Artificial Intelligence Agents: Architecture, Classifications, Applications, Challenges and Future Research Directions—A Comprehensive Review

Abstract

Artificial Intelligence (AI) agents have emerged as a transformative paradigm in modern computing, extending traditional artificial intelligence systems by enabling autonomous perception, reasoning, planning, learning, and decisionmaking. Recent advances in foundation models, large language models (LLMs), reinforcement learning, and multi-agent systems have significantly accelerated the adoption of AI agents across healthcare, finance, education, cybersecurity, manufacturing, transportation, agriculture, and smart cities. This review synthesizes the current state of AI agent research by examining their conceptual foundations, architectural components, classifications, learning mechanisms, and practical applications. Furthermore, the paper discusses emerging paradigms including autonomous agents, agentic AI, collaborative multi-agent systems, and LLM-powered intelligent assistants. Finally, key challenges including explainability, trustworthiness, safety, privacy, governance, and ethical considerations are critically analyzed, while future research directions are identified to support the development of robust, scalable, and human-centered AI agent ecosystems.