AI's 70-Year Journey: From Hardcoded Rules to Autonomous Agents

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Artificial intelligence has evolved through distinct stages over more than seven decades, beginning with rule-based systems that relied entirely on manually programmed logic. The field progressed through classical machine learning, which identified patterns in labeled data, and then deep learning, which allowed neural networks to automatically extract features from raw inputs. Large Language Models, powered by the 2017 Transformer architecture, marked a shift to general-purpose models capable of handling diverse tasks without retraining. The latest development is agentic AI, where LLMs are equipped with tools and multi-step planning abilities to carry out complex tasks with minimal human intervention. Despite rapid advances, key limitations persist — including hallucination and compounding errors in agents — with reliability remaining the central unsolved challenge.

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