COMPARING AUTONOMY AND COLLABORATIVE DYNAMICS IN AGENTIC AI SYSTEMS AND 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 rapid advancement of artificial intelligence has given rise to two closely related yet distinct paradigms: AI agents and agent-based AI systems. AI agents emphasize modular, task-specific automation, often enabled by large language models (LLMs), whereas agent-based systems extend these capabilities through multi-agent collaboration, dynamic reasoning, and persistent autonomy. This article provides a comparative analysis of the two paradigms from both theoretical and practical perspectives, synthesizing insights from foundational research.
We distinguish their architectures, interaction models, and design objectives, and illustrate applications across healthcare, robotics, business automation, and digital ecosystems. Key challenges—including hallucination, coordination gaps, and accountability—are discussed alongside mitigation strategies such as ReAct loops, retrieval-augmented generation (RAG), and causal modeling. Beyond technical considerations, the study examines governance, ethical dimensions, and the broader restructuring of industries driven by agent-based technologies.
Our contribution lies in proposing a unified framework and roadmap that clarifies terminology, aligns capabilities with real-world complexity, and supports the development of transparent, scalable, and reliable intelligent systems. This synthesis provides actionable guidance for researchers, policymakers, and industry leaders navigating the transition from automated tools to collaborative intelligent agents.
