What Is Semantic Kernel? – Definition and How It Works

Learn what Microsoft's Semantic Kernel provides, how plugins and planners work, and where it fits among AI frameworks.

Semantic Kernel is Microsoft's open-source SDK for building AI applications, available for .NET, Python, and Java. It is notable for being the framework with genuine first-class .NET support.

1. The Core Abstractions

The kernel-as-container model is the distinguishing shape: services and capabilities are registered, and the framework resolves them — a dependency-injection idiom that will look familiar to .NET developers and slightly unusual to Python ones.

2. Plugins Are Just Annotated Functions

from semantic_kernel.functions import kernel_function

class CollectionPlugin:
    @kernel_function(description="Fetch a page and return its main text content")
    def fetch_page(self, url: str) -> str:
        return extract_main_content(http_get(url))

    @kernel_function(description="Search collected documents for a query")
    def search_corpus(self, query: str, limit: int = 5) -> str:
        return format_results(retrieve(query, limit))

kernel.add_plugin(CollectionPlugin(), plugin_name="collection")

The description is not documentation — it is what the model reads to decide whether to call the function. Vague descriptions produce unused or misused tools, which makes writing them carefully the highest-leverage part of the work.

3. Two Kinds of Function

Native functions are ordinary code, as above — deterministic, testable, and where anything with real consequences belongs.

Prompt functions are templated prompts treated as callable units:

Summarise the following page content in three bullet points.
Content: {{$content}}

Treating a prompt as a named, versioned, parameterised function rather than an inline string is a genuine organisational improvement — it makes prompts reviewable and swappable.

4. Planners, and Why to Be Careful

A planner asks the model to decide which functions to call in which order to satisfy a goal.

It is the most impressive feature in a demonstration and the one requiring most caution in production:

• Plans are nondeterministic — the same goal produces different plans.
• Failures are hard to reason about, because the control flow was generated.
• Cost and latency are unpredictable.
• A plan may call functions in orders you never anticipated.

For anything where correctness matters, an explicit pipeline with the model making narrow decisions inside it is more defensible than delegating control flow. Use a planner for exploration; pin the plan for production.

5. Where It Fits

Choose it when: you are on .NET and want first-class support, you are in an Azure-centric environment, you want the DI-style composition, or you need one framework across .NET, Python, and Java.

Choose something else when: you want the largest Python ecosystem, you need a graph-based agent runtime with explicit state, or you want the lightest possible abstraction.

6. Compared With the Alternatives

Its .NET support is the clearest differentiator — for a .NET team it is close to the only mature option, which matters more than any feature comparison.

7. Giving It Web Data

The natural integration for collection work is a plugin wrapping your own retrieval:

1. Expose search over your collected corpus as a function, not raw fetching.
2. Write precise descriptions, since they drive tool selection.
3. Return structured, bounded results — a handful of records, not a whole page.
4. Keep credentials in native code, never in prompt templates.
5. Include provenance in returned results, so answers can cite sources.
6. Validate and bound arguments — a model-supplied URL or limit is untrusted input.

Point 6 matters: a function that fetches a model-chosen URL is an SSRF risk unless the target is validated against an allowlist.

8. Summary

Semantic Kernel is Microsoft's multi-language AI SDK organised around a kernel container, plugins of native and prompt functions, planners, and memory connectors, with first-class .NET support as its main differentiator.

Function descriptions drive tool selection so they deserve care, prompts-as-named-functions is a real organisational gain, and planners should be treated as an exploration tool rather than production control flow — while any function taking a model-supplied URL needs allowlist validation.

Potential Related Glossary

• Data Structure
• Unstructured Data
• Parsing
• Web Crawling