Building an Agentic Analytics Harness

Cube’s shift from a universal semantic layer to agentic analytics has centered on building an “agentic analytics harness” that governs model context, tools, memory, validation, and permissions to make AI behavior reliable in production. The company found that SQL gives agents the flexibility to explore unanticipated questions, while Semantic SQL preserves governed metric definitions, joins, aggregations, and access controls rather than exposing raw warehouse logic. Reliable tools require precise descriptions, validation, and recovery-oriented error messages so models can understand failures, partial results, and next steps without guessing. Because context is limited, Cube truncates and paginates query results, separates specialized tasks into different agents, and stores persistent work in durable artifacts such as version-controlled data model changes. The semantic layer supplies executable business definitions and policies, but agents also need distinct forms of contextual knowledge, including documentation, certified queries, connectors, and scoped memory. Rather than preloading an entire potentially outdated model, Cube searches the semantic model on demand, filters results by user access, and uses metadata and lineage to help agents select among similar measures.

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