Augmentable

Augmentable describes the capability of a system, data structure, or object to be expanded by adding new elements, information, or functionalities without requiring a complete redesign. The augmentation process typically preserves existing behavior while extending capabilities, attributes, or contents.

In software engineering, augmentable design emphasizes extensibility through well-defined extension points, plugins, or modules. Augmentable data models and databases support backward-compatible schema evolution, allowing new fields or relations to be added without breaking existing data. In knowledge representation, augmentable ontologies and knowledge graphs can incorporate new concepts, relations, or instances and often use provenance and versioning to manage changes. In machine learning, augmentable datasets can be enriched with additional samples, features, or labels, enabling incremental or iterative learning.

Key design considerations include modular interfaces, stable contracts for extensions, and clear separation between core and extension components. Effective augmentation often relies on loose coupling, version-controlled schemas, and well-documented extension points to manage compatibility and maintainability. Potential challenges involve complexity growth, dependency management, performance overhead, and the risk of semantic drift if extensions are not carefully governed.

Examples of augmentable systems range from plugin-enabled applications and extensible APIs to knowledge bases that accept new entries and schema-evolving databases. The term emphasizes the future-proofing of a design, prioritizing the ability to grow in capability and scope without foundational changes.

Etymology derives from the verb augment, with the suffix -able indicating capability.