Agentic and Generative AI are emerging as significant paradigms in the AI landscape, each characterized by unique functionalities and applications. Both share foundational elements like weights, pre-training, and fine-tuning, yet diverge in their primary purposes and operational modes. Agentic AI is task-oriented, similar to executing a written musical score, and is suited for applications requiring high autonomy and decision-making, such as autonomous robots and customer service bots. In contrast, Generative AI resembles an improvisational musician, focusing on creating new content and requiring human prompts, which makes it ideal for generating text, images, and unique outputs. While Agentic AI emphasizes task completion and state changes, Generative AI is evaluated based on the quality and originality of its content. Both paradigms have distinct yet complementary roles, with their integration poised to significantly impact future technological developments.