Why the Enterprise Context Layer Will Make or Break Enterprise AI

As enterprises shift from analyst-mediated business intelligence to AI agents and large language models that query data directly, the central challenge becomes preserving the institutional context analysts once supplied, such as approved metric definitions, reliable sources, business rules, and domain-specific interpretations. The proposed enterprise context layer is a governed functional tier between agents and data that provides structured metrics and logic, semantic relationships across disparate systems, and traceability and access controls for auditable answers. Unlike broad data catalogs that can expose agents to obsolete, temporary, or conflicting assets, it selectively presents steward-certified “gold” data products and metadata harvested from tools such as dbt, Tableau, catalogs, and query histories. Treating data products as code through version-controlled YAML definitions and CI/CD workflows can keep context aligned with changing data pipelines across distributed architectures. The layer can also improve over time by incorporating agent usage, human feedback, and steward corrections, with the goal of giving both people and AI systems a current, certified shared understanding of enterprise data.

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