Introduction
Artificial Intelligence is rapidly evolving, especially in the fields of intelligent agents and search algorithms. During my Artificial Intelligence course, I learned fundamental concepts such as agent models, search strategies, and heuristic algorithms.
To explore how these ideas are applied in modern research, I analyzed two recent AI papers:
The Rise of Agentic AI: A Review of Definitions, Frameworks, and Challenges (2025)
Research on the A* Algorithm Based on Adaptive Weights (2025)
In this blog, I summarize the key ideas from these papers and explain how they relate to concepts we study in AI courses.
Understanding Agentic AI
Agentic AI refers to AI systems that behave like autonomous agents capable of making decisions and achieving goals.
These systems can:
Observe their environment
Plan actions
Execute tasks
Adapt to changes
This is closely related to the agent concept in AI, where an agent perceives the environment through sensors and acts through actuators.
Example of an AI Agent
Environment → Sensors → Agent → Actuators → Environment
Examples of agentic systems include:
Autonomous robots
Intelligent assistants
Automated research agents
The paper highlights that future AI systems will move beyond simple response generation and instead operate as goal-driven autonomous agents.
Improving Search with Adaptive A*
The second research paper focuses on improving the A* search algorithm.
A* is widely used in:
Path planning
Navigation systems
Robotics
Game AI
The algorithm evaluates nodes using the function:
f(n) = g(n) + h(n)
Where:
g(n) → cost from the start node
h(n) → heuristic estimate to the goal
This allows the algorithm to find an optimal path efficiently.
Why Improve A*?
In large environments, standard A* may explore many unnecessary nodes.
The research introduces adaptive heuristic weights that dynamically adjust the search strategy.
Benefits:
Faster path finding
Fewer explored nodes
Better performance in dynamic environments
This improvement is particularly useful for robots and autonomous navigation systems.
What I Learned from This Exploration
Reading these papers helped me realize that AI research builds upon fundamental concepts taught in class.
The idea of agentic AI was particularly interesting because it combines planning, reasoning, and decision-making into a single system.
Using tools like NotebookLM also made it easier to analyze complex research papers and understand their key contributions.
Conclusion
Modern AI systems are evolving from simple algorithms into intelligent agents capable of complex reasoning and autonomous decision-making.
The research on agentic AI and adaptive A* demonstrates how foundational AI concepts continue to evolve and influence real-world applications such as robotics, navigation systems, and intelligent assistants.
Understanding these developments helps students bridge the gap between classroom theory and real-world AI innovation.
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