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

AI Agents versus Agentic AI: A Conceptual Taxonomy, Applications and Challenges

Abstract-This review critically distinguishes between AI Agents and Agentic AI, offering a structured, conceptual taxonomy, application mapping, and analysis of opportunities and challenges to clarify their divergent design philosophies and capabilities. We begin by outlining the search strategy and foundational definitions, characterizing AI Agents as modular systems driven and enabled by large language models and large information models for task-specific automation. Generative AI is positioned as a precursor providing the foundation, with AI agents advancing through tool integration, prompt engineering, and reasoning enhancements. We then characterize Agentic AI systems, which, in contrast to AI Agents, represent a paradigm shift marked by multi-agent collaboration, dynamic task decomposition, persistent memory, and coordinated autonomy. Through a chronological evaluation of architectural evolution, operational mechanisms, interaction styles, and autonomy levels, we present a comparative analysis across both AI agents and agentic AI paradigms. Application domains enabled by AI Agents such as customer support, scheduling, and data summarization are then contrasted with Agentic AI deployments in research automation, robotic coordination, and medical decision support. We further examine unique challenges in each paradigm including hallucination, brittleness, emergent behavior, and coordination failure, and propose targeted solutions such as ReAct loops, retrieval-augmented generation, automation coordination layers, and causal modeling. This work aims to provide a roadmap for developing robust, scalable, and explainable AI-driven systems.

One. INTRODUCTION

Prior to the widespread adoption of AI Agents and Agentic AI around twenty twenty-two, the development of autonomous and intelligent agents was deeply rooted in foundational paradigms of artificial intelligence,

particularly multi-agent systems and expert systems, which emphasized social action and distributed intelligence. Notably, Castelfranchi laid the critical groundwork by introducing ontological categories for social action, structure, and mind, arguing that sociality emerges from individual agents' actions and cognitive processes in a shared environment, with concepts like goal delegation and adoption forming the basis for cooperation and organizational behavior. Similarly, Ferber provided a comprehensive framework for multi-agent systems, defining agents as entities with autonomy, perception, and communication capabilities, and highlighting their applications in distributed problem-solving, collaborative robotics, and synthetic world simulations.

These early studies established that individual social actions and cognitive architectures are fundamental to modeling collective phenomena, setting the stage for modern AI Agents. This paper builds on these foundational concepts to explore how social action modeling, as proposed in, informs the design of AI Agents capable of complex, socially intelligent interactions in dynamic environments.

Classical Agent-like systems were designed to perform specific tasks with predefined rules, which offered limited autonomy, and minimal adaptability to dynamic environments. These systems were primarily reactive or deliberative, relying on symbolic reasoning, rule-based logic, or scripted behaviors rather than the learning-driven, context-aware capabilities of modern AI Agents. For instance, expert systems used knowledge bases and inference engines to emulate human decision-making in domains like medical diagnosis. Other notable examples include DENDRAL, an expert system for molecular structure prediction; XCON, used for computer system configuration; and CLIPS, a rule-based production system framework. Systems like SOAR and the subsumption architecture extended symbolic and reactive logic into cognitive modeling and robotics.

In addition to task-specific reasoning, these agents supported limited forms of social interaction. Early conversational systems like ELIZA and PARRY simulated basic dialogue through pattern matching and script-based responses but lacked genuine understanding or contextual adaptation. Similarly, reactive agents in robotics executed sense-act cycles based on fixed control rules, as seen in early autonomous platforms like the Stanford Cart.

Multi-agent systems facilitated coordination among distributed entities, exemplified by auction-based resource allocation in supply chain management. Scripted AI in video games, like NPC behaviors in early RPGs, used predefined decision trees. Furthermore, Belief- Desire-Intention architectures enabled goal-directed behavior in software agents, such as those in air traffic control simulations.

However, across these diverse systems, early AI agents shared common limitations: they lacked self-learning, generative reasoning, and adaptability to unstructured or evolving environments. These shortcomings distinguish them from Agentic AI a recent paradigm that builds on deep learning, reinforcement learning, and foundation models to enable agents with contextual awareness, continuous learning, and emergent autonomy.

Recent public, academic and industry interest in AI Agents and Agentic AI reflects this broader transition in system capabilities. As illustrated in Figure one, Google Trends data demonstrates a significant rise in global search for both terms following the emergence of large-scale generative models in late twenty twenty-two. This shift is closely tied to the evolution of agent design from the pre-twenty twenty-two era, where AI Agents operated in constrained, rule-based environments, to the post-large language model period marked by learning-driven, flexible/adaptable architectures. These newer systems enable agents to refine their performance over time and interact autonomously with unstructured, dynamic inputs. For instance, while pre-modern expert systems required manual updates to static knowledge bases, modern agents leverage emergent neural architectures to generalize across tasks. The surge in trend activity reflects growing awareness of this technological leap, as researchers and practitioners seek tools that go beyond automation toward autonomy and general-purpose reasoning. Moreover, applications are no longer confined to narrow domains like simulations or logistics, but now extend to broad practical settings demanding real-time reasoning and adaptive control. This momentum, as visualized in Figure one, highlights the significance of recent architectural advances in scaling autonomous agents for real-world deployment.

The release of ChatGPT in November twenty twenty-two marked a pivotal inflection point in the development and public perception of artificial intelligence, catalyzing a global surge in adoption, investment, and research activity. In the wake of this breakthrough, the AI landscape underwent a rapid transformation, shifting from the use of standalone large language models toward more autonomous, task-oriented frameworks. This evolution progressed through two major post-generative phases: AI Agents and Agentic AI. Initially, the widespread success of ChatGPT popularized Generative Agents, which are large language model-based systems designed to produce novel outputs such as text, images, and code from user prompts. These agents were quickly adopted across applications ranging from conversational assistants and content-generation platforms to creative tools, revolutionizing domains like digital design,

marketing, and software prototyping throughout twenty twenty-three and beyond.

Although the term AI Agent was first introduced in nineteen ninety-eight, it has since evolved significantly with the rise of generative AI. Building upon this generative foundation, a new class of systems commonly referred to as AI Agents has emerged. These agents enhanced LLMs with capabilities for external tool use, function calling, and sequential reasoning, enabling them to retrieve real-time information and execute multi-step workflows autonomously. Example frameworks such as AutoGPT and BabyAGI highlight this transition, showcasing how LLMs could be embedded within feedback loops to dynamically plan, act, and adapt in goal-driven environments. By late twenty twenty-three, the field had advanced further into the realm of Agentic AI complex, multi-agent systems in which specialized agents collaboratively decompose goals, communicate, and coordinate toward shared objectives. In line with this evolution, Google introduced the Agent-to-Agent protocol in twenty twenty-five, a proposed standard designed to enable seamless interoperability among agents across different frameworks and vendors. The protocol is built around five core principles: embracing agentic capabilities, building on existing standards, securing interactions by default, supporting long-running tasks, and ensuring modality agnosticism. These guidelines aim to lay the groundwork for a responsive, scalable agentic infrastructure.

Architectures such as CrewAI demonstrate how these agentic frameworks can accomplish decision-making across distributed roles, facilitating intelligent behavior in high-stake applications including robotics, logistics management, and adaptive decision-support.

As the field progresses from Generative Agents toward increasingly autonomous systems of Agentic AI, it becomes critically important to delineate the technological and conceptual boundaries between AI Agents and Agentic AI. While both paradigms build upon LLMs and extend the capabilities of generative systems, they embody fundamentally different architectures, interaction models, and levels of autonomy. AI Agents are typically designed as single-entity systems that perform goal-directed tasks by utilizing external tools, applying sequential reasoning, and integrating real-time information to complete well-defined functions. In contrast, Agentic AI systems are composed of multiple, specialized agents that coordinate, communicate, and dynamically allocate sub-tasks within a broader workflow to achieve a common goal(s). This architectural distinction highlights clear and significant differences in scalability, adaptability, and application scope.

Understanding and formalizing the taxonomy between these two paradigms, AI Agents and Agentic AI, is scientifically significant for several reasons. First, it enables more precise system design by aligning computational frameworks with problem complexity ensuring that AI Agents are deployed for modular, tool-assisted tasks, while Agentic AI is employed for orchestrated multi-agent operations. Moreover, it allows for appropriate benchmarking and evaluation: performance metrics, safety protocols, and resource requirements differ substantially between agents designed for carrying out individual tasks and system of distributed agents designed for accomplishing complex, coordinated tasks. Additionally, clear taxonomy reduces development inefficiencies by preventing the misapplication of design principles such as assuming inter-agent collaboration in a system designed for single-agent execution. Without this clarity, developers and practitioners risk both under-engineering complex scenarios that require agentic coordination and over-engineering simple applications that could be solved with a single AI Agent.

This article aims to clarify the differences between AI Agents and Agentic AI, providing researchers with a foundational understanding of these technologies. The objective of this study is to formalize the distinctions, establish a shared vocabulary, and provide a structured taxonomy between AI Agents and Agentic AI that informs the next generation of intelligent agent design across academic and industrial domains.

This review also provides a comprehensive conceptual and architectural analysis of the progression from traditional AI Agents to emergent Agentic AI systems. Rather than organizing the study around formal research questions common in review articles, we adopt a sequential, layered structure that clearly lays out the historical and technical evolution of these paradigms. Beginning with a detailed description of our search strategy and selection criteria, we first establish the foundational understanding of AI Agents by analyzing their defining attributes, such as autonomy, reactivity, and tool-based execution. We then explore the critical role of foundational models, specifically LLMs and Large Image Models, which serve as the core reasoning and perceptual engines that drive agentic behavior. Subsequent sections examine how generative AI systems have served as precursors to more dynamic, interactive agents, setting the stage for the emergence of Agentic AI. Through this perspective, we examine and present the conceptual leap from isolated, single-agent systems to orchestrated multi-agent architectures, highlighting their structural distinctions, coordination strategies, and collaborative mechanisms. We further map the architectural evolution by analyzing the core system components of both AI Agents and Agentic AI, offering comparative description of their planning, memory, orchestration, and execution layers. Building on this foundation, we review application domains spanning customer support, healthcare, research automation tasks, and robotics, while categorizing real-world deployments by system capabilities and coordination complexity. We then assess key challenges faced by both paradigms including hallucination, limited reasoning depth, causality deficits, scalability issues, and governance risks. To address these limitations, we outline opportunities for emerging solutions such as retrieval-augmented generation, tool-based reasoning, memory architectures, and simulation-based planning. The review concludes with a forward-looking roadmap that envisions the convergence of modular AI Agents and orchestrated Agentic AI in mission-critical domains such as autonomous vehicles, finance, and healthcare, and beyond. We aim to provide researchers with a structured taxonomy and actionable insights to guide the design, deployment, and evaluation of next-generation agentic AI systems.