The Context Access Divide: Interaction-Level Architecture as a Complementary Dimension of Agentic Inequality
Sharp et al. (2025) introduce "agentic inequality" as a framework for analyzing disparities in access to AI agents across three dimensions: availability, quality, and quantity. These person- and organization-level dimensions characterize who can access agents and at what capability, but do not address a structurally important divide operating at a finer level: the individual interaction. Two users with nominally equivalent agent access may experience qualitatively different AI utility depending on whether the system can autonomously retrieve context from the user's knowledge corpus (Dynamic Context Retrieval) or requires the user to manually identify and attach relevant documents at each ...
| Authors | Masahiro Fujita |
|---|---|
| Primary category | cs.CY |
| Published | 9 July 2026 |
| Last updated at source | 9 July 2026 |
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