What is Semantic Kernel and how does it help build AI agents in .NET?

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:

Connecting AI with existing application code
Calling business functions safely
Using memory (Vector Databases) for RAG scenarios
Managing everything in a structured and maintainable way

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:

Your application code
AI models (such as OpenAI or Azure OpenAI)
Existing APIs and business logic

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:

Fetching order status from a database
Calling payment APIs
Searching documents
Creating support tickets
Triggering email notifications

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:

AI model connectors
Plugins and functions
Memory connectors (vector databases)
Filters for logging, monitoring, and safety

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:

Selects the configured AI model
Prepares and formats the prompt
Sends the prompt to the model
Receives the response
Returns the processed output to the application

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:

OpenAI
Azure OpenAI
Hugging Face (limited scenarios)

Supported AI services include:

Chat completion
Text generation
Embeddings
Image generation
Audio support

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:

OrderPlugin
- GetOrderStatus
- CreateOrder
EmailPlugin
- SendEmail

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:

Instructions
Placeholders
User input
Function output

They help keep AI responses consistent and controlled.

4. Vector Store (Memory)

Semantic Kernel integrates with vector databases to support:

Semantic search
RAG (Retrieval-Augmented Generation)
Chat-with-documents scenarios

5. Filters

Filters enable monitoring and safety controls, such as:

Logging prompts and responses
Tracking function calls
Blocking unsafe requests
Measuring token usage and cost

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:

The AI model reads the prompt
Determines that order status is required
Calls the OrderPlugin function
Semantic Kernel executes the function
The result is passed back to the model
The model generates the final response

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:

Plugins and functions
Vector memory for RAG scenarios
Prompt templates
Filters for monitoring and safety

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