Conceptual Foundations of LLM-Powered Agents: From Language Processing to Autonomous Reasoning

Large language models (LLMs) have transformed artificial intelligence by enabling advanced natural language processing and generation. However, their evolution toward autonomous agents require additional capabilities, including structured reasoning, adaptive learning, and interaction with external environments. This article explores the progression from early NLP models to modern LLM-based agents, detailing their core mechanisms, key capabilities, and applications. Additionally, we discuss the challenges in developing fully autonomous AI systems, such as alignment issues, computational efficiency, and decision-making constraints. Finally, we examine future trends, including reinforcement learning integration, enhanced memory architectures, and humanAI collaboration, which will shape the next generation of intelligent agents.