Introduction to Semantic Kernel: AI-powered agents for your apps

What is Semantic Kernel?

Semantic Kernel is a lightweight and modular open-source SDK from Microsoft that helps developers integrate large language models (LLMs) into their applications in a seamless and extensible way. By acting as a bridge between your application logic and the power of modern AI services like OpenAI and Azure OpenAI, it lets you build intelligent agents that understand context, plan actions, and execute real-world functions.

Why Use Semantic Kernel?

While many SDKs enable chat or text generation, Semantic Kernel goes further by offering a framework to define and organise your app's capabilities as plugins, which the AI can invoke. It’s designed with enterprise needs in mind—telemetry, logging, and modular design come built-in. It supports automatic function calling, multi-turn dialogue, and integration with tools like Microsoft 365 Copilot.

Core Components Explained

• Kernel: The container managing services and plugins. It acts as the central orchestrator for your AI agent.
• Connectors: Interfaces to AI services such as OpenAI, Azure OpenAI, or Hugging Face.
• Plugins: Your app logic, APIs, or business functions exposed to the AI agent for action.
• Prompt Templates: Predefined text templates used to generate consistent prompts for the AI models.
• Native Functions: Application-level logic that can be invoked by the AI using function calling.

Function Calling and Plugins

One of the most impressive features is the automatic function calling. You describe your function with attributes, and when the AI recognises that a user’s intent maps to that function, it automatically invokes it with the appropriate parameters. This allows for intelligent action, not just conversation.

Getting Started in .NET

Here is a brief setup guide for C# developers:

dotnet new console -n SemanticKernelDemo
        cd SemanticKernelDemo
        dotnet add package Microsoft.SemanticKernel
        

Then define your kernel and chat service like so:

using Microsoft.SemanticKernel;
        var builder = Kernel.CreateBuilder();
        builder.AddAzureOpenAIChatCompletion("model", "endpoint", "apiKey");
        Kernel kernel = builder.Build();
        

Real-World Use Cases

Developers are using Semantic Kernel in exciting ways:

• Automated email drafting with attachments and scheduling.
• Customer service bots that not only respond but trigger actions (like order status retrieval).
• Internal business process automation, e.g., expense report generation.
• IoT control via AI—turning on lights, checking sensors through plugins.

Advanced Features

Planning: The SDK supports plan generation, where the AI outlines a multi-step execution path (e.g., "Get user data, then send a report"). This is especially powerful when combined with memory or context-aware functions.

Observability: You can monitor all interactions using built-in logging and telemetry. Hooks and filters allow developers to execute pre/post logic around prompts or function calls, enabling advanced auditing and compliance support.

Supported Languages and Extensibility

Semantic Kernel is available in C#, Python, and Java, and it includes a rich plugin architecture. You can build plugins from:

• Native classes with annotated methods
• Prompt templates defined in YAML or Prompty format
• OpenAPI specifications for REST APIs

Community and Resources

Semantic Kernel is actively developed and has an open-source community. You can find learning resources including sample notebooks, official guides, and detailed examples on GitHub:

https://github.com/microsoft/semantic-kernel

Agentic AI with Semantic Kernel

Agentic AI refers to artificial intelligence systems that can act autonomously to achieve user goals. Instead of merely responding to prompts, these systems assess context, formulate a plan, and take initiative to complete tasks. Semantic Kernel is purpose-built for such scenarios.

By combining planning capabilities, function calling, plugin extensibility, and memory integration, Semantic Kernel enables developers to construct agents that go beyond chat. For example, an agent can interpret a request like “generate a report from yesterday’s sales and email it to my manager” by breaking it down into steps and executing them without further input.

These kinds of autonomous, reasoning-driven agents are the future of enterprise software, and Semantic Kernel provides all the foundational components to build them today.

Final Thoughts

Semantic Kernel isn’t just another AI SDK—it’s a strategic foundation for building intelligent, modular, and actionable agents. Whether you’re prototyping, building internal tools, or deploying full-scale AI solutions, it’s an essential addition to your AI toolbox.