Semantic Kernel

The kernel connects configured services and functions so application code can invoke model capabilities and expose native or prompt-based operations. Plugins group callable functionality, with descriptions and contracts that may support model-driven function selection. The SDK has implementations and features across supported languages, whose coverage should be checked for the application. Semantic Kernel is distinct from a model and from a hosted assistant. Microsoft Agent Framework is its documented successor for newer agent development, but existing Semantic Kernel integrations still require understanding their own execution contracts. The skill concerns controlled composition of AI calls with ordinary software components.

The practitioner registers the needed model services and plugins, defines function inputs clearly and controls which functions can be selected. Existing application context should be passed through validated dependencies rather than reconstructed by the model. Useful deliverables include an integration layer, function-call traces and regression tests for business actions. Version and language-specific behavior need verification, especially during migration. Backend functions enforce authorization and invariants independently of whether the model selected an appropriate-looking operation.

Plugin availability does not establish safe use, and generated arguments can satisfy a signature while violating business meaning. Framework feature coverage and orchestration APIs can differ across language versions. Quality requires narrow functions, observable invocation and independent business validation. Existing integrations should be evaluated before moving to a successor framework, because changes in state or calling semantics may alter application behavior even when the same model and nominal plugin functions remain in use.