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Prevent impossible changes by making valid states and transitions explicit in the model. Replace combinations of unrelated flags with state-specific data, then allow only the events or operations that make sense for the current state. Types can catch some invalid transitions before runtime; external input, mutation, and side effects still need runtime safeguards.
Why do impossible changes happen?
A state machine describes the states a system can be in and the events or conditions that move it between them. MDN describes states as conditions in which a system waits for a transition, and transitions as actions triggered by events or conditions (MDN’s state-machine glossary).
Consider a request with three booleans: isLoading, hasData, and hasError. Each can be true or false, so the representation permits combinations such as loading and error at once, or success data and an error simultaneously—even if the application should never allow those combinations. The problem is not just a missing conditional: the data shape itself permits contradictory states.
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Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Stately’s state-machine documentation makes the same point with a form that cannot be filling out and submitting at the same time. An explicit model helps expose such impossible combinations and undesirable transitions (Stately: What are state machines and statecharts?).
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How should the state be represented?
Separate a finite control mode from the data associated with that mode. A request may be in one of four control states—idle, loading, success, or error—while its context can contain arbitrary values such as a username, response, or error details.
Represent the modes as distinct variants, with only the data valid for each one. In TypeScript, for example:
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type RequestState<T> =
| { status: "idle" }
| { status: "loading" }
| { status: "success"; data: T }
| { status: "error"; error: Error };A success state carries a result; loading does not. The representation no longer offers a single broad object whose optional fields can combine loading, data, and error in contradictory ways. A state machine need not use TypeScript specifically: the important idea is to make each valid state’s shape distinct in the language or schema being used.
How do you prevent invalid transitions?
Valid data shapes address one problem; valid state-and-event pairs address another. Define the allowed events and their destination for each state. For the request example, idle accepts FETCH; loading accepts RESOLVE or REJECT. A completion event should not be applied to an idle request.
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One approach is a centralized transition map. A type-level example from Type-level.dev maps each state to accepted events and destination states; an absent edge resolves to TypeScript’s never, so an invalid event call fails type checking (Type-level.dev: State Machines in the Type System, published April 15, 2026).
Another approach is typestate: give each state a different type and expose only operations permitted from that state. Idris documentation describes encoding valid operations in types (Idris 1.3.3: State Machines in Types); a TypeScript library example likewise uses state-specific transition methods (doeixd/machine documentation). These approaches express the same constraint differently: a transition map centralizes the state/event rules, while typestate APIs make permitted operations available on each state-specific value.
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What does the type system catch—and what does it not?
- State representation: A tagged variant can make contradictory data combinations unavailable to code checked against that type.
- Transition representation: A typed transition map or state-specific API can reject invalid operations at compile time when the language and API encode those constraints.
- Runtime boundary: Incoming network data is not validated merely because the application has types. Parse and validate external payloads before admitting them into the modeled state system.
- Runtime behavior: Compile-time checks do not automatically prevent nested mutation, side effects, global-state reads, or nondeterministic behavior. The TypeScript library documentation identifies these as outside its compile-time guarantees.
For a runtime dispatcher, handle unknown events explicitly: reject them, return a structured error, or otherwise define the intended behavior. Types protect only code paths checked by the type system; runtime guards are still needed for values that arrive from outside it or bypass the model.
Which modeling approach fits the workflow?
| Approach | How transitions are expressed | Where invalidity can be caught | Best fit |
|---|---|---|---|
| Optional fields and unrelated flags | Conditionals distributed through the code | Often later through tests, review, or runtime checks | Very small, temporary logic where the possible combinations are genuinely harmless |
| Tagged state variants | Each state has its own data shape | Compile time for code checked against the type; runtime validation for external input | Workflows where states carry different data, such as loading, success, and error |
| Central transition map | A state/event map specifies accepted events and destinations | Compile time when encoded in the type system; runtime guards for untyped events | Workflows where rules should be visible in one place and invalid state/event pairs should be constrained |
| Typestate API | Each state-specific type exposes only its permitted operations | Compile time for calls checked by the type system | APIs where callers should be guided toward legal operations at each step |
This is a practical design comparison, not a performance benchmark. A small workflow may not justify a substantial abstraction; a workflow with meaningful state-specific data or many invalid event combinations often benefits from making its rules explicit.
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How do you apply the model safely?
- List the real control states. Name the distinct modes, such as
idle,loading,success, anderror. Keep arbitrary context values separate from this finite set. - Assign data to the state that owns it. Put the response in
success, error details inerror, and avoid optional fields that allow contradictory combinations. - Write the allowed transitions. For every state, identify which events it accepts and the resulting state. Decide deliberately what happens for an event that is not allowed.
- Choose where to enforce the rules. Use a tagged union, transition map, typestate API, runtime guards, or a combination appropriate to the language and workflow.
- Validate boundary inputs. Parse untrusted events and payloads before they enter the model; do not treat compile-time annotations as runtime validation.
- Keep effects explicit. Model the state transition separately from effects such as network calls where practical, and account for mutation and global state that type checking does not control.
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