Semantic Kernel is Microsoft's enterprise-grade bridge between LLMs and existing business code.
ConceptWhat it is
Semantic Kernel is an open-source SDK from Microsoft that lets developers combine conventional code with LLM prompts as interchangeable plugins, orchestrated through planners that decide which functions to call.
It exists to meet enterprises where they already are - C#, Java, and Python codebases - giving them a first-class, supported way to add LLM capabilities without abandoning existing architecture and governance practices.
How it worksThe mechanics
Developers register native functions and prompt-based semantic functions as plugins with the kernel; a planner takes a user goal, selects and sequences the appropriate plugins, and the kernel executes them, passing outputs between steps until the goal is satisfied.
At a glanceSee it
Semantic Kernel's core trick — native code and LLM prompts share one function contract, so the planner can swap them freely.
Inside a semantic function — a template is filled with kernel arguments, sent to the model, and the free-text reply is parsed back into a typed value the pipeline can use.
When to use itWhere it fits
- Enterprises standardized on .NET or Azure wanting native LLM integration.
- Mixing deterministic business logic with LLM-driven steps in one pipeline.
- Organizations needing enterprise support and governance around AI orchestration.
- Teams building copilots embedded in existing Microsoft 365 or Azure workflows.
When NOT to use itLimits & anti-patterns
- Python-first startups with no Microsoft stack dependency, where LangChain has a larger ecosystem.
- Simple prototypes, where the plugin and planner setup is more ceremony than needed.
- Highly experimental agent research, where community momentum favors other frameworks.
Trade-offsAdvantages & costs
Advantages
- Strong enterprise support and Azure integration.
- Clean separation between native code and semantic prompt functions.
- Cross-language support including C#, Java, and Python.
- Built-in planner abstractions for multi-step orchestration.
Trade-offs & costs
- Smaller open-source community than LangChain or LlamaIndex.
- Best-fit scenarios lean toward Microsoft-centric stacks.
- Fewer third-party integrations and tutorials available.
- Planner behavior can be less predictable than hand-written control flow.
ExampleIn the real world
A bank building a Microsoft 365 Copilot extension uses Semantic Kernel to combine a native plugin that queries account balances with a semantic plugin that drafts customer-facing explanations of transactions.
ToolsHow to implement it
- Azure OpenAI Serviceprimary model backend for enterprise deployments.
- Semantic Kernel Plannersequences plugin calls to satisfy a goal.
- Azure AI Searchcommon retrieval plugin for grounding.
- .NET dependency injectionintegrates the kernel into existing enterprise apps.
Cost & effortWhat it takes
SDK is free and open-source; costs come from Azure OpenAI or other model usage plus Azure infrastructure. Moderate engineering effort, lower for teams already on the Microsoft stack.