Understanding Agentic AI, AI Agents and Agents in one article: Don’t confuse them anymore!

The three words Agentic AI, AI Agents and Agents frequently appear in major self-media.

When discussing this with a friend last week, we found that it is easy to confuse these three terms. At first glance, they are similar, but they are not interchangeable. But it may seem unimportant to understand their differences, and when everyone is confused about something, they are all right.

From the current perspective, real Agent/Agentic products seem to be getting rid of the concept of Agent. For example, AI programming, AI research...

Overemphasizing the agent concept may cause users to have too high expectations, and piling up high-end vocabulary will distance users from the product. On the contrary, product positioning that focuses on solving specific problems is more likely to gain user recognition.

Secondly, users care about what problems a product can solve, not what technical category it belongs to. The cursor is popular because it can efficiently assist programming, not because it is an agent.

Of course, returning to the topic of this article, the following are the strict definitions of the three obtained after searching:

Agents

The most basic concept refers to any entity that can perceive the environment and act to achieve a goal. It can be software, hardware, or even a person. The key is: it does not require AI to work.

For example , the thermostat of your water heater at home is a typical agent. It senses the temperature (environmental perception), turns the heating system on and off (takes action), and maintains the set temperature (achieves a goal). It just works according to preset rules and does not require any AI capabilities.

AI Agents

These are upgraded agents, driven by AI. They no longer just follow simple rules, but can make decisions using AI technologies such as machine learning and natural language processing.

The best feature is that it can learn from data, adapt to new situations, and become smarter over time.

For example , virtual assistants such as Siri and Xiao Ai are AI Agents. They can understand your voice commands, learn to improve the quality of their responses, and perform tasks such as setting alarms and playing music.

Many current AI models, such as GPT, can act as agents when integrated into workflows, but they are not fully autonomous.

Agentic AI

This is the real kicker! Agentic AI takes AI agents to a whole new level, making them more autonomous, adaptable, and proactive.

Unlike ordinary AI agents that passively wait for instructions, Agentic AI can plan and make decisions independently and can act without human instructions.

For example : An Agentic AI system that manages a smart home can not only adjust the temperature, but also automatically place orders when food is running low, schedule appliance maintenance, and optimize energy use - all without you having to do anything.

For example, it not only helps you book flights as requested, but also actively monitors ticket prices, reminds you of the best time to buy tickets, and even automatically rebooks when a more favorable price is found - all without you having to ask.

Essential difference:

• AI Agents are tools, waiting for you to use them
• Agentic AI is a decision maker that proactively serves you

at last

As a popular concept, Agent has been overused and hyped. Some mature products choose to return to their essence, emphasizing their actual value rather than conceptual labels. Moreover, compared to learning how to interact with Agent, users are more willing to use products that look like traditional tools but have AI capabilities.