AI Agents and Agentic AI: A Comparative Review of Architectures, Working Mechanisms, Applications, and Future Research Directions

This research paper presents a comparative review of AI agents and Agentic AI based on three existing survey papers. It explains the fundamental components of AI agents, including reasoning, planning, memory, tool integration, feedback, and decision-making. The paper discusses how AI agents work, their applications in education, healthcare, software development, business operations, and robotics, and the differences between single-agent and multi-agent systems. It also highlights key challenges such as hallucinations, security risks, reliability, coordination, and computational costs. Finally, it explores future research directions focused on improving agent accuracy, safety, efficiency, and human oversight.