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The concept of Agentic AI, characterized by autonomous systems that can plan, reason, and act to achieve complex goals with minimal human oversight, is at the forefront of AI discourse. While the foundational components are in place, true agentic AI, defined by robust, general-purpose, and reliable autonomous goal pursuit, is not yet a reality. We are currently in an era of "emerging, domain-specific, and often brittle agentic systems." The recent advancements in Large Language Models (LLMs) have acted as a catalyst, enabling new classes of complex automation often labeled "agentic." However, a significant gap exists between this perception and the demonstrable reliability and robustness of current implementations, which struggle with long-term planning, memory, and reasoning. This document provides a detailed analysis of the "great debate," tracing historical foundations, dissecting architectural components, evaluating current examples, outlining roadblocks, and discussing profound ethical and societal implications.