Agentic AI refers to a class of artificial intelligence systems designed to act autonomously toward defined goals, making decisions, initiating actions, and adapting behavior based on changing conditions and feedback. Unlike traditional AI models that primarily respond to direct inputs with outputs, agentic AI systems operate with a degree of independence, often orchestrating multiple steps, tools, or processes to achieve an objective over time. The term has gained prominence alongside advances in large language models, automation frameworks, and multi-agent systems, where software entities increasingly resemble goal-driven actors rather than passive tools.

Autonomous systems sit in that slightly uneasy space between tools and actors. They are built by humans, constrained by code and hardware, yet increasingly capable of making choices that feel less like execution and more like judgment. At a basic level, they are machines or software that perform tasks without continuous human guidance—self-driving cars navigating city streets, industrial robots adjusting production flows in real time, or software agents managing logistics, trading, or customer interactions.