Agentic Artificial Intelligence and Artificial General Intelligence: Emerging Paradigms, Technological Foundations, and Future Implications

Artificial intelligence has progressed rapidly from rule-based expert systems to machine learning, deep learning, and large language models capable of performing complex cognitive tasks. Recent developments have introduced two closely related paradigms that are expected to shape the future trajectory of intelligent computing: Agentic Artificial Intelligence (Agentic AI) and Artificial General Intelligence (AGI). While contemporary artificial intelligence systems primarily function as reactive tools that respond to user instructions, Agentic AI introduces autonomous reasoning, long-term planning, adaptive decision-making, and goal-directed behavior that enables computational systems to independently execute complex workflows. Artificial General Intelligence extends this vision further by representing a form of intelligence capable of understanding, learning, reasoning, and adapting across diverse domains with human-level or potentially superhuman cognitive flexibility. This preprint examines the conceptual evolution of Agentic Artificial Intelligence and Artificial General Intelligence by exploring their technological foundations, operational characteristics, practical applications, and societal implications. The discussion investigates how autonomous agents integrate large language models, reinforcement learning, memory systems, planning algorithms, tool utilization, multi-agent collaboration, and continuous learning to solve increasingly sophisticated problems. The paper also analyzes the relationship between Agentic AI and AGI, emphasizing their similarities, distinctions, and complementary roles within the broader landscape of intelligent computing. Attention is devoted to the potential influence of these technologies across scientific research, healthcare, finance, manufacturing, education, software engineering, cybersecurity, robotics, public administration, and industrial automation. The study further discusses significant challenges associated with safety, alignment, explainability, computational infrastructure, ethical governance, legal accountability, cybersecurity, and responsible deployment. These issues highlight the importance of developing intelligent systems that remain reliable, transparent, and aligned with human values while demonstrating increasing levels of autonomy. The paper concludes that Agentic Artificial Intelligence represents an important intermediate stage in the long-term development of Artificial General Intelligence. Continued advances in reasoning, memory, planning, multimodal learning, embodied intelligence, and human-AI collaboration are expected to reshape scientific research, economic systems, and technological innovation during the coming decades.