Introduction
Artificial intelligence is growing fast. But one idea is getting more attention than most. It is called Agentic AI. This is when an AI system does not just answer questions. It takes action. It plans. and it completes tasks on its own.
This blog looks at a 2025 research paper called The rise of Agentic AI*.* The paper explains what Agentic AI is and why it matters. It is a useful read for anyone studying AI today.
What is Agentic AI?
Normal AI answers your question and stops. Agentic AI keeps going. It can look at a problem. Make a plan. Take steps. Check the result. Then try again if needed.
Think of a helpful AI assistant that books your flight. It does not just give you options. It picks the best one. It confirms the booking. It updates your calendar. All by itself.
Other examples include autonomous robots that move around a space and complete jobs. Or AI systems that plan complex tasks across many steps without needing a human to help at every stage.
Agentic AI = AI that can act, plan, and get things done, not just respond.
Main idea of the paper
The paper was written to make sense of a confusing area. Many researchers use different words to describe Agentic AI. Some call it autonomous AI. Others call it AI agents. The authors looked at many studies and tried to find a common understanding.
They also looked at different frameworks. A framework is a structure that helps explain how something works. The paper finds that there is no single agreed framework yet. This is a problem because it makes it hard for researchers to build on each other's work.
The paper argues that Agentic AI is important and so it is becoming more powerful. So we need clear definitions and solid frameworks before things move too fast.
Connection to AI course topics
Agentic AI connects closely to what we study in class. In our AI course we learn about intelligent agents. An intelligent agent sees its environment and takes action to reach a goal. That is exactly what an Agentic AI system does.
We also study goal-based agents and utility-based agents. Goal-based agents plan steps to reach an outcome. Utility-based agents pick the best option from many choices. Agentic AI systems use both of these ideas together.
Planning systems are also part of our course. Agentic AI depends heavily on planning. Without good planning an agent cannot complete long tasks on its own.
Challenges of Agentic AI
The paper does not ignore the problems. There are real challenges with building powerful AI agents.
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Safety of autonomous systems
Reliability of AI decisions
Controlling powerful agents
Lack of agreed definitions
If an AI agent makes a wrong decision and no human is watching it can cause real harm. We still do not fully know how to make these systems trustworthy.
Personal Reflection
One thing I found interesting was how unclear the definitions still are. I expected researchers to agree on what Agentic AI means by now. But the paper shows there is still a lot of debate. I also found it interesting that planning is so central to this topic. We study planning in class as one concept. But in Agentic AI it is everything. The AI cannot be truly useful without it. Reading this paper made me realize that AI is not just about smart answers. It is about smart actions.
Conclusion
Agentic AI is more than a trend. It is a shift in how we think about what AI can do. Instead of tools that respond they become systems that act. The 2025 paper helps us understand where this field stands and what still needs work. As AI students it is important to follow this area. It will shape the future of the technology we are learning to build.
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