BRINGING AUTONOMY AND COOPERATION TOGETHER: A COMPARISON OF AGENTIC AI SYSTEMS AND AI AGENTS

Abstract

The rapid evolution of artificial intelligence has led to the emergence of two distinct but interdependent paradigms: AI agents and agent-based AI systems. While AI agents focus on modular and task-specific automation, often powered by large language models (LLMs), agentic AI systems represent a conceptual leap by enabling multi-agent collaboration, dynamic reasoning, and persistent autonomy. This article presents a comparative analysis that draws from both theoretical and practical perspectives, integrating the ideas of two fundamental works in the field. We define and differentiate the architectures, interaction models, and design objectives of each paradigm, examining their application in areas such as health, robotics, business automation, and digital ecosystems. The main challenges, such as hallucination, lack of coordination, and accountability, are identified along with mitigation strategies such as ReAct loops, retrieval-augmented generation (RAG), and causal modeling. Furthermore, we analyze the governance, ethical implications, and industry restructuring triggered by agent-based technologies. Our contribution is a unified framework and roadmap that clarifies terminology, aligns capabilities with real-world complexity, and informs the development of robust, transparent, and scalable intelligent systems. This synthesis offers valuable guidance to researchers, policymakers, and industry leaders who are navigating the transition from automated tools to collaborative intelligent agents.