Glossary term
Typed metadata
Typed metadata is application metadata whose keys and values are constrained by a published schema, so that an unknown field type, a misspelled permission, or a workflow step pointing at an object that does not exist is rejected at a validation gate when the definition is written, instead of being discovered in production after it runs.
Also known as schema-validated metadatatyped application definitionstrongly typed metadata
In practice
The distinction is between a configuration file and a contract. A YAML or JSON config file accepts whatever you put in it; the program that reads it decides at run time which keys it happens to recognize, and quietly ignores the rest. That default is where declared-but-unenforced settings come from: someone writes `requireApproval: ture`, nothing complains, and the approval that was declared never runs. Typed metadata inverts the default. The schema is published, every key has a type, and a key nobody recognizes is an error rather than a shrug — so what the file says and what the system does cannot drift apart without something failing loudly first.
Typing does more than reject typos, because a schema is readable by tooling as well as by validators. The same declaration drives editor completion while the definition is written, a validation gate in continuous integration, and the generated documentation a reader consults later. One published contract, three consumers — which is why the schema is worth maintaining as a first-class artifact rather than as validation logic scattered through whatever reads the file.
The argument gets sharper when an agent is the author. A model asked to add an approval rule fails in a characteristic way: it produces something plausible and adjacent — a real-looking key that this schema does not define, a permission value borrowed from a different platform it saw more often in training. Untyped configuration absorbs that silently and ships it. A schema rejects it at the point of authorship, where the agent can read the error and correct it in the same loop, and the schema also steers generation before the mistake happens, because a published type is exactly the kind of constraint a model can follow.
What typing does not do is worth stating, because overselling it is how validation becomes a substitute for review. A schema constrains shape, not intent. A permission set can be perfectly valid and still grant the wrong people access to salary data; a workflow can type-check and still route approvals to someone who left the company. Typed metadata moves an entire class of error from production to the validation gate and makes the rest smaller and more readable — it does not decide whether the declaration is correct, and nothing in the toolchain replaces the person who signs off.
Where this term is used
Pages and articles that put this term to work.
Product pages
- Data modeling Objects, relationships, validations, and formulas become tables, migrations, a query language, and REST APIs — with an object designer, an ER diagram, and datasource sync that federates existing databases in place.
- Platform ObjectOS is the commercial production platform for ObjectStack apps, combining in-app AI, deployment, and team operations with the open, governed ObjectStack runtime.
- Agent developer Teach a coding agent the open ObjectStack target format, Strict TypeScript definitions, Zod schemas, validation gate, and whole-context proof needed to generate reviewable applications.
Articles
- 업무 앱은 몇 토큰일까? 완전한 CRM 전체가 150k 미만 완전한 CRM은 150k 토큰 미만의 타입 메타데이터로 구성됩니다. 비즈니스 로직은 100k 미만, UI는 약 50k이며, 번들 레퍼런스 CRM은 여전히 약 16k입니다.
- Agent 규칙 파일 작성법: AI가 거버넌스 가능한 앱을 생성하게 하기 AGENTS.md, .cursor/rules, CLAUDE.md는 코드 스타일만 다뤄서는 안 된다. 권한, 승인, 감사, 대상 메타데이터 형식을 쓰면 AI 생성 앱은 처음부터 검토하기 쉬워진다.
- AI-native 애플리케이션의 핵심은 코드 생성이 아니라 메타데이터 생성이다 코드 생성은 프로토타입을 빠르게 만들 수 있지만, 엔터프라이즈 애플리케이션에는 객체, 필드, 관계, 뷰, 권한, 워크플로, 액션, Agent 도구를 함께 통제하는 메타데이터 런타임이 필요합니다.