The modern analytics stack, once considered the future, is now showing its age due to the limitations of traditional tools. The rise of generative and agentic AI is exposing these limitations, with business users still waiting for answers and data teams managing backlogs. Agentic analytics aims to bridge this gap by providing autonomous decision-making capabilities through the use of AI agents that generate queries, explore patterns, surface anomalies, and recommend actions grounded in context and driven by logic. Agentic analytics differs from traditional self-service BI tools, which provide more control but not always more insight, by leveraging generative AI techniques for data analysis. It bridges the gap between human roles in the data and analytics lifecycle, ensuring data integrity and automating exploration, decision-making, and follow-up tasks. With Cube D3's agentic analytics, users can ask questions into an Analytics Chat Interface, receiving governed semantic SQL queries and explanations within seconds, and receiving follow-ups and suggested next steps.