INTEGRATING AUTONOMY AND COLLABORATION: DISTINGUISHING AGENTIC AI SYSTEMS FROM AI AGENTS
Keywords:
AI Agents, Agentic AI, Large Language Models (LLM), Dynamic Reasoning, Autonomous System, Retrieval Augmented Generation (RAG), Ethical AI, Human AI Collaboration, Scalable Intelligent SystemAbstract
The evolution of AI has brought forth two interlinked paradigms: AI agents, which execute predefined, task-specific actions, and agentic AI systems, which coordinate multiple agents to autonomously set goals, plan, and act within dynamic environments. This article offers a comparative overview—covering architecture, interaction models, and applications in domains such as healthcare, robotics, business automation, and digital ecosystems—while addressing challenges like hallucination, coordination failures, and accountability. We discuss mitigation strategies including ReAct loops, retrieval-augmented generation (RAG), and causal modeling, and explore governance and ethical implications. Our contribution is a unified framework that aligns terminology, bridges theoretical and practical gaps, and guides the development of scalable, transparent, and goal-oriented intelligent systems.
