Useful?

Semantic Kernel is designed to be:

Future-Proof

AI models evolve rapidly. Semantic Kernel allows you to switch or upgrade models without rewriting the entire application.

Enterprise-Friendly

Real-world systems require logging, monitoring, safety controls, and modular design. Semantic Kernel supports these needs out of the box.

Developer-Friendly

For .NET developers, Semantic Kernel feels familiar because it follows structured patterns similar to Dependency Injection.

What Problem Does Semantic Kernel Solve?

AI integration is often misunderstood as simply sending a prompt and receiving a response. In real applications, AI needs to perform actions such as:

Semantic Kernel allows you to expose existing application logic as callable functions. The AI model can then invoke these functions when needed, enabling the creation of real AI assistants rather than simple chatbots.

Understanding the Kernel

The Kernel is the core component of Semantic Kernel. It acts as the central brain of the application and manages:

All prompts and function calls flow through the kernel, giving developers a single place to configure and observe AI behavior.

How Semantic Kernel Works Internally

When a prompt is executed, Semantic Kernel performs several steps behind the scenes:

When plugins are involved, the kernel can allow the AI model to call application functions and retrieve real data.

Main Components of Semantic Kernel

1. AI Service Connectors

AI connectors link Semantic Kernel with AI providers such as:

Supported AI services include:

Most applications primarily use chat completion and embeddings.

2. Plugins and Functions

Plugins group related functions that can be exposed to the AI model.

Example:

Once registered, these functions can be called by the AI model through Semantic Kernel.

3. Prompt Templates

Prompt templates are reusable and structured prompts that include:

They help keep AI responses consistent and controlled.

4. Vector Store (Memory)

Semantic Kernel integrates with vector databases to support:

5. Filters

Filters enable monitoring and safety controls, such as:

Installing Semantic Kernel

dotnet add package Microsoft.SemanticKernel

Creating a Kernel and Adding an AI Model

using Microsoft.SemanticKernel;

var builder = Kernel.CreateBuilder();

builder.AddOpenAIChatCompletion(
    modelId: "gpt-4o-mini",
    apiKey: "YOUR_API_KEY"
);

var kernel = builder.Build();

Running a Simple Prompt

var prompt = """
You are a helpful assistant.

Explain Semantic Kernel in simple words.
""";

var result = await kernel.InvokePromptAsync(prompt);
Console.WriteLine(result);

Plugin Example (Real Use Case)

Creating an Order Plugin

using Microsoft.SemanticKernel;

public class OrderPlugin
{
    [KernelFunction]
    public string GetOrderStatus(int orderId)
    {
        return $"Order {orderId} is Shipped.";
    }
}

Registering the Plugin

kernel.Plugins.AddFromObject(new OrderPlugin(), "OrderPlugin");

Using the Plugin in a Prompt

var prompt = """
User asked: What is the status of order 101?

Use OrderPlugin.GetOrderStatus to get the status.
""";

var result = await kernel.InvokePromptAsync(prompt);
Console.WriteLine(result);

In this flow:

This is how Semantic Kernel enables AI agents to work with real application logic.

Conclusion

Semantic Kernel is a lightweight and powerful SDK for integrating AI into .NET applications. It goes beyond simple prompt execution by supporting:

For developers building AI-powered applications in C#, Semantic Kernel provides a structured and scalable foundation.