Purpose of the Research
The paper titled “The Rise of Agentic AI: A Review of Definitions, Frameworks, and Challenges” examines how artificial intelligence systems are shifting from passive tools to autonomous agents capable of planning and performing tasks.
Traditional AI systems mostly respond to prompts or queries provided by users. In contrast, agentic AI systems are designed to plan a sequence of actions, evaluate different options, and interact with external tools to complete complex tasks. These systems typically combine several components such as reasoning modules, memory storage, and planning mechanisms.
Relation to Course Concepts
During our AI course, we studied the concept of intelligent agents and different types of agent architectures, including:
Simple reflex agents
Model-based agents
Goal-based agents
Utility-based agents
Learning agents
Agentic AI can be seen as an advanced implementation of these ideas. Instead of only reacting to input, these agents can analyze situations, plan strategies, and execute multiple steps to achieve a goal.
My Learning Experience
While reading the research paper manually, I learned how modern AI agents integrate several modules like memory and reasoning to perform more advanced tasks. Some sections were technically complex, so I used NotebookLM to better understand the explanations and summaries.
One aspect I found particularly interesting is how agentic AI systems can break large problems into smaller steps and solve them sequentially, which resembles how humans approach complex taskshttps:
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