Coronary Artery Disease (CAD) is one of the leading causes of global mortality, which requires early diagnosis and accurate detection. Cardiac CT scans done for audit at AI-Detection of CAD done and this review looks deep into the concept of this agentic AI that works autonomously. Unlike standard deep learning models, Agentic AI incorporates self-improvement mechanisms, multi-agent collaboration, and dynamic learning strategies to boost accuracy for diagnostic purposes. We review cutting-edge AI paradigms such as self-supervised learning, reinforcement-driven optimization, and vision transformer models for the automation of coronary artery plaque and stenosis detection. Agentic AI also outperforms manually extracted CCTA features in terms of calculation speed, time-efficient clinical decisions and manual intervention in the clinical alert process9. Moreover, upon discussing the future trends, the review also discusses some of the challenges such as dataset generalizability, explainability and real-time deployment which needs to be addressed in future, along with the trends, such as hybrid AI-agent systems, continual learning and federated diagnostics. These findings highlight Agentic AI’s game-changing potential in revolutionizing the way CAD is diagnosed and transforming precision medicine for cardiovascular health.