From Reactive BI to Agentic AI: The Rise of Domain Specific Autonomous Insight Systems in Enterprise Analytics

Enterprises need descriptive and diagnostic insight engines that think ahead of users, not after them. This paper presents a research framework for agentic analytics that unifies four ideas into a single, verifiable system. First, ontology aligned grounding maps business language to concrete data assets and permitted joins. Second, a scaffolded tool layer executes plans safely across query engines, catalogs, code runners, and visualization services while exposing traces for audit. Third, a multi-turn hypothesis engine treats explanations as first-class objects that are generated, tested against data, revised, and ranked. Fourth, a dual judge mechanism fuses an LLM critic with a gold data layer that serves as the final arbiter of numerical claims and structural correctness. We specify rubric signals for each stage and show how those signals become rewards for reinforcement learning of both single agents and coordinated teams. The result is a pathway from natural language intent to governed, reproducible descriptive analytics that improve over time.