Context Engineering for Snowflake

Context engineering is presented as an architectural approach for enabling AI agents to use Snowflake data reliably by supplying governed business definitions, metadata, and access policies that raw warehouse tables alone may not convey. The discussion argues that while Snowflake provides scalable storage and compute, agents need a shared context layer to identify authoritative datasets, apply consistent KPI definitions, and enforce row- and column-level permissions. Rather than replacing Snowflake or requiring data migration, this layer can use federation to connect Snowflake with other systems, including data lakes and Iceberg tables, while delivering curated, governed data products to analysts and agents. The article also highlights the Model Context Protocol as a managed and observable mechanism for agent access, and recommends beginning with a limited pilot that defines key terms, applies centralized policies, packages reusable data products, and then expands the pattern across teams.

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