Agentic AI represents a significant evolution in artificial intelligence, characterized by autonomous systems capable of perceiving environments, reasoning about goals, and independently executing actions without detailed instructions. Unlike traditional AI, which performs singular tasks based on fixed inputs, agentic AI dynamically adapts and collaborates to achieve complex objectives, akin to a smart assistant managing projects. This approach necessitates new considerations in system design, safety, and ethics, as it shifts from command-following tools to initiative-taking systems. The architecture of agentic AI involves distinct layers for perception, reasoning, memory, and action, which work together to enable real-time decision-making and adaptability. Applications span various industries, from healthcare diagnostic assistants to travel-planning systems, showcasing the potential for transforming workflows through continuous adaptation and minimal human intervention. However, deploying these systems poses challenges, including ensuring responsible behavior, managing scalability, and handling unpredictable scenarios, necessitating robust safety measures and human oversight to mitigate risks.