Principle:Microsoft Semantic kernel Process Step Definition
| Knowledge Sources | |
|---|---|
| Domains | Process_Orchestration, Event_Driven_Architecture |
| Last Updated | 2026-02-11 19:00 GMT |
Overview
Process steps are the fundamental units of work within Semantic Kernel's process orchestration framework, each encapsulating a discrete operation with event emission capabilities.
Description
Process Step Definition is the design principle that governs how individual units of work are modeled within Semantic Kernel's event-driven process framework. Each step is a self-contained class that derives from KernelProcessStep and exposes one or more methods decorated with the [KernelFunction] attribute. These methods define the executable behavior of the step within a process workflow.
The step model follows a clear separation of concerns. A step does not know about other steps in the process; it only knows how to perform its own work and emit events when it has results or needs to signal downstream activity. This isolation is achieved through the KernelProcessStepContext parameter, which is automatically injected into step functions by the process runtime. The context provides the EmitEventAsync method, which is the sole mechanism by which a step communicates outcomes to the rest of the process graph.
Steps can also maintain internal state across invocations by deriving from the generic KernelProcessStep<TState> variant. When a step declares a state type, the framework will persist and rehydrate that state across process executions. The ActivateAsync method is called when the step is first initialized, providing the opportunity to restore state from a previous run or set initial values.
This design creates a programming model where each step is:
- Independently testable: Steps can be unit tested in isolation by providing mock contexts.
- Reusable across processes: The same step class can participate in multiple different process definitions.
- Event-driven: Steps communicate exclusively through events, not direct method calls.
- Stateful when needed: The optional state generic parameter allows steps to maintain data across invocations without external storage.
Usage
Define a process step whenever you need to encapsulate a discrete unit of work within an event-driven process workflow. Steps are appropriate for any operation that has a clear input, performs some computation or side effect, and produces an output or signal. Common examples include data validation, AI service calls, data transformation, user interaction handling, and external system integration.
Theoretical Basis
The Process Step Definition principle draws from several established software engineering patterns:
Command Pattern: Each step encapsulates a single action as an object. The [KernelFunction] method on a step is analogous to the Execute method on a command object. This allows the process runtime to invoke, queue, and retry steps uniformly without knowing their internal details.
Event Sourcing: Steps communicate exclusively through events. The step does not directly call another step; instead, it emits a KernelProcessEvent that the process runtime routes to the appropriate target. This decoupling means:
Step_A.Execute() → EmitEvent(E1)
Process_Runtime.Route(E1) → Step_B.Execute()
Rather than:
Step_A.Execute() → Step_B.Execute() // Direct coupling - NOT used
Single Responsibility Principle: Each step should encapsulate exactly one logical operation. The step does not need to know its position in the process graph, how many predecessors or successors it has, or what the overall process topology looks like. It simply performs its function and emits events.
State Machine Semantics: When using KernelProcessStep<TState>, each step can be modeled as a stateful node in a state machine. The state is preserved across activations, and the ActivateAsync method serves as the state restoration hook:
ActivateAsync(state) → Restore internal state
[KernelFunction] Execute(context, inputs) → Perform work, update state, emit events
The combination of these patterns produces a step model that is both flexible and predictable: steps are loosely coupled through events, independently stateful, and uniformly invocable by the process runtime.