Source-linked AI summary
AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges
Ranjan Sapkota, Konstantinos I. Roumeliotis, Manoj Karkee
TL;DR
The paper addresses ambiguity caused by surveys that conflate AI Agents with Agentic AI. It develops a structured taxonomy and comparative framework, concluding that AI Agents suit narrow tool-integrated tasks whereas Agentic AI supports distributed, multi-agent planning and decision-making, subject to significant coordination and safety limitations.
Problem
Existing surveys often conflate AI Agents and Agentic AI, creating conceptual ambiguity and misaligned system design across autonomy, coordination, interaction, and reasoning scope.
Method
The review synthesizes literature into a structured taxonomy and comparative analysis of architectural evolution, applications, limitations, and potential solutions across both paradigms.
Results
AI Agents serve narrow, tool-integrated scenarios with defined goals, while Agentic AI represents distributed, multiagent cognition capable of autonomous planning and decision-making.
Takeaways & Limitations
The framework provides a roadmap for developing and deploying trustworthy, scalable Agentic AI systems adaptable to complex real-world environments.
Takeaways & Limitations
AI Agents remain constrained by limited causal reasoning, adaptability, robustness, planning, and the absence of safety guarantees for open-ended LLM-powered agents.
Abstract
from arXiv · showhide
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 traces the evolution from foundational agent systems to AI Agents and Agentic AI, while addressing persistent ambiguity between these paradigms. It proposes a structured taxonomy distinguishing modular, tool-assisted agents from orchestrated multi-agent systems.
- Historical foundations: Early agent systems relied on symbolic reasoning, predefined rules, and limited adaptability, although multi-agent systems introduced distributed coordination.Examples included expert systems, reactive robotics, conversational systems, auction-based allocation, and BDI architectures.
- Architectural evolution: The post-2022 rise of generative models accelerated a shift from constrained, rule-based agents toward learning-driven and adaptive architectures.The release of ChatGPT in November 2022 marked a pivotal inflection point in public and research activity.
- AI Agents: AI Agents extend LLMs with external tools, function calling, and sequential reasoning to retrieve information and execute multi-step workflows.Frameworks such as AutoGPT and BabyAGI embed LLMs in feedback loops that plan, act, and adapt.
- Agentic AI: Agentic AI advances toward complex multi-agent systems in which specialized agents decompose goals, communicate, and coordinate across distributed roles.CrewAI exemplifies this approach in applications including robotics, logistics, and adaptive decision support.
- Review approach: The review uses a sequential, layered structure to analyze the historical and technical progression from traditional AI Agents to emergent Agentic AI.Its stated objective is to establish shared vocabulary and guide next-generation agent design.
- Taxonomy and motivation: The proposed taxonomy separates AI Agents and Agentic AI across autonomy, coordination, interaction, and reasoning scope to improve system design and evaluation.The review positions AI Agents as modular single-entity systems and Agentic AI as orchestrated ecosystems with emergent behaviors.
2. Foundational understanding of AI Agents
AI Agents are bounded, goal-directed systems built around perception, reasoning, reactivity, and task-specific autonomy. Their evolution from generative models depends on LLMs and LIMs, tool integration, memory, and planning, but remains constrained by robustness and reasoning limitations.
- Definition and scope: AI Agents are autonomous software entities that perceive inputs, reason over context, and act toward specific objectives within bounded digital environments.They operate as interfaces between pretrained models and domain-specific utility pipelines.
- Core characteristics: The core characteristics of AI Agents are autonomy, task-specificity, and reactivity with adaptation.Together, these traits support goal-oriented behavior within constrained deployment settings.
- Capabilities and limitations: AI Agents provide modular and efficient automation for constrained tasks, but remain limited by hallucination, brittle prompting, coordination complexity, error propagation, and context-window constraints.These limitations restrict robustness and planning, especially in dynamic or high-stakes environments.
- Foundational models: LLMs provide language understanding and decision-making, while LIMs support visual perception such as classification, detection, and vision-language grounding.These models enable agents to handle multimodal inputs and reasoning tasks beyond hard-coded automation.
- Illustrative application: An orchard drone illustrates how an AI Agent uses vision models to detect diseased fruits or damaged branches and trigger targeted intervention alerts autonomously.The workflow combines live aerial perception, predefined protocols, autonomy, and reactivity.
- Generative AI as precursor: Generative AI is a reactive precursor that produces prompted content but does not independently pursue goals, maintain state, or execute environmental actions.Tool-augmented AI Agents add memory buffers, APIs, reasoning chains, and planning routines to support active task completion.
3. The emergence of Agentic AI from AI Agent foundations
Agentic AI emerges from tool-augmented AI Agents by extending single-agent task execution into coordinated, adaptive multi-agent systems. The review distinguishes these paradigms through architecture, planning, memory, communication, autonomy, and operational scope.
- Foundations: AI Agents use LLMs, external tools, sequential reasoning, and real-time information to execute narrowly scoped tasks.Examples include document retrieval, customer support, scheduling, and real-time news search workflows.
- Architectural transition: Agentic AI decomposes high-level goals into subtasks distributed among specialized agents coordinated through centralized orchestration or decentralized protocols.Planning agents sequence subtasks dynamically and can adapt to environmental changes or partial failures.
- Architectural enhancements: Persistent memory, inter-agent communication, and reflective reasoning allow Agentic AI systems to retain context, evaluate decisions, and refine strategies across interactions.Coordination can use asynchronous messaging, shared memory buffers, or intermediate output exchanges without continuous central oversight.
- Comparative taxonomy: The taxonomy positions Generative AI as a prompt-driven baseline, AI Agents as tool-based executors, and Agentic AI as coordinators of subagents for high-level workflows.The comparison also considers initiation, adaptation, temporal continuity, memory, and coordination across broader paradigms.
- System-level coordination: Agentic architectures shift intelligence from isolated model outputs toward system-level behavior involving role assignment, recursive feedback, communication, memory, and decentralized control.AutoGen and ChatDev illustrate planner, retriever, and synthesizer agents communicating through shared memory under orchestration.
4. Application of AI Agents and Agentic AI
AI Agents are applied mainly to structured enterprise and consumer workflows, while Agentic AI addresses broader multi-stage, dynamic, and collaborative applications. The review maps these uses across support, search, communication, personalization, research, robotics, and medical decision support.
- AI Agent applications: AI Agents automate customer support and internal enterprise search through retrieval-augmented models connected to APIs and organizational knowledge bases.They answer queries, triage tickets, initiate actions, and retrieve semantically relevant documents.
- AI Agent applications: AI Agents support email filtering, prioritization, personalized recommendations, and natural-language data reporting through classification, behavioral analysis, and structured query generation.These systems can classify messages, detect urgency, infer preferences, generate reports, and return visual or narrative summaries.
- AI Agent applications: Autonomous scheduling assistants interpret calendars, book tasks, reduce coordination overhead, and improve scheduling efficiency by resolving ambiguity and optimizing calendar use.The application is presented as a structured operational workflow requiring minimal user input.
- Agentic AI applications: Agentic AI research assistants coordinate retrievers, summarizers, synthesizers, and citation formatters to automate multi-stage knowledge compilation and draft coherent outputs.Platforms such as AutoGen and CrewAI use orchestration, role dependencies, and persistent memory across the workflow.
- Agentic AI applications: Agentic AI applications extend to adaptive robotic coordination and collaborative medical decision support, where specialized agents share information and distribute complex tasks.Medical examples assign diagnostics, monitoring, and treatment planning to different agents, although real-world implementations remain limited because the field is nascent.
5. Challenges and limitations in AI Agents and Agentic AI
AI Agents remain constrained by weak causal reasoning, inherited LLM shortcomings, incomplete autonomy, and limited long-horizon planning. Agentic AI expands these risks through multi-agent coordination, communication, scalability, and emergent-behavior challenges, motivating system-level safeguards and targeted design strategies.
- AI Agents: AI Agents struggle with causal reasoning, hallucinations, shallow reasoning, incomplete agentic properties, and long-horizon planning or recovery.These limitations are linked to stateless prompts, limited memory, heuristic reasoning loops, static pretrained models, and brittle prompt engineering.
- Mitigation strategies: Proposed remedies include retrieval-augmented generation, tool-based reasoning, ReAct feedback loops, role-based orchestration, memory architectures, causal modeling, and governance-aware design.Together, these strategies form a roadmap toward safer, more scalable, and more context-aware autonomous systems.
- AI Agents: Causal-model deficits prevent reliable counterfactual evaluation and can leave agents brittle under distributional shifts or unfamiliar conditions.The paper contrasts these limitations with formal reasoning systems and causal inference frameworks designed for logical consistency and probabilistic causality.
- AI Agents: LLM-based agents also face prompt sensitivity, hallucinations, computational cost, latency, static knowledge cutoffs, and inherited dataset biases.Retrieval and tool augmentation are identified as ways to integrate new information, while auditing and mitigation remain necessary for sensitive deployments.
- Agentic AI: Agentic AI amplifies individual-agent weaknesses because distributed agents can propagate faulty outputs, trigger error cascades, and corrupt subsequent decisions.Inter-agent distributional shifts and downstream effects make coordination more difficult when agents lack causal reasoning.
- Agentic AI: Communication and coordination bottlenecks arise from weak goal alignment, ambiguous natural-language protocols, contextual drift, resource contention, and absent centralized orchestration.The paper calls for standardized communication protocols, semantic task planners, and global resource management mechanisms.
6. Potential solutions and future roadmap
The paper proposes technical and governance pathways to improve AI Agents and Agentic AI, combining grounding, tools, memory, orchestration, monitoring, and causal or reflexive mechanisms. Its roadmap extends modular agents toward proactive, continually learning systems and coordinated, domain-specific agent collectives, including self-evolving learning through AZR.
- Potential solutions: RAG and tool-augmented reasoning ground agent outputs in external information and let agents interact with real-world systems.RAG retrieves relevant documents, while function calling enables API, script, and database access.
- Potential solutions: Memory architectures preserve information across tasks and support personalization, adaptive decisions, and long-horizon coordination.Agentic AI may combine local agent memories with shared global memory to improve consistency and context-aware communication.
- Potential solutions: Role specialization and orchestration divide complex workflows among functional units, with Agentic AI using meta-agents to delegate tasks and manage dependencies.Single-agent systems can simulate compartmentalized reasoning, whereas Agentic AI institutionalizes dynamic coordination across specialized agents.
- Potential solutions: Monitoring, auditing, explainability pipelines, and governance-aware architectures improve traceability, access control, sandboxing, identity resolution, and accountability.Logs can record prompts, tool calls, memory updates, and outputs; integrated audit trails support multi-agent error attribution and compliance.
- Future roadmap: Future AI Agents are projected to develop proactive reasoning, tool integration, causal inference, continual learning, and trust-centric operations.These capabilities are intended to support task initiation, cause-and-effect reasoning, and adaptation across sessions and environments.
- Future roadmap: Future Agentic AI systems emphasize multi-agent scaling, unified orchestration, simulation planning, ethical governance, and domain-specific workflows.The roadmap describes specialized agents operating under distributed control and using hypothetical decision trajectories to optimize actions.
- Future roadmap: AZR proposes self-play reasoning without external datasets, enabling agents to generate, validate, and solve tasks through verifiable feedback.In orchestrated Agentic AI workflows, the framework supports specialized agents’ co-evolution through self-improving interactions and shared learning objectives.
7. Conclusion
The review distinguishes constrained, task-specific AI Agents from coordinated Agentic AI ecosystems and maps their architectures, applications, and challenges. It concludes that AI Agents fit defined tool-integrated goals, while Agentic AI supports distributed cognition, autonomous planning, and collaborative decision-making.
- AI Agents: AI Agents are modular, task-specific systems with constrained autonomy, using LLMs and LIMs for perception, language understanding, and decision-making.Generative AI is treated as a precursor, while tool augmentation advances systems from passive generation toward interactive task completion.
- Agentic AI: Agentic AI coordinates specialized agents through role assignment, task delegation, and centralized or distributed control for collaborative decision-making.Its distinguishing features include distributed cognition, persistent memory, and coordinated planning.
- Architectural evolution: Orchestration layers and reflective memory support dynamic collaboration, task adaptability, context preservation, and scalable agent collectives.Orchestration mechanisms assign tasks, monitor interactions, and manage dependencies across specialized agents.
- Applications and challenges: AI Agents are applied to customer support, enterprise search, email prioritization, and scheduling, whereas Agentic AI supports research, swarm robotics, medical decision support, and adaptive workflows.The review also identifies hallucinations, shallow reasoning, and planning constraints for AI Agents, alongside causality issues, coordination bottlenecks, emergent behavior, and governance concerns for Agentic AI.
- Conclusion: AI Agents serve narrow, tool-integrated scenarios with defined goals, while Agentic AI enables distributed, multiagent cognition with autonomous planning and decision-making.The review presents this transition from reactive task execution to orchestrated collaborative workflows as a milestone in intelligent-system evolution.
CRediT authorship contribution statement
The authors contributed across writing, visualization, methodology, investigation, and formal analysis, with additional roles in supervision, resources, project administration, and funding acquisition.
- Ranjan Sapkota contributed to writing, visualization, methodology, investigation, formal analysis, and conceptualization.
- Konstantinos I. Roumeliotis contributed to writing, visualization, and methodology.
- Manoj Karkee contributed to writing, visualization, supervision, resources, project administration, investigation, funding acquisition, and formal analysis.
Statement on AI Writing Assistance
ChatGPT and Perplexity supported grammatical and sentence-structure refinement, while ChatGPT-4o generated realistic visualizations; all AI-generated revisions were reviewed and edited.
- ChatGPT and Perplexity were used to improve grammatical accuracy and refine sentence structure.
- All AI-generated revisions were thoroughly reviewed and edited for relevance.
- ChatGPT-4o was employed to generate realistic visualizations.