Introduction to Semantic Kernel

Semantic Kernel is an open-source SDK designed to help developers integrate AI capabilities into existing applications without rebuilding them from scratch. Instead of wrapping our app around a large language model, Semantic Kernel lets our code call and orchestrate AI services cleanly, reliably, and with minimal friction.

What is Semantic Kernel?

At its core, Semantic Kernel lets us continue writing in our usual programming language (C#, Python, Java, etc.) while injecting prompts, semantic functions, and AI-driven logic where needed. It’s built on the same Copilot stack Microsoft uses for products like Microsoft 365 Copilot, Windows Copilot, and Bing, and it focuses on simplifying prompt engineering and orchestration so you can build production-ready AI behaviors inside your application.

Who is it for?

Semantic Kernel is primarily for software developers and architects who want to extend existing systems with AI. If you maintain applications written in C#, Python, or Java and need your software to do more than just generate text (for example: act, automate, or interact with other systems), Semantic Kernel provides a structured way to do that.

Why use Semantic Kernel?

Even though we have systems like ChatGPT, Semantic Kernel is useful because it easily adds existing code to AI agents. Agents can interact with real-world applications through plugins, and Semantic Kernel helps create fully automated agents. AI models alone generate text/images but don’t make end-to-end apps; Semantic Kernel bridges this gap to build autonomous workflows. It also offers flexible integration with AI services via connectors and plugins, you can easily switch between models (e.g., OpenAI, Azure OpenAI) or combine them as needed, making it simple to extend your current systems with practical, maintainable AI.

Key components of Semantic Kernel

• Kernel

The core engine where we register connectors and plugins, configure settings, and enable logging and telemetry.

• Memory

Provides conversational/contextual memory. Plugins and agents can recall past interactions. Memory can be implemented in several ways:

1. Key-value pairs
2. Local file system
3. Semantic memory using embeddings

• Planner

Translates a prompt into an execution plan. Different planner types include:

1. Basic Planner: simple chaining of functions
2. Sequential Planner: complex interconnections between functions
3. Action Planner: single-function execution
4. Stepwise Planner: step-by-step execution where each step depends on the previous output

• Connectors

Bridge between our application and AI services. A large set of predefined connectors is available to make switching or combining services easier.

• Plugins (Skills)

Collections of functions (native and AI-backed). Semantic Kernel supports semantic functions and native functions.

1. C# offers first-class support for writing plugins.
2. Python is heavily supported too, and you can combine C# and Python in the same Semantic Kernel solution.

Example use case (concise)

Imagine an agent that processes an incoming support request: it consults memory for past tickets, uses a planner to decide actions, calls a native function to query a database, and then uses an LLM to draft a response all coordinated by Semantic Kernel. Similarly, it can be used to orchestrate IoT actions (e.g., toggling a device) by wiring semantic functions to native control APIs.

Conclusion

Semantic Kernel bridges the gap between powerful language models and real-world applications. It brings structure: memory, planners, connectors, and skills to AI integration, enabling developers to create reliable, maintainable, and automated workflows without discarding existing code.

If you find this useful, let me know which areas you’d like a deeper dive on, and I’ll prepare a follow-up.