From a simple rule-based system focused on task execution, to a collaborative system focused on autonomous decision-making, Artificial Intelligence has undergone a rapid transformation, matched by equally rapid adoption. This article explores the evolution of AI, unpacks the differences between traditional AI agents and agentic AI, and examines why this distinction matters.
What Is Artificial Intelligence?
A field of research in computer science since the 1950s, Artificial Intelligence (AI) refers to the capability of computers to perform human intelligence tasks such as learning, reasoning, problem-solving, perception, and decision-making. Its applications include search engines, recommendation systems, generative and creative tools, virtual assistants, vehicles and more.
What are AI Agents?
 AI agents are systems that can take actions automatically to achieve specific, defined goals. They operate only within explicit boundaries set by their design and permissions, thus making them limited in their abilities. In short, AI agents excel at focused automation.Â
There is no continuous learning since AI agents only follow instructions. Today, AI underpins a wide range of applications, including search engines, recommendation systems, generative and creative tools, virtual assistants, etc. These agents are ideal for automating repetitive tasks that follow fixed logic.
What is Agentic AI and How It Works?
Unlike AI agents, which operate within constraints and require human input, agentic AI is designed to operate with greater autonomy. It can act independently and purposefully by understanding the broader goal, determining how to get there, and adapting its approach based on real-time context.
Agentic AI has the ability to plan, reason, and route across multiple agents and systems. It learns and improves itself over time through experience and feedback. Rather than eliminating human oversight entirely, these systems operate with reduced supervision. Some examples of this include autonomous driving systems, AI research agents, and workflow automation tools capable of multi-step decision-making.
What is the Difference Between AI Agents and Agentic AI
AI agents either respond reactively to prompts or execute limited multi-step tasks. Even the most capable agents remain narrowly scoped and are not designed to coordinate complex workflows or adapt strategy on their own. By contrast, agentic AI involves coordinated planning and goal-directed reasoning across systems.
As AI systems become more advanced, and by extension, more autonomous, their role is shifting from task execution to decision-making. AI is now deeply embedded across industries and workflows. However, increased autonomy also brings increased risk. Agentic systems can make errors or unintended decisions at scale, raising questions around oversight and accountability.
Not all AI systems carry the same capabilities or risks. Distinguishing between AI agents and agentic AI helps set appropriate expectations, controls, and responsibility.
