Artificial intelligence (AI) has become the buzzword of our time. It’s a term that often conjures images of robots and self-learning machines, but in reality, AI is a broad umbrella with many distinct subfields. Two of the most talked-about developments today are generative AI and agentic AI. The crucial thing to grasp is that they function in distinctly different ways. Understanding these differences is essential if we want to grasp how AI is reshaping our world—and how it will continue to do so. Generative AI is all about creation. Think of it as the imaginative side of artificial intelligence. These systems are designed to produce content—text, images, music, code, and even video. At its core, generative AI learns from existing data and uses that knowledge to generate new, original outputs that mimic human creativity. The rise of tools like ChatGPT, DALL•E, and MidJourney has catapulted generative AI into the mainstream. These systems rely on advanced machine learning models, particularly neural networks, to analyze and replicate patterns in the data they are trained on. But generative AI isn’t perfect. Its outputs are only as good as the data it’s trained on. Feed it biased or incomplete data, and it will reflect those flaws. Moreover, it doesn’t truly “understand” the content it creates.

Full explanation : What are the key differences between Generative AI Vs. Agentic AI.