Context Architecture: A Framework for Governing Organizational Judgment in Human-AI Work

Generative AI has substantially reduced the time and effort required to produce content, code, and interface concepts. As production becomes easier, a growing share of work quality depends on something a model does not bring on its own: the information that defines the situation in which the work will be used and judged. In practice, that information is scattered, explained differently in each interaction, and rarely has a clear owner or validity period. This paper proposes Context Architecture, a framework for selecting, structuring, authorizing, and maintaining an organization’s knowledge and judgment when people and AI systems work with it. Its unit of analysis is the context package: a governed set of elements assembled for a defined task or decision. The framework defines five components, Decision, Criterion, Memory, World, and Standard; classification rules; a governance cycle; eight design principles; and a seven-stage method with a readiness gate before production generation. The framework was developed through a design-science approach grounded in the author’s practice as an information architect and design director, prior authored work, and an integrative review of literature on context, organizational memory, design rationale, information architecture, and AI-supported work. It is illustrated through two anonymized engagements, the reusable artifact derived from the first, and a retrospective case used as an interpretive contrast. Evidence that does not fit the framework is reported alongside supporting observations. Six hypotheses, observable variables, and an experimental design are proposed for later evaluation. Because the author developed and applied the framework in all reported engagements, transfer to other practitioners remains untested. This is a conceptual design-science preprint. The field illustrations demonstrate that the framework can be instantiated; they do not establish its superiority over alternative ways of working.