I see this mistake every day—people use these two terms interchangeably. They may sound similar, but they are very different.

AI Agents and Agentic AI both involve decision-making and actions, but their autonomy, adaptability, and scope set them apart.

Why you should not mix these up?
đź“Ś Calling a simple AI agent (like a chatbot) “Agentic AI” is incorrect because it lacks advanced autonomy, reasoning, and adaptability.
đź“Ś Similarly, calling a truly agentic system just an “AI Agent” downplays its complexity and potential.

To put it into a simple example: A self-driving car follows rules and reacts to traffic, but a fully autonomous robotic driver can learn, plan, and handle unexpected situations on its own.

👉 Another aspect that you need to keep in mind is on their training methods:
AI Agents (Task-Specific Learning) → Trained using predefined objectives and methods like:
đź’  Supervised Learning
đź’  Reinforcement Learning
đź’  Imitation Learning

Agentic AI (Autonomous, Multi-Step Learning) → Requires more advanced and adaptive training:
đź’  Multi-Objective Learning
đź’  Reinforcement Learning with Memory
đź’  Self-Supervised Learning
đź’  Goal-Oriented Learning (e.g., Hierarchical Reinforcement Learning, Tree Search Algorithms)

As you can see, the resources and time to train these are also very different:
⏳ AI Agents can be trained in hours to weeks for specific tasks.
⏳ Agentic AI requires months to years, as they have to complex and massive datasets to enable multi objective learning.

🚀 So next time, before using these terms freely, pause and think—are you using the right one?