Agentic AI, AI Agents, and Agents: What’s the Difference?

Recently, I’ve been hearing a lot about Agentic AI, AI Agents, and Agents. If I’m being honest, I used to detest these AI-related trends and buzzwords, despite being a data analyst and scientist.

It felt like the tech world was moving too fast, and I wasn’t sure I wanted to keep up.

But here’s the simple truth: if I don’t make an effort to understand these concepts, I risk becoming irrelevant in a few years. And that’s not an option.

So, I decided to dig deeper, and truthfully, these concepts are more fascinating than I expected.

In this article, I’ll break down what I’ve learned about AI Agents, Agentic AI, and Agents, and highlight their differences in the simplest way possible. So if you’re new to this, you’re in the right place.

Last week, my colleagues and I were discussing three terms: agentic AI, AI agents, and agents. At first glance, they sound similar, but they’re not interchangeable. Here’s how I’ve come to understand them:

Agents

In the broadest sense, an agent is any entity (software, hardware, or even a human) that can perceive its environment and take action to achieve specific goals. It doesn’t need AI. They can operate based on simple rules or predefined logic.

↳ Example: A thermostat is an agent. It senses the temperature (perceives its environment) and turns the heating or cooling system on or off (takes action) to maintain a set temperature (achieves a goal). It doesn’t need AI to do this. It’s just following predefined rules.

AI Agents

These are agents powered by artificial intelligence. They go beyond simple rule-based systems and use machine learning, natural language processing, or other AI techniques to make decisions.

These agents are more advanced because they can learn from data, adapt to new situations, and improve over time.

↳ Example: Virtual assistants like Siri or Alexa are AI agents. They use natural language processing to understand your voice commands, machine learning to improve their responses, and other AI techniques to perform tasks like setting reminders or playing music.

Many AI models today (like GPT) can act as agents when integrated into workflows but aren’t fully autonomous.

That’s where Agentic AI comes in.

Agentic AI

This is where things get interesting. Agentic AI takes AI agents a step further by making them more autonomous, adaptable, and proactive.

Unlike regular AI agents that wait for instructions, Agentic AI can plan, make independent decisions, and act without human prompting.

↳ Example: An agentic AI system managing a smart home might not only adjust the temperature, but also order groceries when supplies are low, schedule appliance maintenance, and optimize energy usage — all without explicit human input.

Another example is that instead of just booking a flight when asked, an agentic AI could monitor flight prices, alert you to the best time to book, and even rebook if a better deal appears — all without you asking.

In essence:

• AI agents are tools.
• Agentic AI behaves more like a decision-maker.

If you’re a data professional (or aspiring to be one), ignoring this shift could mean falling behind. Companies are increasingly integrating autonomous AI systems, and knowing how they work can give you a competitive edge.

For newbies, this is an opportunity to stay ahead of the curve. If you understand agentic AI now, you’ll be part of the conversation, not catching up later.

What are your thoughts on AI agents and agentic AI? Have you worked with them, or are they still just buzzwords to you?

#estheranagu #aiagents #agents #agenticai