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

Ranjan Sapkota, Konstantinos I. Roumeliotis, Manoj Karkee

arXiv:2505.10468v5cs.AI

TL;DR

Existing surveys often conflate AI Agents and Agentic AI, leaving their design principles and capabilities conceptually ambiguous. This review develops a structured taxonomy and concludes that AI Agents suit narrow, tool-integrated tasks, while Agentic AI represents a shift toward distributed, multi-agent cognition.

  • Problem

    Existing surveys often conflate AI Agents and Agentic AI, leaving a gap in distinguishing their design principles and capabilities.

    Source: §1 · p. 3

  • Method

    The review synthesizes literature into a taxonomy comparing AI Agents and Agentic AI across foundational concepts, architectural evolution, applications, limitations, and potential solutions.

    Source: §7 · p. 25

  • Results

    The review concludes that AI Agents suit narrow, tool-integrated scenarios with defined goals, whereas Agentic AI shifts toward distributed multi-agent cognition with autonomous planning and decision-making.

    Source: §7 · p. 25

  • Takeaways & Limitations

    The taxonomy supports matching modular, tool-assisted tasks to AI Agents and orchestrated multi-agent operations to Agentic AI, which have different benchmarking, safety, and resource requirements.

    Source: §1 · p. 2

    LLM hallucination and weak causal reasoning remain concerns, while Agentic AI adds emergent behavior, coordination complexity, and opaque reasoning that can make outputs unpredictable or unexplainable.

    Source: §5.1 · p. 22

Abstract

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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 LLMs and LIMs 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 (RAG), automation coordination layers, and causal modeling. This work aims to provide a roadmap for developing robust, scalable, and explainable AI-driven systems.

1. Introduction

The review distinguishes modular, single-agent AI Agents from coordinated Agentic AI and presents a structured taxonomy to clarify their design, evaluation, and deployment. It traces their historical and technical progression and outlines the review’s search strategy, applications, challenges, and potential solutions.

1. Introduction

  • Classical agents relied on predefined rules and symbolic reasoning, with limited autonomy and adaptability; examples ranged from expert systems to scripted dialogue and reactive robotics.Earlier MAS and expert-system traditions emphasized autonomy, perception, communication, and distributed problem-solving, while social-action concepts informed cooperation and organizational behavior.Source: §1 · pp. 1–2
  • After ChatGPT’s November 2022 release, systems evolved from generative models to tool-using AI Agents and then to Agentic AI, where specialized agents coordinate and divide tasks.AI Agents use tools, sequential reasoning, and real-time information for defined tasks, whereas Agentic AI coordinates specialized agents toward shared goals.Source: §1 · p. 2
  • The review addresses survey-driven conceptual ambiguity by formalizing a taxonomy that separates single-entity AI Agents from orchestrated Agentic AI across autonomy, coordination, interaction, and reasoning scope.The distinction is intended to align system design and evaluation with task complexity and reduce both under-engineering of coordinated tasks and over-engineering of simpler ones.Source: §1 · pp. 2–3

1.1. Methodology overview

  • The review proceeds from agent definitions and model foundations to Agentic AI, comparing architectures, applications, limitations, and proposed solutions.Its framework covers components such as perception, reasoning, and action selection, and examines LLMs’ roles in interaction and planning alongside limitations including hallucination and weak causal reasoning.Source: §1.1 · pp. 3–4
  • The review maps four application areas per paradigm, from customer support and enterprise search for AI Agents to research assistants and robotics coordination for Agentic AI.It examines task complexity, real-time decision-making, and collaborative execution, then considers paradigm-specific challenges and solutions including RAG, causal modeling, multi-agent memory, and robust evaluation.Source: §1.1 · p. 4

1.1.1. Search strategy

  • The search strategy queried twelve academic databases and AI-powered interfaces using Boolean combinations of terms related to agents, tool-augmented LLMs, and multi-agent systems.Targeted queries addressed coordination, planning, tool use, and reasoning; inclusion considered novelty, empirical evaluation, architectural contribution, and citation impact.Source: §1.1.1 · p. 4

2. Foundational understanding of AI Agents

The section defines AI Agents as bounded, goal-directed systems and explains how they extend generative models with model-based reasoning and tool use for adaptive task execution. It identifies their core design traits, enabling models, and progression toward more capable workflows.

2. Foundational understanding of AI Agents

  • AI Agents perceive inputs, reason over context, and act toward goals in bounded environments, differing from deterministic scripts through reactive adaptability.Claude’s Computer Use project illustrates an agent loop: it selects an action, observes the result, and repeats until the task is complete.Source: §2 · p. 4

2.0.1. Core characteristics of AI Agents

  • AI Agents combine autonomy, task-specificity, and reactivity, serving as lightweight interfaces between pretrained models and domain-specific utility pipelines.They can operate with minimal oversight on narrow tasks and adapt to real-time inputs; their simplicity and efficiency support scalable automation, though studies with specialized reasoning LLMs are not yet reported.Source: §2.0.1 · pp. 4–5, Figure 4 · p. 5

2.0.2. Foundational models: The role of LLMs and LIMs

  • LLMs provide language understanding and decision-making, while LIMs add visual perception for agents working in robotics, autonomous vehicles, and content moderation.In the orchard example, a drone uses imagery to identify diseased fruits or damaged branches and trigger targeted interventions.Source: §2.0.2 · pp. 5–6

2.0.3. Generative AI as a precursor

  • Generative AI is a reactive precursor: without scaffolding, it lacks autonomous tool use, persistent state, feedback loops, and multi-step planning.AI Agents add memory buffers, tool-calling APIs, reasoning chains, and planning routines, shifting systems from content generation toward task execution.Source: §2.0.3 · p. 6

2.1. Language models as the engine for AI Agent progression

  • Repurposing language models as reasoning engines supports agents that plan adaptively, make real-time decisions, and respond to their environments.This progression embeds models such as GPT-3, Llama [119], T5, Baichuan 2, and GPT3mix [122] in dynamic agent frameworks.Source: §2.1 · p. 6

2.1.1. LLMs as core reasoning components

  • In agent systems, LLMs interpret goals, evaluate plans, select strategies, use tools, and manage multi-step workflows.AutoGPT [38] and BabyAGI use GPT-4 to decompose objectives into subtasks, invoke APIs, and monitor progress in a feedback loop.Source: §2.1.1 · p. 6

2.1.2. Tool-augmented AI Agents: Enhancing functionality

  • Tool-augmented agents connect external tools and APIs to model reasoning, enabling real-time information access, code execution, and interaction with dynamic data.The ReAct framework alternates reasoning with tool use and integrates returned results into the model’s context to update task status and choose next steps.Source: §2.1.2 · pp. 6–7

2.1.3. Illustrative examples and emerging capabilities

  • Tool-augmented agents support market analysis, software development, and literature-grounded research, while introducing coordination complexity, error propagation, and context-window limits.Examples include AutoGPT [38], GPT-Engineer [138,139], and Paper-QA [140]; tool integration also enables long-horizon planning and cross-tool coordination while setting the stage for persistent memory and scalable autonomy.Source: §2.1.3 · p. 7

3. The emergence of Agentic AI from AI Agent foundations

The section frames Agentic AI as a response to single-agent limits in complex, dynamic, cooperative work, extending tool-using agents into coordinated systems with shared memory, planning, and orchestration.

3. The emergence of Agentic AI from AI Agent foundations

  • Agentic AI emerges because tool-augmented single agents struggle with complex, dynamic, multistep, or cooperative scenarios, prompting collaboration among goal-directed entities.The emerging systems coordinate through structured communication, shared memory, and dynamic role assignment.Source: §3 · p. 7

3.0.1. Conceptual leap: From isolated agents to coordinated systems

  • Agentic AI shifts from isolated, reactive task execution to specialized agents that decompose goals, coordinate subtasks, and adapt through inter-agent collaboration.Agents can sequence and redistribute tasks as conditions change or failures occur, using communication channels and memory to preserve context and refine strategies.Source: §3.0.1 · p. 7

3.0.2. Key differences between AI Agents and Agentic AI

  • The taxonomy distinguishes prompt-driven generation, bounded tool-using agents, and orchestrated Agentic AI, while placing Generative Agents as modular subtask contributors.It compares the paradigms across function, architecture, operation, task scope, and autonomy, tracing a progression toward coordinated workflows and persistent state.Source: §3.0.2 · pp. 8–9

3.1. Architectural evolution: From AI Agents to Agentic AI systems

  • Agentic architectures retain perception, reasoning, and action while adding specialized agents, advanced planning, persistent memory, and orchestration for distributed, goal-driven operation.The architectural progression also introduces shared context, system coordination, and task decomposition.Source: §3.1 · p. 10

3.1.1. Core architectural components of AI Agents

  • Traditional AI Agents use a perception–reasoning–action–learning loop, translating inputs into decisions and tool actions with comparatively limited memory and adaptation.Customization can use domain-specific prompts, rules, or workflow templates; ReAct iterates reasoning and action before selecting external actions.Source: §3.1.1 · pp. 10, 12

3.1.2. Architectural enhancements in Agentic AI

  • Agentic AI adds specialized role-bound agents, iterative planning, persistent memory, and orchestration layers that coordinate dependencies and resolve conflicts.These capabilities support sustained context, distributed work, and strategic adaptation; systems can construct and revise complex objectives with minimal human supervision.Source: §3.1.2 · p. 13

4. Application of AI Agents and Agentic AI

The review maps four application domains for conventional AI Agents and four for Agentic AI, distinguishing task-focused automation from collaborative, dynamic systems. It illustrates these domains through examples spanning enterprise support, scheduling, research, robotics, healthcare, and adaptive workflows.

4.0.1. Application of AI Agents

  • AI Agents use retrieval-augmented LLMs and enterprise APIs for customer support and internal search, handling queries, ticket triage, actions, and document retrieval.A single system can retrieve shipping or policy information for customers and semantically search enterprise documents for employees.Source: §4.0.1 · p. 13
  • AI Agents triage email using metadata, semantics, and feedback, personalize recommendations from behavior, and turn natural-language analytics requests into queries and reports.Email systems can learn user preferences, while recommendation and reporting systems use interaction patterns, ranking, and structured database queries.Source: §4.0.1 · pp. 13–14
  • Scheduling agents use calendar APIs and learned preferences to find or propose meeting times across participants and time zones, reducing coordination overhead and improving scheduling efficiency.They can detect conflicts, suggest alternatives, and notify attendees through connected tools.Source: §4.0.1 · pp. 14–15

4.0.2. Appications of Agentic AI

  • Agentic research assistants coordinate specialized agents and persistent memory for collaborative literature reviews and proposal drafting [98], while robot teams share spatial memory and reallocate tasks amid changing conditions [155].Orchestrators manage role dependencies in research pipelines; in orchard robotics, shared maps guide pickers and transporters, with workloads reassigned if a picker fails.Source: §4.0.2 · pp. 15–16
  • Medical decision-support agents distribute diagnostic, history-retrieval, and treatment-planning tasks, but real-world implementations remain limited despite reported potential [174, 175].An orchestrator can integrate recommendations and surface conflicts for clinician review, with feedback stored in persistent memory.Source: §4.0.2 · p. 16
  • Agentic game and workflow systems use goal-directed agents for emergent interaction and specialized modules for resilient, adaptive processes; cybersecurity coordination can reduce decision latency and false positives [176, 98].In the described incident-response workflow, agents classify threats, correlate logs, assess compliance, and model mitigation strategies under an orchestrator.Source: §4.0.2 · p. 16

5. Challenges and limitations in AI Agents and Agentic AI

AI Agents are limited by weak causal reasoning, inherited LLM weaknesses, incomplete autonomy, and brittle long-horizon planning; Agentic AI adds coordination, emergence, security, and governance risks. The section pairs these diagnoses with technical and governance strategies while noting gaps in standardization, evaluation, and real-world validation.

5. Challenges and limitations in AI Agents and Agentic AI

  • The section’s comparative framework identifies four AI Agent limitations and eight Agentic AI bottlenecks, then proposes design strategies to address them.Fig. 12 summarizes the challenge categories, while Fig. 13 gathers strategies including RAG, tool-based reasoning, ReAct feedback loops, multi-agent orchestration, memory architectures, causal modeling, and governance-aware design.Source: §5 · p. 16, Figure 12 · p. 18

5.0.1. Challenges and limitations of AI Agents

  • AI Agents lack reliable causal and counterfactual reasoning, inherit hallucination and prompt brittleness, and remain vulnerable to shallow inference, distribution shifts, latency, and static knowledge.Chain-of-Thought and ReAct heuristics do not ensure logically valid reasoning; absent causal grounding, agents may fail in novel or high-stakes settings.Source: §5.0.1 · pp. 16–18
  • AI Agents exhibit only partial autonomy and weak proactivity, social interaction, and real-time reactivity, while heuristic planning and limited recovery undermine long-horizon reliability and safety.They depend on external scaffolding and narrow memory, may repeat tool calls or fail to adapt when subtasks go wrong, and lack established safety guarantees for open-ended use.Source: §5.0.1 · pp. 19–20

5.0.2. Challenges and limitations of agentic AI

  • In Agentic AI, weak causal reasoning can trigger inter-agent distribution shifts, redundant work, coordination breakdowns, and cascading errors from a single faulty or hallucinated output.Distributed agents also struggle with shared goals, ambiguous communication, resource contention, and the absence of standardized protocols or global scheduling.Source: §5.0.2 · p. 20
  • Agentic AI faces unpredictable emergent behavior, scaling and debugging complexity, weak explainability and verification, and expanded adversarial attack surfaces.Unintended outcomes can include loops, deadlocks, contradictory actions, and harmful behavior; compromised agents or tools can spread corrupted state through workflows lacking robust security controls.Source: §5.0.2 · pp. 20–21
  • Agentic AI also faces accountability gaps, bias amplification, value drift, ad hoc architectures, and unresolved causal foundations that impede reliable generalization.The section calls for governance-aware designs and foundational research in multi-agent theory, causal inference, and benchmarks to move beyond fragile prototypes.Source: §5.0.2 · p. 21

5.1. Balanced critique and field-wide limitations

  • Field-wide critiques include surveillance and consent concerns around persistent memory, artificial benchmarks, limited empirical validation, and nonstandard orchestration that impairs interoperability and reproducibility.The section argues for transparent evaluation metrics, governance mechanisms, and balanced attention to functional robustness and ethical integrity.Source: §5.1 · p. 22

6. Potential solutions and future roadmap

The section proposes technical solutions to improve agent reliability, scalability, and coordination, then outlines distinct future directions for AI Agents and Agentic AI, including autonomous self-play learning.

6.1. Potential solutions

  • RAG grounds AI Agent outputs in real-time external data and gives Agentic AI teams a shared semantic layer that reduces inconsistent context and inter-agent misinformation.The passage highlights enterprise search and customer support as settings where current knowledge is essential for accurate, trusted responses.Source: §6.1 · p. 22
  • Function calling lets AI Agents query APIs, scripts, and databases, while orchestrated Agentic AI workflows use specialized tools, validation, and audit mechanisms to coordinate tasks [171,217].Defined roles and schema checks or feedback logs reinforce behavioral boundaries, traceability, and accountability in complex domains,.Source: §6.1 · p. 22
  • ReAct replaces single-pass inference with reason–act–observe iterations, while episodic, semantic, and vector memories support continuity, personalization, and long-horizon planning, [224].Multi-agent systems need shared memory and synchronized updates to reconcile observations and sustain consistent system-level planning.Source: §6.1 · pp. 22–23
  • Role specialization and orchestration divide complex work among agents, improving interpretability, scalability, and fault tolerance; reflexive review and programmatic prompts further support quality and coordination [225, 226].Reflexive mechanisms enable self-critique and inter-agent evaluation,, while automated prompt pipelines reduce brittleness and coordination drift [233], [171].Source: §6.1 · p. 23
  • Causal models and simulation support counterfactual planning and safer coordination, while monitoring, explainability, and governance architectures enable auditing, accountability, and role-bounded action.The roadmap notes that deployed systems still rely heavily on heuristic wrappers, manual prompts, and shallow coordination, and calls for mature, interoperable components.Source: §6.1 · pp. 22, 24

6.2. Future roadmap

  • The AI Agent roadmap emphasizes proactive reasoning, tool integration, causal inference, continual learning, and trust mechanisms to enable adaptive but controllable operation [s27.p1#6e0cd6].Trust measures include verifiable output logs, bias detection, and ethical guardrails as autonomy increases.Source: §6.2 · p. 24
  • The Agentic AI roadmap centers on scaling specialized agents through unified orchestration, persistent memory, simulation planning, ethical governance, and domain-specific systems.These capabilities are intended to coordinate roles and dependencies, sustain goal awareness, reduce real-world risk, and support sectors including law, medicine, logistics, and climate science.Source: §6.2 · p. 24
  • AZR [173] proposes self-play learning without external datasets, letting agents generate, validate, and solve tasks using verifiable feedback, then co-evolve through shared objectives.In orchestrated research pipelines, agents could propose hypotheses, run simulations, validate findings, and revise strategies without continuous human oversight [173].Source: §6.2 · pp. 24–25

7. Conclusion

The study’s comparative taxonomy distinguishes task-specific AI Agents from Agentic AI ecosystems, which coordinate specialized agents for autonomous planning and decision-making. It maps their architectures, applications, limitations, and development implications.

Conclusion

  • AI Agents are modular, task-specific entities with constrained autonomy and reactivity, using LLMs and LIMs for perception, language understanding, and decision-making.Generative AI is described as their functional precursor, limited in autonomy and goal persistence; tool augmentation advances LLMs from passive generation toward interactive task completion.Source: §7 · p. 25
  • Agentic AI coordinates specialized agents through role assignment, task delegation, and centralized or distributed control, with persistent memory and planning enabling adaptive collaboration and context preservation.Orchestration layers manage tasks, interactions, and dependencies as architectures shift from monolithic rule-based frameworks to modular, role-specialized networks.Source: §7 · p. 25
  • AI Agent applications include customer support, enterprise search, email prioritization, and scheduling, while Agentic AI examples include collaborative research, swarm robotics, medical decision support, and adaptive workflow automation.Source: §7 · p. 25
  • AI Agents face hallucinations, shallow reasoning, and planning constraints, while Agentic AI faces amplified causality issues, coordination bottlenecks, emergent behavior, and governance concerns.Source: §7 · p. 25
  • AI Agents suit narrow, tool-integrated scenarios with defined goals, while Agentic AI shifts toward distributed, multi-agent cognition and autonomous planning and decision-making.The study presents this transition from reactive task execution to orchestrated collaboration as a milestone and a roadmap toward trustworthy, scalable systems adaptable to complex environments.Source: §7 · p. 25

CRediT authorship contribution statement

The statement delineates each of the three authors’ contributions across writing, methods, analysis, and project responsibilities.

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