Semantic Kernel is a lightweight, open-source development kit from Microsoft for building AI agents and adding AI models to existing C#, Python or Java codebases. It acts as middleware: when a model decides to call a function, Semantic Kernel translates the request into a function call and passes the result back. Developers describe their existing code as plugins, and prompts combine with current APIs to perform real actions. The kit is free, with version 1.0+ support across all three languages and a commitment to non-breaking changes.
The audience is software developers and engineering teams, especially in enterprises that already run C#, Python or Java services and want AI features without rewriting them. Microsoft and other Fortune 500 companies use it, citing flexibility, modularity and observability. Teams concerned with responsible deployment can rely on telemetry support plus hooks and filters for control over what models do. Pro-code and low-code developers inside one company can also share extensions, because plugins follow OpenAPI specifications.
Typical use cases include automating business processes by letting a model call internal functions, building agents that answer requests by invoking company APIs, and extending existing chat-based APIs to handle voice and video. Because connectors are provided out of the box, a team can swap in a newly released model without touching the rest of the codebase. Developers also package existing business logic as plugins to maximize the value of earlier work, and share those plugins with colleagues who build solutions in other tools. Enterprise projects that need observability at scale can add telemetry from the start.
