Bridging the Gap: From Classical Search Theory to the Era of Agentic AI

Abstract This paper is all about the evolution of Artificial Intelligence from foundational problem-solving agents to modern agentic systems. By synthesizing basic search algorithms like A*, Breadth-First Search (BFS), and Iterative Deepening with recent milestones in Large Language Model (LLM)-based search agents, we demonstrate how classical state-space theory provides the essential framework for autonomous intelligence. In this blog, we will analyze the shift from reactive generative models to proactive agents capable of dynamic planning, reflection, and multi-turn reasoning. 1. Introduction: The Agentic Evolution In classical AI theory, a problem-solving agent is defined as one that plans ahead by finding sequences of actions that lead to favorable goals. Traditionally, these agents operated in static, fully observable, and deterministic environments using atomic representations. However, the industry is moving into "The Rise of Agentic AI," a shift toward systems that do not merel