Semantic Kernel is the code layer Microsoft maintains underneath its own AI products: understanding how it works explains a lot about how Copilot Studio and AI Foundry Agent Service behave under the hood.
Semantic Kernel is an open-source SDK (available in C#, Python, and Java) that Microsoft actively develops as its reference AI orchestration layer, with first-class support for Azure OpenAI and other providers' models alike. Unlike LangChain, which prioritizes broad integration coverage and fast experimentation, Semantic Kernel prioritizes API stability for enterprise production environments, with strict semantic versioning and multi-language compatibility designed for teams that already have codebases in .NET or Java, not just Python.
The Kernel object is the central composition point: it registers available AI services (one or several models, potentially from different providers), loaded plugins, and memory or vector store services. Any invoked function passes through the kernel, which lets you apply cross-cutting filters (logging, cost control, content moderation) in one place without touching each individual function.
A plugin groups semantic functions (parameterized prompts the model executes, for example "summarize text" or "extract entities") and native functions (regular C# or Python code, for example a database query or a call to an internal API) under a single interface. To the model, both look identical: they're invokable functions with a name, description, and typed parameters, which lets the function-calling engine choose between them without distinguishing their underlying implementation.
When the underlying model supports native function calling (like GPT-4o), Semantic Kernel automatically exposes registered plugins as available tools and executes the real invocation when the model requests it, with no additional routing code. For scenarios where you need to plan a sequence of steps before executing (rather than deciding function by function), the Handlebars planner and the Function Calling Stepwise Planner generate an explicit plan that can be inspected and audited before execution -- valuable in business processes where the sequence of actions matters.
The Agent Framework, built on top of the base kernel, adds the concept of an agent with its own persona and instructions, and lets you compose multi-agent conversations where different agents (each with its own kernel, model, or set of plugins) collaborate within a single orchestrated chat, with configurable turn-taking strategies (who speaks next) and termination strategies (when the task is considered resolved).
Semantic Kernel abstracts access to vector databases (Azure AI Search, Qdrant, Redis, Chroma, among others) behind a common vector store interface, letting you swap the memory backend without rewriting the application's retrieval logic -- a useful pattern when a project starts with a lightweight store for development and migrates to Azure AI Search in production.
A common workflow on Microsoft teams: prototype the agent logic in Semantic Kernel, where the code iteration cycle is faster, then migrate the agent definition to AI Foundry Agent Service for production, gaining managed thread handling, scaling, and observability without losing the ability to keep using Semantic Kernel as the multi-agent orchestration layer on top.
Carlos Montiel is an enterprise AI solutions architect. He implements LLMs, Agents, RAG, and orchestrators for companies across Guatemala and Latin America. Reach out for a consultation.
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