By skyforbes 
Artificial Intelligence has entered a phase of redefinition. Beyond generative models and chatbots, we now face deeper split between task-oriented AI agents and goal-driven, autonomous agentic AI.
Though both appear similar in name and origin, their essence and operation differ in powerful ways. This discussion walks through how AI evolved from early autonomous systems to today’s multi-agent intelligence, showing what truly separates AI Agents from Agentic AI, and what that means for the future of intelligent technology.
Before the rise of generative models, autonomous systems followed a rule-based path. Early AI including expert systems, MYCIN, DENDRAL, or XCON worked within tightly scripted boundaries. They could reason only as far as their pre-defined rules allowed. These systems lacked adaptability; once the environment changed, they failed to adjust.
That rigidity shaped decades of AI research. Then came a turning point in the shape of Large Language Models (LLMs) and Large Image Models (LIMs). When ChatGPT appeared in late 2022, interest in AI Agents and Agentic AI spiked globally, marking the beginning of a new era.
