Agentic AI refers to artificial intelligence agents that work autonomously in real-world and virtual settings, utilizing advanced AI techniques to make decisions, take actions, and achieve goals independently. This technology traces back to the development of artificial intelligence, with early efforts focusing on creating programs that mimicked human decision-making within limited contexts. The modern era of agentic AI began in the 21st century with the advent of machine learning, neural networks, and reinforcement learning, empowering systems to learn from existing data, adapt to change, and pursue goals with minimal human intervention. Agentic AI can be distinguished from generative AI, which focuses on creating new content based on patterns learned from data. The technology has numerous benefits for organizations, including improved efficiency and productivity, reduced expenses, and enhanced agility and adaptability. However, its adoption also raises concerns about job displacement and the need for careful planning, continuous monitoring, and commitment to ethical and transparent practices to mitigate risks. As agentic AI becomes more autonomous and adaptive, it will be widely adopted across industries and seamlessly integrated into daily functions, enabling agents and humans to work together to solve global issues.