Agentic AI refers to artificial intelligence systems exhibiting autonomy in achieving specific goals with minimal supervision. Unlike traditional AI, which requires prompting and step-by-step guidance, agentic AI can independently make contextual decisions and perform complex tasks. It leverages large language models (LLMs) to function in dynamic environments, extending generative AI capabilities by applying generated outputs toward specific goals. The term "agentic" highlights these models' capacity to act independently and purposefully.
Agentic AI operates through a process of perceiving, reasoning, goal setting, decision-making, execution, and continuous learning and adaptation. It often involves multiple specialized AI agents coordinating with each other to break down complex workflows. These agents gather data from various sources, analyze it to extract meaningful insights, set objectives, and choose optimal actions based on factors like efficiency and predicted outcomes. Agentic AI is being implemented across industries to personalize customer service, streamline software development, optimize supply chains, and facilitate patient interactions. Examples include AI agents for customer service, financial trading, HR, healthcare, and manufacturing.
