Agentic AI and Smarter Search Algorithms: A Research Reflection on AI and A* Search

24P-0671 Muhammad Hamza Shahzad — Tue, 05 May 2026 12:08:44 +0000

Agentic AI represents a new evolution in artificial intelligence where systems behave like autonomous agents.

These systems are not limited to generating responses but can plan, reason, and take actions toward defined goals.

This shift makes AI more dynamic and closer to human-like problem-solving.

Agentic AI refers to AI systems that can independently make decisions.

They break down tasks into steps and execute them using tools or reasoning.

Unlike traditional AI, they continuously adapt based on feedback and environment changes.

This makes them suitable for complex workflows like automation, research, and decision systems.

Search algorithms are fundamental to problem-solving in computer science.

They help explore possible solutions and find the most optimal path.

Common examples include BFS, DFS, and heuristic-based methods like A* search.

These algorithms provide structure and efficiency to AI decision-making.

A* is one of the most efficient pathfinding algorithms.

It uses the formula: f(n) = g(n) + h(n)

g(n): actual cost from start

h(n): estimated cost to goal

By combining both, A* finds the shortest and most efficient path.

It is widely used in navigation systems, games, and robotics.

Agentic AI uses search strategies like A* to improve decision-making.

Instead of random reasoning, agents evaluate multiple paths before acting.

This improves accuracy, speed, and resource optimization.

It allows AI systems to plan multi-step solutions intelligently.

The combination of Agentic AI and smarter search algorithms represents a major step in AI evolution.

It connects classical computer science with modern intelligent systems.

Together, they enable AI to move from prediction-based models to goal-oriented autonomous systems.

Forem: 24P-0671 Muhammad Hamza Shahzad