What Is Agentic Analytics?

Agentic analytics uses AI agents to interpret business questions, plan and execute multi-source analyses, validate results, iterate on follow-ups, and, when authorized, trigger operational actions, shifting people from writing individual SQL queries to guiding autonomous systems. Unlike basic chatbots that may generate a single query, these agents discover relevant data, combine sources, and evaluate outputs, but they can produce plausible yet incorrect answers when they lack shared definitions for terms such as revenue, churn, or active customer. Effective deployment therefore depends on a governed context layer containing semantic definitions, ownership, freshness information, certified data products, quality expectations, and access policies. The approach can query federated data where it resides under existing security rather than requiring full centralization, while an open lakehouse using formats such as Apache Iceberg can support high-performance local analytics and auditability. Governance should enforce read-only query permissions, role- and attribute-based controls, and audit logs at the server level, while a separate control plane manages any write actions or external workflows. Starburst presents its AIDA product as an example of this model and recommends beginning with one domain, testing agents against known-correct “golden” questions, and expanding only after accuracy, consistency, and traceability are established.

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