# AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenge

**Authors:** Ranjan Sapkota, Konstantinos I. Roumeliotis, Manoj Karkee  
**arXiv:** [2505.10468](https://arxiv.org/abs/2505.10468)  
**Date:** 2025-05-15  
**Summary page:** https://www.summarizepaper.com/en/paper/2505.10468v1/

## Key points

- The study distinguishes between AI Agents and Agentic AI, highlighting their divergent design philosophies and capabilities.
- AI Agents are defined as modular systems powered by Large Language Models (LLMs) and Large Image Models (LIMs for narrow, task-specific automation.
- Generative AI is identified as a precursor to AI Agents, evolving through tool integration, prompt engineering, and reasoning enhancements.
- Agentic AI represents a paradigm shift characterized by multi-agent collaboration, dynamic task decomposition, persistent memory, and orchestrated autonomy.
- Application domains such as customer support, scheduling, data summarization are contrasted with deployments in research automation, robotic coordination, and medical decision support.
- Challenges unique to each paradigm include hallucination, brittleness emergent behavior,and coordination failure. Proposed solutions include ReAct loops,RAG models orchestration layers,and causal modeling.
- The emergence of Agentic AI is discussed as a transformative evolution from isolated agents to orchestrated ecosystems with distributed cognition,persistent memory,and coordinated planning distinguishing it from conventional agent models.
- Architectural evolution from monolithic rule-based frameworks to modular role-specialized networks facilitated by orchestration layers reflective memory architectures is highlighted.
- Application domains for both paradigms showcase the role of Agentic AI in automating customer support,internal enterprise search,email prioritization,and scheduling while demonstrating use cases for AI Agents in collaborative research, robotics, medical decision support,and adaptive workflow automation.
- Challenges affecting both paradigms such as hallucinations, shallow reasoning, planning constraints for Agentic AI amplified causality issues coordination bottlenecks emergent behavior,and governance concerns for are analyzed providing insights for future development and deployment of trustworthy scalable agentic systems. Solutions include recent advances in causal modeling,retrieval-augmented generation (RAG),multi-agent memory frameworks,and robust evaluation pipelines essential for scaling agentic systems into high-stakes domains like healthcare finance and autonomous robotics.

## Summary

This comprehensive study delves into the distinction between AI Agents and Agentic AI, providing a structured conceptual taxonomy, application mapping, and challenge analysis to elucidate their divergent design philosophies and capabilities. The research begins by outlining a search strategy and foundational definitions. are defined as modular systems powered by Large Language Models (LLMs) and Large Image Models (LIMs) for narrow, task-specific automation. Generative AI is identified as a precursor to AI Agents, which evolve through tool integration, prompt engineering, and reasoning enhancements. In contrast, systems represent a paradigm shift characterized by multi-agent collaboration, dynamic task decomposition, persistent memory, and orchestrated autonomy. Through a sequential evaluation of architectural evolution, operational mechanisms, interaction styles, and autonomy levels across both paradigms are compared. Application domains such as customer support,scheduling,data summarization are contrasted with  deployments in research automation, robotic coordination,and medical decision support. The study further explores challenges unique to each paradigm including hallucination,brittleness emergent behavior,and coordination failure. Proposed solutions include ReAct loops,RAG models orchestration layers,and causal modeling. The emergence of  is discussed as a transformative evolution from isolated agents to orchestrated ecosystems with distributed cognition,persistent memory,and coordinated planning distinguishing it from conventional agent models. Architectural evolution from monolithic rule-based frameworks to modular role-specialized networks facilitated by orchestration layers reflective memory architectures is highlighted. Application domains for both paradigms are surveyed showcasing the role of  in automating customer support,internal enterprise search,email prioritization,and scheduling while demonstrating use cases for  in collaborative research,robotics,medical decision support,and adaptive workflow automation. Challenges affecting both paradigms such as hallucinations,shallow reasoning,planning constraints for ,amplified causality issues,coordination bottlenecks,emergent behavior,and governance concerns for  are analyzed providing insights for future development and deployment of trustworthy scalable agentic systems. Solutions include recent advances in causal modeling,retrieval-augmented generation (RAG),multi-agent memory frameworks,and robust evaluation pipelines essential for scaling agentic systems into high-stakes domains like healthcare,finance,and autonomous robotics. Overall,this study offers a methodological structure enabling a systematic assessment of the state of  and ,providing theoretical clarity and practical guidance for researchers and practitioners navigating this rapidly evolving field.