Conventional analytics tools answer questions that a human already knew to ask. Agentic systems plan a line of inquiry, decompose it into queries against governed data, evaluate what comes back, and iterate — and in the operational case, act on the result.
This paper sets out an architecture for agentic AI in enterprise analytics: the semantic layer agents reason over, the orchestration and tool-use patterns that make multi-step inquiry reliable, and the guardrails — permissioning, provenance, human-in-the-loop checkpoints — without which autonomy is not deployable inside a regulated enterprise. It closes by contrasting agentic analytics with conversational BI, which shares an interface but not a control model.
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