Agentic AI: Vision and challenges

Abstract

Agentic AI systems are increasingly viewed as a viable response to the shortcomings of static, rigid, and human-in-the-loop Artificial Intelligence (AI) systems. This is because autonomous operation enables rapid adaptation to dynamic, complex problems with improved time-critical behaviour under real-world constraints. Despite significant progress, current agentic pipelines are still challenged by output instability, scalability gaps, and system integration issues. Addressing these limitations, this article presents a comprehensive conceptual framework unifying core AI functionality with implementation approaches across different system scales, including Agentic AI builds upon Large Language Models (LLMs). Furthermore, the popular applications of Agentic AI and areas for future investigation and open problems are systematically presented.