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Why Your Feature Flag Service Should Validate Values

Feature-flag values drive application behavior. Explicit contracts and validation at definition, publication, and evaluation time help catch wrong or unusable values.

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Your application treats feature-flag values as runtime configuration. If a flag supplies the wrong type—or a value the application cannot safely use—code can take an unintended path or fail during evaluation. Define a contract for each flag and check values at the points where they are created, published, and consumed.

Feature flags are configuration, not just on/off switches

A feature flag may return a boolean, string, number, or structured value. The OpenFeature specification defines these value types and documents TYPE_MISMATCH for a value that differs from the type the caller expects. Its wording is direct: “The type of the flag value does not match the expected type.” OpenFeature: Types and Data Structures

That distinction matters because application code acts on the returned value. A caller expecting a number may not know what to do with a string; a structured setting may be missing a field the code assumes exists. The result can be an incorrect branch, a failed operation, or unexpected behavior rather than the intended configuration change.

Type checking is not the same as validating a value

Primitive type checks answer whether a value is a boolean, string, number, or structure. They do not necessarily answer whether it is acceptable for a particular application. A numeric timeout, for example, could be numeric yet outside the range the application considers safe. A string could have the right type but not be one of the choices the code supports.

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Encode those application-level rules in a schema or validation rule when the tooling supports them. Depending on the schema and implementation, rules may cover allowed choices, required fields, or other structural and domain constraints. Do not assume that every feature-flag service checks every such rule: confirm what its validation actually enforces.

Validate at more than one boundary

No single check covers the entire path from a developer defining a flag to an application consuming it. Manifest checks, control-plane checks, and runtime checks address different failure points and can complement one another.

At definition or build time: make the contract explicit

A flag manifest can keep a flag’s key, description, type, and default value together. The OpenFeature CLI documentation describes a schema-backed manifest, JSON Schema validation, and generated type-safe clients. This can catch definition problems early and make the expected type visible to application code. OpenFeature CLI

Typed evaluation methods provide another way to make the caller’s expectation explicit. OpenFeature describes typed methods for boolean, numeric, string, and structured values. Use the method that matches the contract rather than relying on an untyped value and an informal assumption. OpenFeature: Flag Evaluation API

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When values are saved or published: reject invalid configuration early

Validation in a service’s control plane can stop a bad value before it reaches applications. For example, LaunchDarkly documents JSON Schema validation for multivariate flag variation values after a flag is saved. That is a specific documented capability, not evidence that every flag service validates every constraint or that every invalid edit is blocked before saving. LaunchDarkly: Creating flag variations

At evaluation time: protect the running application

Configuration can still be unexpected at runtime, so applications may need a final check before using a value. OpenFeature hooks can run globally, for a client, or for an individual evaluation invocation; validation is one documented use case. A hook can centralize checks, though teams should keep validation work appropriate for a frequently called path. OpenFeature: Hooks

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Choose failure behavior before an invalid value appears

Validation is useful only when the application has a deliberate response to failure. Decide which mistakes should block a build or publication, and what a running application should do if evaluation returns an unexpected value. OpenFeature says evaluation calls return the caller’s default value during abnormal execution. Its detailed evaluation API can also expose an error code and may include an error message. A default is a fallback, not a guarantee that every outage or unsafe state is prevented. OpenFeature: Types and Data Structures OpenFeature: Flag Evaluation API

  • Set defaults that are safe for the application, not merely convenient for testing.
  • Use detailed evaluation results where operators or application logic need to distinguish a normal value from an evaluation error.
  • Make failures visible through an appropriate monitoring or reporting path; avoid generating excessive logs on hot request paths.
  • Test both valid and invalid values, including the fallback behavior the application will use.
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A practical validation policy

  1. Define each flag’s contract. Record its key, purpose, expected type, default, and any supported domain constraints.
  2. Check definitions early. Use a manifest and schema-backed checks where available; generate or use typed accessors when the tooling supports them.
  3. Check configuration on save or publish. Verify the service’s documented behavior and ensure the rules cover the variations and constraints your application relies on.
  4. Validate before use where risk warrants it. Apply runtime checks through a hook or another suitable layer, especially when a malformed value could trigger an unsafe or costly path.
  5. Specify the response. Choose which errors block release, which use a safe default, and how the team will detect and investigate them.

These checks are not a substitute for testing application behavior or designing safe defaults. They make the boundary between configuration and code explicit, and help catch mismatches closer to where they are introduced.

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