Managing Data Quality in a Headless Architecture

In the previous article, we explored how OneEntry allows you to manage data structure without changing frontend code: adding attributes, grouping them using markers, and building a dynamic interface driven by the model. This provides flexibility and makes it possible to evolve the product without constant deployments. However, once data becomes dynamic, the focus shifts to ensuring correctness and maintaining predictable system behavior. If fields can be freely added and modified, who guarantees that invalid values will not end up in them? Where should rules for required fields, formats, and value ranges live? How can UI behavior be controlled through the data model without reintroducing duplicated logic into frontend code? In a mature headless architecture, responsibility for data integrity cannot reside solely in the interface. It must be embedded in the model itself. In OneEntry, this role is fulfilled by validators and additional attribute fields — mechanisms that allow you to mana