The Evolution of Generative and Agentic AI: From Rule-Based Systems to Autonomous Intelligence

The rapid evolution of Artificial Intelligence (AI) has transitioned from rule-based systems to advanced generative and agentic models capable of autonomous decision-making and reasoning. Early AI frameworks relied heavily on symbolic logic and deterministic rules, offering limited adaptability and contextual understanding. With the advent of Machine Learning (ML) and Deep Learning (DL), AI systems gained the ability to learn from data and improve performance autonomously. The emergence of Generative AI (GenAI), powered by Large Language Models (LLMs) such as GPT, BERT, and Gemini, marked a paradigm shift by enabling contextual creativity, natural language understanding, and multimodal reasoning. Meanwhile, Agentic AI introduces a new dimension — systems capable of goal-driven autonomy, memory-based reasoning, and adaptive interaction with environments and users. This review explores the historical progression of AI technologies, from rule-based inference engines to generative and agentic intelligence, analyzing their architectural evolution, learning mechanisms, ethical challenges, and real-world applications. The paper also discusses the integration of Retrieval-Augmented Generation (RAG), MCP servers, and data-driven reinforcement models as enablers of scalable autonomous intelligence. Finally, future research directions emphasize hybrid intelligence frameworks, human-AI collaboration, and explainable autonomy for safe and responsible AI deployment.