Enterprise business intelligence has traditionally depended on dashboards that visualize historical and near-real-time data for human interpretation. Although dashboards remain essential for transparency and performance monitoring, they often require decision makers to detect anomalies, interpret root causes, and translate insights into action. This paper proposes an agentic artificial intelligence framework for enterprise business intelligence modernization, shifting BI from passive reporting toward autonomous, governed, and decision-oriented intelligence. The study uses a conceptual design methodology supported by literature on agentic AI, enterprise analytics, decision intelligence, and national digital transformation priorities. The proposed framework integrates five layers: unified data foundation, semantic and KPI governance, agent orchestration, human-in-the-loop decision control, and performance assurance. It explains how AI agents can monitor business metrics, detect exceptions, generate contextual explanations, recommend actions, and support executive decision-making while preserving governance, accountability, and responsible AI controls. From a business and economic perspective, the framework links analytics modernization to productivity improvement, cost optimization, working-capital visibility, faster management response, and measurable return on AI investment. The framework is positioned for Saudi enterprises aligned with Vision 2030, where data and AI are central enablers of economic diversification, operational efficiency, and digital maturity. The paper contributes a practical modernization model for organizations seeking to evolve from descriptive dashboards to agentic intelligence ecosystems.