Semantic Kernel

Why It Matters

Semantic Kernel represents Microsoft’s approach to AI application development. Unlike Python-first frameworks like LangChain, Semantic Kernel prioritizes C# and enterprise patterns: strong typing, dependency injection, and plugin architectures familiar to .NET developers.

The framework shines in enterprise environments already invested in Microsoft’s stack. Native Azure OpenAI integration, seamless compatibility with existing .NET codebases, and a plugin model that wraps existing business logic make it practical for organizations that can’t adopt Python-based solutions.

For AI engineers, Semantic Kernel matters as an example of enterprise-grade AI framework design. Its concepts (skills, memory, planners) influenced how other frameworks evolved. Understanding Semantic Kernel helps when building for or consulting with enterprises that prefer Microsoft’s ecosystem.

Implementation Basics

Semantic Kernel uses several core abstractions:

Plugins (formerly Skills) encapsulate functionality the AI can use. A plugin might contain functions for email sending, database queries, or calculations. Each function has descriptions that help the AI decide when to use it.

Planners orchestrate multi-step tasks. When given a goal, a planner creates a sequence of plugin calls to achieve it. Different planner types handle different complexity levels, from simple sequential plans to dynamic, branching execution.

Memory provides semantic recall across conversations. Semantic Kernel can store and retrieve information using vector embeddings, maintaining context across sessions.

Connectors link to AI services. Azure OpenAI and OpenAI are first-class citizens, with support for other providers.

Start by wrapping your existing code as plugins. This is where Semantic Kernel excels. Define clear function descriptions so the AI can select them appropriately. Use the handlebars planner for simple orchestration, graduating to more complex planners as your use cases demand it.