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AI Agents vs. Agentic AI: A Conceptual
Taxonomy, Applications and Challenges
Ranjan Sapkota∗‡, Konstantinos I. Roumeliotis †, Manoj Karkee ∗‡
∗Cornell University, Department of Biological and Environmental Engineering, USA
†University of the Peloponnese, Department of Informatics and Telecommunications, Tripoli, Greece
‡Corresponding authors: [email protected], [email protected]
Abstract—This review critically distinguishes between AI
Agents and Agentic AI, offering a structured conceptual tax-
onomy, application mapping, and challenge analysis to clarify
their divergent design philosophies and capabilities. We begin by
outlining the search strategy and foundational definitions, charac-
terizing AI Agents as modular systems driven by LLMs and LIMs
for narrow, task-specific automation. Generative AI is positioned
as a precursor, with AI agents advancing through tool integration,
prompt engineering, and reasoning enhancements. In contrast,
agentic AI systems represent a paradigmatic shift marked by
multi-agent collaboration, dynamic task decomposition, persis-
tent memory, and orchestrated autonomy. Through a sequential
evaluation of architectural evolution, operational mechanisms,
interaction styles, and autonomy levels, we present a compara-
tive analysis across both paradigms. Application domains such
as customer support, scheduling, and data summarization are
contrasted with Agentic AI deployments in research automa-
tion, 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, RAG,
orchestration layers, and causal modeling. This work aims to
provide a definitive roadmap for developing robust, scalable, and
explainable AI-driven systems.
Index Terms—AI Agents, Agentic AI, Autonomy, Reasoning,
Context Awareness, Multi-Agent Systems, Conceptual Taxonomy,
vision-language model
Nov 2022 Nov 2023 Nov 2024 2025
AI Agents Agentic AI
Source:
Fig. 1: Global Google search trends showing rising interest
in “AI Agents” and “Agentic AI” since November 2022
(ChatGPT Era).
I. I NTRODUCTION
Prior to the widespread adoption of AI agents and agentic
AI around 2022 (Before ChatGPT Era), the development
of autonomous and intelligent agents was deeply rooted in
foundational paradigms of artificial intelligence, particularly
multi-agent systems (MAS) and expert systems, which em-
phasized social action and distributed intelligence [1], [2].
Notably, Castelfranchi [3] laid critical groundwork by intro-
ducing 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 [4] provided a comprehensive framework for MAS,
defining agents as entities with autonomy, perception, and
communication capabilities, and highlighting their applica-
tions in distributed problem-solving, collective robotics, and
synthetic world simulations. These early works 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 insights
to explore how social action modeling, as proposed in [3], [4],
informs the design of AI agents capable of complex, socially
intelligent interactions in dynamic environments.
These systems were designed to perform specific tasks with
predefined rules, limited autonomy, and minimal adaptability
to dynamic environments. Agent-like 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 [5], [6].
For instance, expert systems used knowledge bases and infer-
ence engines to emulate human decision-making in domains
like medical diagnosis (e.g., MYCIN [7]). Reactive agents,
such as those in robotics, followed sense-act cycles based on
hardcoded rules, as seen in early autonomous vehicles like the
Stanford Cart [8]. Multi-agent systems facilitated coordina-
tion among distributed entities, exemplified by auction-based
resource allocation in supply chain management [9], [10].
Scripted AI in video games, like NPC behaviors in early RPGs,
used predefined decision trees [11]. Furthermore, BDI (Belief-
Desire-Intention) architectures enabled goal-directed behavior
in software agents, such as those in air traffic control simu-
lations [12], [13]. These early systems lacked the generative
capacity, self-learning, and environmental adaptability of mod-
ern agentic AI, which leverages deep learning, reinforcement
learning, and large-scale data [14].
Recent public and academic interest in AI Agents and Agen-
tic AI reflects this broader transition in system capabilities.
As illustrated in Figure 1, Google Trends data demonstrates
a significant rise in global search interest for both terms
arXiv:2505.10468v3 [cs.AI] 20 May 2025 | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-21T00:48:59+00:00", "moddate": "2025-05-21T00:48:59+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "trapped": "/False", "source": "data\\raw\\ai_agents_vs_agentic_ai_2505.10468.pdf", "total_pages": 33, "page": 0, "page_label": "1", "loader_type": "pdf", "load_timestamp": "2025-05-21T10:22:32.134900"} |
following the emergence of large-scale generative models in
late 2022. This shift is closely tied to the evolution of agent
design from the pre-2022 era, where AI agents operated in
constrained, rule-based environments, to the post-ChatGPT
period marked by learning-driven, flexible architectures [15]–
[17]. These newer systems enable agents to refine their perfor-
mance over time and interact autonomously with unstructured,
dynamic inputs [18]–[20]. For instance, while pre-modern
expert systems required manual updates to static knowledge
bases, modern agents leverage emergent neural behaviors
to generalize across tasks [17]. The rise in trend activity
reflects increasing recognition of these differences. Moreover,
applications are no longer confined to narrow domains like
simulations or logistics, but now extend to open-world settings
demanding real-time reasoning and adaptive control. This mo-
mentum, as visualized in Figure 1, underscores the significance
of recent architectural advances in scaling autonomous agents
for real-world deployment.
The release of ChatGPT in November 2022 marked a pivotal
inflection point in the development and public perception of
artificial intelligence, catalyzing a global surge in adoption,
investment, and research activity [21]. In the wake of this
breakthrough, the AI landscape underwent a rapid transforma-
tion, shifting from the use of standalone LLMs toward more
autonomous, task-oriented frameworks [22]. 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 LLM-
based systems designed to produce novel outputs such as text,
images, and code from user prompts [23], [24]. These agents
were quickly adopted across applications ranging from con-
versational assistants (e.g., GitHub Copilot [25]) and content-
generation platforms (e.g., Jasper [26]) to creative tools (e.g.,
Midjourney [27]), revolutionizing domains like digital design,
marketing, and software prototyping throughout 2023.
Although the term AI agent was first introduced in
1998 [3], it has since evolved significantly with the rise
of generative AI. Building upon this generative founda-
tion, 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 se-
quential reasoning, enabling them to retrieve real-time in-
formation and execute multi-step workflows autonomously
[28], [29]. Frameworks such as AutoGPT [30] and BabyAGI
(https://github.com/yoheinakajima/babyagi) exemplified this
transition, showcasing how LLMs could be embedded within
feedback loops to dynamically plan, act, and adapt in goal-
driven environments [31], [32]. By late 2023, 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 (A2A) protocol in 2025 [33], 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 capabil-
ities, 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 agen-
tic frameworks can orchestrate decision-making across dis-
tributed roles, facilitating intelligent behavior in high-stakes
applications including autonomous robotics, logistics manage-
ment, and adaptive decision-support [34]–[37].
As the field progresses from Generative Agents toward
increasingly autonomous systems, it becomes critically impor-
tant to delineate the technological and conceptual boundaries
between AI Agents and Agentic AI. While both paradigms
build upon large LLMs and extend the capabilities of gener-
ative systems, they embody fundamentally different architec-
tures, interaction models, and levels of autonomy. AI Agents
are typically designed as single-entity systems that perform
goal-directed tasks by invoking external tools, applying se-
quential reasoning, and integrating real-time information to
complete well-defined functions [17], [38]. In contrast, Agen-
tic AI systems are composed of multiple, specialized agents
that coordinate, communicate, and dynamically allocate sub-
tasks within a broader workflow [14], [39]. This architec-
tural distinction underpins profound 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 reserved
for orchestrated multi-agent operations. Moreover, it allows
for appropriate benchmarking and evaluation: performance
metrics, safety protocols, and resource requirements differ
markedly between individual-task agents and distributed agent
systems. Additionally, clear taxonomy reduces development
inefficiencies by preventing the misapplication of design prin-
ciples such as assuming inter-agent collaboration in a system
architected for single-agent execution. Without this clarity,
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.
Since the field of artificial intelligence has seen significant
advancements, particularly in the development of AI Agents
and Agentic AI. These terms, while related, refer to distinct
concepts with different capabilities and applications. This
article aims to clarify the differences between AI Agents and
Agentic AI, providing researchers with a foundational under-
standing 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, as illustrated
in Figure 2.
This review provides a comprehensive conceptual and archi-
tectural analysis of the progression from traditional AI Agents | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-21T00:48:59+00:00", "moddate": "2025-05-21T00:48:59+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "trapped": "/False", "source": "data\\raw\\ai_agents_vs_agentic_ai_2505.10468.pdf", "total_pages": 33, "page": 1, "page_label": "2", "loader_type": "pdf", "load_timestamp": "2025-05-21T10:22:32.134910"} |
AI Agents
&
Agentic AI
Architecture
Mechanisms
Scope/
Complexity
Interaction
Autonomy
Fig. 2: Mind map of Research Questions relevant to AI
Agents and Agentic AI. Each color-coded branch represents
a key dimension of comparison: Architecture, Mechanisms,
Scope/Complexity, Interaction, and Autonomy.
to emergent Agentic AI systems. Rather than organizing the
study around formal research questions, we adopt a sequential,
layered structure that mirrors the historical and technical
evolution of these paradigms. Beginning with a detailed de-
scription of our search strategy and selection criteria, we
first establish the foundational understanding of AI Agents
by analyzing their defining attributes, such as autonomy, reac-
tivity, and tool-based execution. We then explore the critical
role of foundational models specifically LLMs and Large
Image Models (LIMs) which serve as the core reasoning and
perceptual substrates 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
lens, we trace the conceptual leap from isolated, single-agent
systems to orchestrated multi-agent architectures, highlight-
ing their structural distinctions, coordination strategies, and
collaborative mechanisms. We further map the architectural
evolution by dissecting the core system components of both
AI Agents and Agentic AI, offering comparative insights into
their planning, memory, orchestration, and execution layers.
Building upon this foundation, we review application domains
spanning customer support, healthcare, research automation,
and robotics, 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
emerging solutions such as retrieval-augmented generation,
tool-based reasoning, memory architectures, and simulation-
based planning. The review culminates in a forward-looking
roadmap that envisions the convergence of modular AI Agents
and orchestrated Agentic AI in mission-critical domains. Over-
all, this paper aims to provide researchers with a structured
taxonomy and actionable insights to guide the design, deploy-
ment, and evaluation of next-generation agentic systems.
A. Methodology Overview
This review adopts a structured, multi-stage methodology
designed to capture the evolution, architecture, application,
and limitations of AI Agents and Agentic AI. The process
is visually summarized in Figure 3, which delineates the
sequential flow of topics explored in this study. The analytical
framework was organized to trace the progression from basic
agentic constructs rooted in LLMs to advanced multi-agent
orchestration systems. Each step of the review was grounded in
rigorous literature synthesis across academic sources and AI-
powered platforms, enabling a comprehensive understanding
of the current landscape and its emerging trajectories.
The review begins by establishing a foundational under-
standing of AI Agents, examining their core definitions, design
principles, and architectural modules as described in the litera-
ture. These include components such as perception, reasoning,
and action selection, along with early applications like cus-
tomer service bots and retrieval assistants. This foundational
layer serves as the conceptual entry point into the broader
agentic paradigm.
Next, we delve into the role of LLMs as core reasoning
components, emphasizing how pre-trained language models
underpin modern AI Agents. This section details how LLMs,
through instruction fine-tuning and reinforcement learning
from human feedback (RLHF), enable natural language in-
teraction, planning, and limited decision-making capabilities.
We also identify their limitations, such as hallucinations, static
knowledge, and a lack of causal reasoning.
Building on these foundations, the review proceeds to the
emergence of Agentic AI , which represents a significant con-
ceptual leap. Here, we highlight the transformation from tool-
augmented single-agent systems to collaborative, distributed
ecosystems of interacting agents. This shift is driven by the
need for systems capable of decomposing goals, assigning
subtasks, coordinating outputs, and adapting dynamically to
changing contexts—capabilities that surpass what isolated AI
Agents can offer.
The next section examines the architectural evolution from
AI Agents to Agentic AI systems , contrasting simple, modular
agent designs with complex orchestration frameworks. We
describe enhancements such as persistent memory, meta-agent
coordination, multi-agent planning loops (e.g., ReAct and
Chain-of-Thought prompting), and semantic communication
protocols. Comparative architectural analysis is supported with
examples from platforms like AutoGPT, CrewAI, and Lang-
Graph.
Following the architectural exploration, the review presents
an in-depth analysis of application domains where AI Agents
and Agentic AI are being deployed. This includes six key | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-21T00:48:59+00:00", "moddate": "2025-05-21T00:48:59+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "trapped": "/False", "source": "data\\raw\\ai_agents_vs_agentic_ai_2505.10468.pdf", "total_pages": 33, "page": 2, "page_label": "3", "loader_type": "pdf", "load_timestamp": "2025-05-21T10:22:32.134911"} |
Hybrid Literature Search
Foundational
Understanding
of AI Agents
LLMs as Core
Reasoning Components
Emergence of
Agentic AI
Architectural Evolution:
Agents→Agentic AI
Applications of
AI Agents & Agentic AI
Challenges & Limitations
(Agents + Agentic AI)
Potential Solutions:
RAG, Causal
Models, Planning
Fig. 3: Methodology pipeline from foundational AI agents to Agentic AI systems, applications, limitations, and solution
strategies.
application areas for each paradigm, ranging from knowledge
retrieval, email automation, and report summarization for AI
Agents, to research assistants, robotic swarms, and strategic
business planning for Agentic AI. Use cases are discussed in
the context of system complexity, real-time decision-making,
and collaborative task execution.
Subsequently, we address the challenges and limitations
inherent to both paradigms. For AI Agents, we focus on issues
like hallucination, prompt brittleness, limited planning ability,
and lack of causal understanding. For Agentic AI, we identify
higher-order challenges such as inter-agent misalignment, error
propagation, unpredictability of emergent behavior, explain-
ability deficits, and adversarial vulnerabilities. These problems
are critically examined with references to recent experimental
studies and technical reports.
Finally, the review outlines potential solutions to over-
come these challenges , drawing on recent advances in causal
modeling, retrieval-augmented generation (RAG), multi-agent
memory frameworks, and robust evaluation pipelines. These
strategies are discussed not only as technical fixes but as foun-
dational requirements for scaling agentic systems into high-
stakes domains such as healthcare, finance, and autonomous
robotics.
Taken together, this methodological structure enables a
comprehensive and systematic assessment of the state of AI
Agents and Agentic AI. By sequencing the analysis across
foundational understanding, model integration, architectural
growth, applications, and limitations, the study aims to provide
both theoretical clarity and practical guidance to researchers
and practitioners navigating this rapidly evolving field.
1) Search Strategy: To construct this review, we imple-
mented a hybrid search methodology combining traditional
academic repositories and AI-enhanced literature discovery
tools. Specifically, twelve platforms were queried: academic
databases such as Google Scholar, IEEE Xplore, ACM Dig-
ital Library, Scopus, Web of Science, ScienceDirect, and
arXiv; and AI-powered interfaces including ChatGPT, Per-
plexity.ai, DeepSeek, Hugging Face Search, and Grok. Search
queries incorporated Boolean combinations of terms such as
“AI Agents,” “Agentic AI,” “LLM Agents,” “Tool-augmented
LLMs,” and “Multi-Agent AI Systems.”
Targeted queries such as “Agentic AI + Coordination +
Planning,” and “AI Agents + Tool Usage + Reasoning”
were employed to retrieve papers addressing both conceptual
underpinnings and system-level implementations. Literature
inclusion was based on criteria such as novelty, empirical
evaluation, architectural contribution, and citation impact. The
rising global interest in these technologies, as illustrated in
Figure 1 using Google Trends data, underscores the urgency
of synthesizing this emerging knowledge space.
II. F OUNDATIONAL UNDERSTANDING OF AI AGENTS
AI Agents are an autonomous software entities engineered
for goal-directed task execution within bounded digital en-
vironments [14], [40]. These agents are defined by their
ability to perceive structured or unstructured inputs [41],
reason over contextual information [42], [43], and initiate
actions toward achieving specific objectives, often acting
as surrogates for human users or subsystems [44]. Unlike
conventional automation scripts, which follow deterministic
workflows, AI agents demonstrate reactive intelligence and
limited adaptability, allowing them to interpret dynamic inputs
and reconfigure outputs accordingly [45]. Their adoption has
been reported across a range of application domains, including | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-21T00:48:59+00:00", "moddate": "2025-05-21T00:48:59+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "trapped": "/False", "source": "data\\raw\\ai_agents_vs_agentic_ai_2505.10468.pdf", "total_pages": 33, "page": 3, "page_label": "4", "loader_type": "pdf", "load_timestamp": "2025-05-21T10:22:32.134913"} |
AI Agents
Fig. 4: Core characteristics of AI Agents autonomy, task-specificity, and reactivity illustrated with symbolic representations for
agent design and operational behavior.
customer service automation [46], [47], personal productivity
assistance [48], internal information retrieval [49], [50], and
decision support systems [51], [52]. A noteworthy example of
autonomous AI agents is Anthropic’s ”Computer Use” project,
where Claude was trained to navigate computers to automate
repetitive processes, build and test software, and perform open-
ended tasks such as research [53].
1) Overview of Core Characteristics of AI Agents: AI
Agents are widely conceptualized as instantiated operational
embodiments of artificial intelligence designed to interface
with users, software ecosystems, or digital infrastructures in
pursuit of goal-directed behavior [54]–[56]. These agents dis-
tinguish themselves from general-purpose LLMs by exhibiting
structured initialization, bounded autonomy, and persistent
task orientation. While LLMs primarily function as reactive
prompt followers [57], AI Agents operate within explicitly de-
fined scopes, engaging dynamically with inputs and producing
actionable outputs in real-time environments [58].
Figure 4 illustrates the three foundational characteristics that
recur across architectural taxonomies and empirical deploy-
ments of AI Agents. These include autonomy, task-specificity,
and reactivity with adaptation . First, autonomy denotes the
agent’s ability to act independently post-deployment, mini-
mizing human-in-the-loop dependencies and enabling large-
scale, unattended operation [47], [59]. Second, task-specificity
encapsulates the design philosophy of AI agents being spe-
cialized for narrowly scoped tasks allowing high-performance
optimization within a defined functional domain such as
scheduling, querying, or filtering [60], [61]. Third, reactivity
refers to an agent’s capacity to respond to changes in its
environment, including user commands, software states, or
API responses; when extended with adaptation, this includes
feedback loops and basic learning heuristics [17], [62].
Together, these three traits provide a foundational profile for
understanding and evaluating AI Agents across deployment
scenarios. The remainder of this section elaborates on each
characteristic, offering theoretical grounding and illustrative
examples.
• Autonomy: A central feature of AI Agents is their
ability to function with minimal or no human intervention
after deployment [59]. Once initialized, these agents are
capable of perceiving environmental inputs, reasoning
over contextual data, and executing predefined or adaptive
actions in real-time [17]. Autonomy enables scalable
deployment in applications where persistent oversight is
impractical, such as customer support bots or scheduling
assistants [47], [63].
• Task-Specificity: AI Agents are purpose-built for narrow,
well-defined tasks [60], [61]. They are optimized to
execute repeatable operations within a fixed domain, such
as email filtering [64], [65], database querying [66], or
calendar coordination [39], [67]. This task specialization
allows for efficiency, interpretability, and high precision
in automation tasks where general-purpose reasoning is
unnecessary or inefficient.
• Reactivity and Adaptation: AI Agents often include
basic mechanisms for interacting with dynamic inputs,
allowing them to respond to real-time stimuli such as
user requests, external API calls, or state changes in
software environments [17], [62]. Some systems integrate
rudimentary learning [68] through feedback loops [69],
[70], heuristics [71], or updated context buffers to refine
behavior over time, particularly in settings like personal-
ized recommendations or conversation flow management | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-21T00:48:59+00:00", "moddate": "2025-05-21T00:48:59+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "trapped": "/False", "source": "data\\raw\\ai_agents_vs_agentic_ai_2505.10468.pdf", "total_pages": 33, "page": 4, "page_label": "5", "loader_type": "pdf", "load_timestamp": "2025-05-21T10:22:32.134914"} |
[72]–[74].
These core characteristics collectively enable AI Agents to
serve as modular, lightweight interfaces between pretrained AI
models and domain-specific utility pipelines. Their architec-
tural simplicity and operational efficiency position them as key
enablers of scalable automation across enterprise, consumer,
and industrial settings. Although still limited in reasoning
depth compared to more general AI systems [75], their high
usability and performance within constrained task boundaries
have made them foundational components in contemporary
intelligent system design.
2) Foundational Models: The Role of LLMs and LIMs:
The foundational progress in AI agents has been significantly
accelerated by the development and deployment of LLMs
and LIMs, which serve as the core reasoning and perception
engines in contemporary agent systems. These models enable
AI agents to interact intelligently with their environments,
understand multimodal inputs, and perform complex reasoning
tasks that go beyond hard-coded automation.
LLMs such as GPT-4 [76] and PaLM [77] are trained on
massive datasets of text from books, web content, and dialogue
corpora. These models exhibit emergent capabilities in natural
language understanding, question answering, summarization,
dialogue coherence, and even symbolic reasoning [78], [79].
Within AI agent architectures, LLMs serve as the primary
decision-making engine, allowing the agent to parse user
queries, plan multi-step solutions, and generate naturalistic
responses. For instance, an AI customer support agent powered
by GPT-4 can interpret customer complaints, query backend
systems via tool integration, and respond in a contextually
appropriate and emotionally aware manner [80], [81].
Large Image Models (LIMs) such as CLIP [82] and BLIP-
2 [83] extend the agent’s capabilities into the visual domain.
Trained on image-text pairs, LIMs enable perception-based
tasks including image classification, object detection, and
vision-language grounding. These capabilities are increasingly
vital for agents operating in domains such as robotics [84],
autonomous vehicles [85], [86], and visual content moderation
[87], [88].
For example, as illustrated in Figure 5 in an autonomous
drone agent tasked with inspecting orchards, a LIM can
identify diseased fruits [89] or damaged branches by inter-
preting live aerial imagery and triggering predefined inter-
vention protocols. Upon detection, the system autonomously
triggers predefined intervention protocols, such as notifying
horticultural staff or marking the location for targeted treat-
ment without requiring human intervention [17], [59]. This
workflow exemplifies the autonomy and reactivity of AI agents
in agricultural environment and recent literature underscores
the growing sophistication of such drone-based AI agents.
Chitra et al. [90] provide a comprehensive overview of AI
algorithms foundational to embodied agents, highlighting the
integration of computer vision, SLAM, reinforcement learning,
and sensor fusion. These components collectively support real-
time perception and adaptive navigation in dynamic envi-
ronments. Kourav et al. [91] further emphasize the role of
Fig. 5: An AI agent–enabled drone autonomously inspects
an orchard, identifying diseased fruits and damaged branches
using vision models, and triggers real-time alerts for targeted
horticultural interventions
natural language processing and large language models in
generating drone action plans from human-issued queries,
demonstrating how LLMs support naturalistic interaction and
mission planning. Similarly, Natarajan et al. [92] explore deep
learning and reinforcement learning for scene understand-
ing, spatial mapping, and multi-agent coordination in aerial
robotics. These studies converge on the critical importance
of AI-driven autonomy, perception, and decision-making in
advancing drone-based agents.
Importantly, LLMs and LIMs are often accessed via infer-
ence APIs provided by cloud-based platforms such as OpenAI
https://openai.com/, HuggingFace https://huggingface.co/, and
Google Gemini https://gemini.google.com/app. These services
abstract away the complexity of model training and fine-
tuning, enabling developers to rapidly build and deploy agents
equipped with state-of-the-art reasoning and perceptual abil-
ities. This composability accelerates prototyping and allows
agent frameworks like LangChain [93] and AutoGen [94]
to orchestrate LLM and LIM outputs across task workflows.
In short, foundational models give modern AI agents their
basic understanding of language and visuals. Language models
help them reason with words, and image models help them
understand pictures-working together, they allow AI to make
smart decisions in complex situations.
3) Generative AI as a Precursor: A consistent theme in the
literature is the positioning of generative AI as the foundational
precursor to agentic intelligence. These systems primarily
operate on pretrained LLMs and LIMs, which are optimized
to synthesize novel content text, images, audio, or code
based on input prompts. While highly expressive, generative
models fundamentally exhibit reactive behavior: they produce
output only when explicitly prompted and do not pursue goals
autonomously or engage in self-initiated reasoning [95], [96].
Key Characteristics of Generative AI: | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-21T00:48:59+00:00", "moddate": "2025-05-21T00:48:59+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "trapped": "/False", "source": "data\\raw\\ai_agents_vs_agentic_ai_2505.10468.pdf", "total_pages": 33, "page": 5, "page_label": "6", "loader_type": "pdf", "load_timestamp": "2025-05-21T10:22:32.134915"} |
• Reactivity: As non-autonomous systems, generative
models are exclusively input-driven [97], [98]. Their
operations are triggered by user-specified prompts and
they lack internal states, persistent memory, or goal-
following mechanisms [99]–[101].
• Multimodal Capability: Modern generative systems can
produce a diverse array of outputs, including coherent
narratives, executable code, realistic images, and even
speech transcripts. For instance, models like GPT-4 [76],
PaLM-E [102], and BLIP-2 [83] exemplify this capacity,
enabling language-to-image, image-to-text, and cross-
modal synthesis tasks.
• Prompt Dependency and Statelessness: Although gen-
erative systems are stateless in that they do not retain con-
text across interactions unless explicitly provided [103],
[104], recent advancements like GPT-4.1 support larger
context windows-up to 1 million tokens-and are better
able to utilize that context thanks to improved long-text
comprehension [105]. Their design also lacks intrinsic
feedback loops [106], state management [107], [108],
or multi-step planning a requirement for autonomous
decision-making and iterative goal refinement [109],
[110].
Despite their remarkable generative fidelity, these systems
are constrained by their inability to act upon the environment
or manipulate digital tools independently. For instance, they
cannot search the internet, parse real-time data, or interact
with APIs without human-engineered wrappers or scaffolding
layers. As such, they fall short of being classified as true
AI Agents, whose architectures integrate perception, decision-
making, and external tool-use within closed feedback loops.
The limitations of generative AI in handling dynamic tasks,
maintaining state continuity, or executing multi-step plans led
to the development of tool-augmented systems, commonly
referred to as AI Agents [111]. These systems build upon
the language processing backbone of LLMs but introduce
additional infrastructure such as memory buffers, tool-calling
APIs, reasoning chains, and planning routines to bridge the
gap between passive response generation and active task
completion. This architectural evolution marks a critical shift
in AI system design: from content creation to autonomous
utility [112], [113]. The trajectory from generative systems to
AI agents underscores a progressive layering of functionality
that ultimately supports the emergence of agentic behaviors.
A. Language Models as the Engine for AI Agent Progression
The emergence of AI agent as a transformative paradigm
in artificial intelligence is closely tied to the evolution and
repurposing of large-scale language models such as GPT-3
[114], Llama [115], T5 [116], Baichuan 2 [117] and GPT3mix
[118]. A substantial and growing body of research confirms
that the leap from reactive generative models to autonomous,
goal-directed agents is driven by the integration of LLMs
as core reasoning engines within dynamic agentic systems.
These models, originally trained for natural language pro-
cessing tasks, are increasingly embedded in frameworks that
require adaptive planning [119], [120], real-time decision-
making [121], [122], and environment-aware behavior [123].
1) LLMs as Core Reasoning Components:
LLMs such as GPT-4 [76], PaLM [77], Claude
https://www.anthropic.com/news/claude-3-5-sonnet, and
LLaMA [115] are pre-trained on massive text corpora using
self-supervised objectives and fine-tuned using techniques
such as Supervised Fine-Tuning (SFT) and Reinforcement
Learning from Human Feedback (RLHF) [124], [125]. These
models encode rich statistical and semantic knowledge,
allowing them to perform tasks like inference, summarization,
code generation, and dialogue management. However, in
agentic contexts, their capabilities extend beyond response
generation. They function as cognitive engines that interpret
user goals, formulate and evaluate possible action plans,
select the most appropriate strategies, leverage external tools,
and manage complex, multi-step workflows.
Recent work identifies these models as central
to the architecture of contemporary agentic
systems. For instance, AutoGPT [30] and BabyAGI
https://github.com/yoheinakajima/babyagi use GPT-4 as
both a planner and executor: the model analyzes high-level
objectives, decomposes them into actionable subtasks, invokes
external APIs as needed, and monitors progress to determine
subsequent actions. In such systems, the LLM operates in a
loop of prompt processing, state updating, and feedback-based
correction, closely emulating autonomous decision-making.
2) Tool-Augmented AI Agents: Enhancing Functionality:
To overcome limitations inherent to generative-only systems
such as hallucination, static knowledge cutoffs, and restricted
interaction scopes, researchers have proposed the concept of
tool-augmented LLM agents [126] such as Easytool [127],
Gentopia [128], and ToolFive [129]. These systems integrate
external tools, APIs, and computation platforms into the
agent’s reasoning pipeline, allowing for real-time information
access, code execution, and interaction with dynamic data
environments.
Tool Invocation. When an agent identifies a need that
cannot be addressed through its internal knowledge such as
querying a current stock price, retrieving up-to-date weather
information, or executing a script, it generates a structured
function call or API request [130], [131]. These calls are
typically formatted in JSON, SQL, or Python dictionary,
depending on the target service, and routed through an or-
chestration layer that executes the task.
Result Integration. Once a response is received from the
tool, the output is parsed and reincorporated into the LLM’s
context window. This enables the agent to synthesize new
reasoning paths, update its task status, and decide on the next
step. The ReAct framework [132] exemplifies this architecture
by combining reasoning (Chain-of-Thought prompting) and
action (tool use), with LLMs alternating between internal
cognition and external environment interaction. A prominent
example of a tool-augmented AI agent is ChatGPT, which,
when unable to answer a query directly, autonomously invokes
the Web Search API to retrieve more recent and relevant | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-21T00:48:59+00:00", "moddate": "2025-05-21T00:48:59+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "trapped": "/False", "source": "data\\raw\\ai_agents_vs_agentic_ai_2505.10468.pdf", "total_pages": 33, "page": 6, "page_label": "7", "loader_type": "pdf", "load_timestamp": "2025-05-21T10:22:32.134916"} |
information, performs reasoning over the retrieved content,
and formulates a response based on its understanding [133].
3) Illustrative Examples and Emerging Capabilities: Tool-
augmented LLM agents have demonstrated capabilities across
a range of applications. In AutoGPT [30], the agent may
plan a product market analysis by sequentially querying the
web, compiling competitor data, summarizing insights, and
generating a report. In a coding context, tools like GPT-
Engineer combine LLM-driven design with local code exe-
cution environments to iteratively develop software artifacts
[134], [135]. In research domains, systems like Paper-QA
[136] utilize LLMs to query vectorized academic databases,
grounding answers in retrieved scientific literature to ensure
factual integrity.
These capabilities have opened pathways for more robust
behavior of AI agents such as long-horizon planning, cross-
tool coordination, and adaptive learning loops. Nevertheless,
the inclusion of tools also introduces new challenges in or-
chestration complexity, error propagation, and context window
limitations all active areas of research. The progression toward
AI Agents is inseparable from the strategic integration of
LLMs as reasoning engines and their augmentation through
structured tool use. This synergy transforms static language
models into dynamic cognitive entities capable of perceiving,
planning, acting, and adapting setting the stage for multi-agent
collaboration, persistent memory, and scalable autonomy.
Figure 6 illustrates a representative case: a news query agent
that performs real-time web search, summarizes retrieved
documents, and generates an articulate, context-aware answer.
Such workflows have been demonstrated in implementations
using LangChain, AutoGPT, and OpenAI function-calling
paradigms.
Fig. 6: Illustrating the workflow of an AI Agent performing
real-time news search, summarization, and answer generation
III. T HE EMERGENCE OF AGENTIC AI FROM AI AGENT
FOUNDATIONS
While AI Agents represent a significant leap in artificial in-
telligence capabilities, particularly in automating narrow tasks
through tool-augmented reasoning, recent literature identifies
notable limitations that constrain their scalability in complex,
multi-step, or cooperative scenarios [137]–[139]. These con-
straints have catalyzed the development of a more advanced
paradigm: Agentic AI. This emerging class of systems extends
the capabilities of traditional agents by enabling multiple
intelligent entities to collaboratively pursue goals through
structured communication [140]–[142], shared memory [143],
[144], and dynamic role assignment [14].
1) Conceptual Leap: From Isolated Tasks to Coordinated
Systems: AI Agents, as explored in prior sections, integrate
LLMs with external tools and APIs to execute narrowly scoped
operations such as responding to customer queries, performing
document retrieval, or managing schedules. However, as use
cases increasingly demand context retention, task interde-
pendence, and adaptability across dynamic environments, the
single-agent model proves insufficient [145], [146].
Agentic AI systems represent an emergent class of in-
telligent architectures in which multiple specialized agents
collaborate to achieve complex, high-level objectives [33]. As
defined in recent frameworks, these systems are composed of
modular agents each tasked with a distinct subcomponent of
a broader goal and coordinated through either a centralized
orchestrator or a decentralized protocol [16], [141]. This
structure signifies a conceptual departure from the atomic,
reactive behaviors typically observed in single-agent architec-
tures, toward a form of system-level intelligence characterized
by dynamic inter-agent collaboration.
A key enabler of this paradigm is goal decomposition ,
wherein a user-specified objective is automatically parsed and
divided into smaller, manageable tasks by planning agents
[39]. These subtasks are then distributed across the agent
network. Multi-step reasoning and planning mechanisms
facilitate the dynamic sequencing of these subtasks, allowing
the system to adapt in real time to environmental shifts or
partial task failures. This ensures robust task execution even
under uncertainty [14].
Inter-agent communication is mediated through distributed
communication channels , such as asynchronous messaging
queues, shared memory buffers, or intermediate output ex-
changes, enabling coordination without necessitating contin-
uous central oversight [14], [147]. Furthermore, reflective
reasoning and memory systems allow agents to store context
across multiple interactions, evaluate past decisions, and itera-
tively refine their strategies [148]. Collectively, these capabili-
ties enable Agentic AI systems to exhibit flexible, adaptive,
and collaborative intelligence that exceeds the operational
limits of individual agents.
A widely accepted conceptual illustration in the literature
delineates the distinction between AI Agents and Agentic AI
through the analogy of smart home systems. As depicted in
Figure 7, the left side represents a traditional AI Agent in the
form of a smart thermostat. This standalone agent receives
a user-defined temperature setting and autonomously controls
the heating or cooling system to maintain the target tempera-
ture. While it demonstrates limited autonomy such as learning | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-21T00:48:59+00:00", "moddate": "2025-05-21T00:48:59+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "trapped": "/False", "source": "data\\raw\\ai_agents_vs_agentic_ai_2505.10468.pdf", "total_pages": 33, "page": 7, "page_label": "8", "loader_type": "pdf", "load_timestamp": "2025-05-21T10:22:32.134918"} |
Fig. 7: Comparative illustration of AI Agent vs. Agentic AI, synthesizing conceptual distinctions. Left: A single-task AI Agent.
Right: A multi-agent, collaborative Agentic AI system.
user schedules or reducing energy usage during absence, it
operates in isolation, executing a singular, well-defined task
without engaging in broader environmental coordination or
goal inference [17], [59].
In contrast, the right side of Figure 7 illustrates an Agentic
AI system embedded in a comprehensive smart home ecosys-
tem. Here, multiple specialized agents interact synergistically
to manage diverse aspects such as weather forecasting, daily
scheduling, energy pricing optimization, security monitoring,
and backup power activation. These agents are not just reactive
modules; they communicate dynamically, share memory states,
and collaboratively align actions toward a high-level system
goal (e.g., optimizing comfort, safety, and energy efficiency
in real time). For instance, a weather forecast agent might
signal upcoming heatwaves, prompting early pre-cooling via
solar energy before peak pricing hours, as coordinated by an
energy management agent. Simultaneously, the system might
delay high-energy tasks or activate surveillance systems during
occupant absence, integrating decisions across domains. This
figure embodies the architectural and functional leap from
task-specific automation to adaptive, orchestrated intelligence.
The AI Agent acts as a deterministic component with limited
scope, while Agentic AI reflects distributed intelligence, char-
acterized by goal decomposition, inter-agent communication,
and contextual adaptation, hallmarks of modern agentic AI
frameworks.
2) Key Differentiators between AI Agents and Agentic AI:
To systematically capture the evolution from Generative AI
to AI Agents and further to Agentic AI, we structure our
comparative analysis around a foundational taxonomy where
Generative AI serves as the baseline. While AI Agents and
Agentic AI represent increasingly autonomous and interactive
systems, both paradigms are fundamentally grounded in gener-
ative architectures, especially LLMs and LIMs. Consequently,
each comparative table in this subsection includes Generative
AI as a reference column to highlight how agentic behavior
diverges and builds upon generative foundations.
A set of fundamental distinctions between AI Agents and
Agentic AI particularly in terms of scope, autonomy, architec-
tural composition, coordination strategy, and operational com-
plexity are synthesized in Table I, derived from close analysis
of prominent frameworks such as AutoGen [94] and ChatDev
[149]. These comparisons provide a multi-dimensional view
of how single-agent systems transition into coordinated, multi-
agent ecosystems. Through the lens of generative capabilities,
we trace the increasing sophistication in planning, communica-
tion, and adaptation that characterizes the shift toward Agentic
AI.
While Table I delineates the foundational and operational
differences between AI Agents and Agentic AI, a more gran- | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-21T00:48:59+00:00", "moddate": "2025-05-21T00:48:59+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "trapped": "/False", "source": "data\\raw\\ai_agents_vs_agentic_ai_2505.10468.pdf", "total_pages": 33, "page": 8, "page_label": "9", "loader_type": "pdf", "load_timestamp": "2025-05-21T10:22:32.134919"} |
TABLE I: Key Differences Between AI Agents and Agentic
AI
Feature AI Agents Agentic AI
Definition
Autonomous
software
programs that
perform specific
tasks.
Systems of multiple AI
agents collaborating to
achieve complex goals.
Autonomy Level
High autonomy
within specific
tasks.
Higher autonomy with
the ability to manage
multi-step, complex tasks.
Task
Complexity
Typically handle
single, specific
tasks.
Handle complex,
multi-step tasks requiring
coordination.
Collaboration Operate
independently.
Involve multi-agent
collaboration and
information sharing.
Learning and
Adaptation
Learn and adapt
within their
specific domain.
Learn and adapt across a
wider range of tasks and
environments.
Applications
Customer service
chatbots, virtual
assistants,
automated
workflows.
Supply chain
management, business
process optimization,
virtual project managers.
ular taxonomy is required to understand how these paradigms
emerge from and relate to broader generative frameworks.
Specifically, the conceptual and cognitive progression from
static Generative AI systems to tool-augmented AI Agents,
and further to collaborative Agentic AI ecosystems, necessi-
tates an integrated comparative framework. This transition is
not merely structural but also functional encompassing how
initiation mechanisms, memory use, learning capacities, and
orchestration strategies evolve across the agentic spectrum.
Moreover, recent studies suggest the emergence of hybrid
paradigms such as ”Generative Agents,” which blend gen-
erative modeling with modular task specialization, further
complicating the agentic landscape. In order to capture these
nuanced relationships, Table II synthesizes the key conceptual
and cognitive dimensions across four archetypes: Generative
AI, AI Agents, Agentic AI, and inferred Generative Agents.
By positioning Generative AI as a baseline technology, this
taxonomy highlights the scientific continuum that spans from
passive content generation to interactive task execution and
finally to autonomous, multi-agent orchestration. This multi-
tiered lens is critical for understanding both the current ca-
pabilities and future trajectories of agentic intelligence across
applied and theoretical domains.
To further operationalize the distinctions outlined in Ta-
ble I, Tables III and II extend the comparative lens to en-
compass a broader spectrum of agent paradigms including
AI Agents, Agentic AI, and emerging Generative Agents.
Table III presents key architectural and behavioral attributes
that highlight how each paradigm differs in terms of pri-
mary capabilities, planning scope, interaction style, learning
dynamics, and evaluation criteria. AI Agents are optimized
for discrete task execution with limited planning horizons and
rely on supervised or rule-based learning mechanisms. In con-
trast, Agentic AI systems extend this capacity through multi-
step planning, meta-learning, and inter-agent communication,
positioning them for use in complex environments requiring
autonomous goal setting and coordination. Generative Agents,
as a more recent construct, inherit LLM-centric pretraining
capabilities and excel in producing multimodal content cre-
atively, yet they lack the proactive orchestration and state-
persistent behaviors seen in Agentic AI systems.
The second table (Table III) provides a process-driven
comparison across three agent categories: Generative AI,
AI Agents, and Agentic AI. This framing emphasizes how
functional pipelines evolve from prompt-driven single-model
inference in Generative AI, to tool-augmented execution in AI
Agents, and finally to orchestrated agent networks in Agentic
AI. The structure column underscores this progression: from
single LLMs to integrated toolchains and ultimately to dis-
tributed multi-agent systems. Access to external data, a key
operational requirement for real-world utility, also increases
in sophistication, from absent or optional in Generative AI
to modular and coordinated in Agentic AI. Collectively, these
comparative views reinforce that the evolution from generative
to agentic paradigms is marked not just by increasing system
complexity but also by deeper integration of autonomy, mem-
ory, and decision-making across multiple levels of abstraction.
Furthermore, to provide a deeper multi-dimensional un-
derstanding of the evolving agentic landscape, Tables V
through IX extend the comparative taxonomy to dissect five
critical dimensions: core function and goal alignment, archi-
tectural composition, operational mechanism, scope and com-
plexity, and interaction-autonomy dynamics. These dimensions
serve to not only reinforce the structural differences between
Generative AI, AI Agents, and Agentic AI, but also introduce
an emergent category Generative Agents representing modular
agents designed for embedded subtask-level generation within
broader workflows [150]. Table V situates the three paradigms
in terms of their overarching goals and functional intent. While
Generative AI centers on prompt-driven content generation,
AI Agents emphasize tool-based task execution, and Agentic
AI systems orchestrate full-fledged workflows. This functional
expansion is mirrored architecturally in Table VI, where the
system design transitions from single-model reliance (in Gen-
erative AI) to multi-agent orchestration and shared memory
utilization in Agentic AI. Table VII then outlines how these
paradigms differ in their workflow execution pathways, high-
lighting the rise of inter-agent coordination and hierarchical
communication as key drivers of agentic behavior.
Furthermore, Table VIII explores the increasing scope and
operational complexity handled by these systems ranging
from isolated content generation to adaptive, multi-agent col-
laboration in dynamic environments. Finally, Table IX syn-
thesizes the varying degrees of autonomy, interaction style,
and decision-making granularity across the paradigms. These
tables collectively establish a rigorous framework to classify
and analyze agent-based AI systems, laying the groundwork
for principled evaluation and future design of autonomous,
intelligent, and collaborative agents operating at scale. | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-21T00:48:59+00:00", "moddate": "2025-05-21T00:48:59+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "trapped": "/False", "source": "data\\raw\\ai_agents_vs_agentic_ai_2505.10468.pdf", "total_pages": 33, "page": 9, "page_label": "10", "loader_type": "pdf", "load_timestamp": "2025-05-21T10:22:32.134920"} |
TABLE II: Taxonomy Summary of AI Agent Paradigms: Conceptual and Cognitive Dimensions
Conceptual Dimension Generative AI AI Agent Agentic AI Generative Agent
(Inferred)
Initiation Type Prompt-triggered by user or
input
Prompt or goal-triggered
with tool use
Goal-initiated or orchestrated
task
Prompt or system-level trig-
ger
Goal Flexibility (None) fixed per prompt (Low) executes specific goal (High) decomposes and
adapts goals
(Low) guided by subtask
goal
Temporal Continuity Stateless, single-session out-
put
Short-term continuity within
task
Persistent across workflow
stages
Context-limited to subtask
Learning/Adaptation Static (pretrained) (Might in future) Tool selec-
tion strategies may evolve
(Yes) Learns from outcomes Typically static; limited
adaptation
Memory Use No memory or short context
window
Optional memory or tool
cache
Shared episodic/task mem-
ory
Subtask-local or contextual
memory
Coordination Strategy None (single-step process) Isolated task execution Hierarchical or decentralized
coordination
Receives instructions from
system
System Role Content generator Tool-using task executor Collaborative workflow or-
chestrator
Subtask-level modular gener-
ator
TABLE III: Key Attributes of AI Agents, Agentic AI, and
Generative Agents
Aspect AI Agent Agentic AI Generative
Agent
Primary Ca-
pability
Task execution Autonomous
goal setting
Content genera-
tion
Planning
Horizon
Single-step Multi-step N/A (content
only)
Learning
Mechanism
Rule-based or
supervised
Reinforcement/meta-
learning
Large-scale pre-
training
Interaction
Style
Reactive Proactive Creative
Evaluation
Focus
Accuracy,
latency
Engagement,
adaptability
Coherence, diver-
sity
TABLE IV: Comparison of Generative AI, AI Agents, and
Agentic AI
Feature Generative AI AI Agent Agentic AI
Core
Function
Content genera-
tion
Task-specific
execution using
tools
Complex
workflow
automation
Mechanism Prompt → LLM
→ Output
Prompt → Tool
Call → LLM →
Output
Goal → Agent
Orchestration →
Output
Structure Single model LLM + tool(s) Multi-agent sys-
tem
External
Data
Access
None (unless
added)
Via external APIs Coordinated
multi-agent
access
Key Trait Reactivity Tool-use Collaboration
Each of the comparative tables presented from Table V
through Table IX offers a layered analytical lens to isolate
the distinguishing attributes of Generative AI, AI Agents, and
Agentic AI, thereby grounding the conceptual taxonomy in
concrete operational and architectural features. Table V, for
instance, addresses the most fundamental layer of differentia-
tion: core function and system goal. While Generative AI is
narrowly focused on reactive content production conditioned
on user prompts, AI Agents are characterized by their ability
to perform targeted tasks using external tools. Agentic AI,
by contrast, is defined by its ability to pursue high-level
goals through the orchestration of multiple subagents each
addressing a component of a broader workflow. This shift
from output generation to workflow execution marks a critical
inflection point in the evolution of autonomous systems.
In Table VI, the architectural distinctions are made explicit,
especially in terms of system composition and control logic.
Generative AI relies on a single model with no built-in capabil-
ity for tool use or delegation, whereas AI Agents combine lan-
guage models with auxiliary APIs and interface mechanisms
to augment functionality. Agentic AI extends this further by
introducing multi-agent systems where collaboration, memory
persistence, and orchestration protocols are central to the
system’s operation. This expansion is crucial for enabling
intelligent delegation, context preservation, and dynamic role
assignment capabilities absent in both generative and single-
agent systems. Likewise in Table VII dives deeper into how
these systems function operationally, emphasizing differences
in execution logic and information flow. Unlike Generative
AI’s linear pipeline (prompt → output), AI Agents implement
procedural mechanisms to incorporate tool responses mid-
process. Agentic AI introduces recursive task reallocation and
cross-agent messaging, thus facilitating emergent decision-
making that cannot be captured by static LLM outputs alone.
Table VIII further reinforces these distinctions by mapping
each system’s capacity to handle task diversity, temporal scale,
and operational robustness. Here, Agentic AI emerges as
uniquely capable of supporting high-complexity goals that de-
mand adaptive, multi-phase reasoning and execution strategies.
Furthermore, Table IX brings into sharp relief the opera-
tional and behavioral distinctions across Generative AI, AI
Agents, and Agentic AI, with a particular focus on autonomy
levels, interaction styles, and inter-agent coordination. Gener-
ative AI systems, typified by models such as GPT-3 [114]
and and DALL·E https://openai.com/index/dall-e-3/, remain
reactive generating content solely in response to prompts | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-21T00:48:59+00:00", "moddate": "2025-05-21T00:48:59+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "trapped": "/False", "source": "data\\raw\\ai_agents_vs_agentic_ai_2505.10468.pdf", "total_pages": 33, "page": 10, "page_label": "11", "loader_type": "pdf", "load_timestamp": "2025-05-21T10:22:32.134921"} |
TABLE V: Comparison by Core Function and Goal
Feature Generative AI AI Agent Agentic AI Generative Agent
(Inferred)
Primary Goal Create novel content based
on prompt
Execute a specific task us-
ing external tools
Automate complex work-
flow or achieve high-level
goals
Perform a specific genera-
tive sub-task
Core Function Content generation (text,
image, audio, etc.)
Task execution with exter-
nal interaction
Workflow orchestration and
goal achievement
Sub-task content generation
within a workflow
TABLE VI: Comparison by Architectural Components
Component Generative AI AI Agent Agentic AI Generative Agent
(Inferred)
Core Engine LLM / LIM LLM Multiple LLMs (potentially
diverse)
LLM
Prompts Yes (input trigger) Yes (task guidance) Yes (system goal and agent
tasks)
Yes (sub-task guidance)
Tools/APIs No (inherently) Yes (essential) Yes (available to constituent
agents)
Potentially (if sub-task re-
quires)
Multiple Agents No No Yes (essential; collabora-
tive)
No (is an individual agent)
Orchestration No No Yes (implicit or explicit) No (is part of orchestration)
TABLE VII: Comparison by Operational Mechanism
Mechanism Generative AI AI Agent Agentic AI Generative Agent
(Inferred)
Primary Driver Reactivity to prompt Tool calling for task execu-
tion
Inter-agent communication
and collaboration
Reactivity to input or sub-
task prompt
Interaction Mode User → LLM User → Agent → Tool User → System → Agents System/Agent → Agent →
Output
Workflow Handling Single generation step Single task execution Multi-step workflow coordi-
nation
Single step within workflow
Information Flow Input → Output Input → Tool → Output Input → Agent1 → Agent2
→ ... → Output
Input (from system/agent)
→ Output
TABLE VIII: Comparison by Scope and Complexity
Aspect Generative AI AI Agent Agentic AI Generative Agent
(Inferred)
Task Scope Single piece of generated
content
Single, specific, defined task Complex, multi-faceted
goal or workflow
Specific sub-task (often
generative)
Complexity Low (relative) Medium (integrates tools) High (multi-agent coordina-
tion)
Low to Medium (one task
component)
Example (Video) Chatbot Tavily Search Agent YouTube-to-Blog
Conversion System
Title/Description/Conclusion
Generator
TABLE IX: Comparison by Interaction and Autonomy
Feature Generative AI AI Agent Agentic AI Generative Agent
(Inferred)
Autonomy Level Low (requires prompt) Medium (uses tools au-
tonomously)
High (manages entire pro-
cess)
Low to Medium (executes
sub-task)
External Interaction None (baseline) Via specific tools or APIs Through multiple
agents/tools
Possibly via tools (if
needed)
Internal Interaction N/A N/A High (inter-agent) Receives input from system
or agent
Decision Making Pattern selection Tool usage decisions Goal decomposition and as-
signment
Best sub-task generation
strategy | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-21T00:48:59+00:00", "moddate": "2025-05-21T00:48:59+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "trapped": "/False", "source": "data\\raw\\ai_agents_vs_agentic_ai_2505.10468.pdf", "total_pages": 33, "page": 11, "page_label": "12", "loader_type": "pdf", "load_timestamp": "2025-05-21T10:22:32.134922"} |
without maintaining persistent state or engaging in iterative
reasoning. In contrast, AI Agents such as those constructed
with LangChain [93] or MetaGPT [151], exhibit a higher
degree of autonomy, capable of initiating external tool invoca-
tions and adapting behaviors within bounded tasks. However,
their autonomy is typically confined to isolated task execution,
lacking long-term state continuity or collaborative interaction.
Agentic AI systems mark a significant departure from these
paradigms by introducing internal orchestration mechanisms
and multi-agent collaboration frameworks. For example, plat-
forms like AutoGen [94] and ChatDev [149] exemplify agentic
coordination through task decomposition, role assignment,
and recursive feedback loops. In AutoGen, one agent might
serve as a planner while another retrieves information and
a third synthesizes a report, each communicating through
shared memory buffers and governed by an orchestrator agent
that monitors dependencies and overall task progression. This
structured coordination allows for more complex goal pur-
suit and flexible behavior in dynamic environments. Such
architectures fundamentally shift the focus of intelligence
from single-model outputs to emergent system-level behavior,
wherein agents learn, negotiate, and update decisions based on
evolving task states. Thus, the comparative taxonomy not only
highlights increasing levels of operational independence but
also illustrates how Agentic AI introduces novel paradigms of
communication, memory integration, and decentralized con-
trol, paving the way for the next generation of autonomous
systems with scalable, adaptive intelligence.
A. Architectural Evolution: From AI Agents to Agentic AI
Systems
While both AI Agents and Agentic AI systems are grounded
in modular design principles, Agentic AI significantly extends
the foundational architecture to support more complex, dis-
tributed, and adaptive behaviors. As illustrated in Figure 8,
the transition begins with core subsystems Perception, Rea-
soning, and Action, that define traditional AI Agents. Agentic
AI enhances this base by integrating advanced components
such as Specialized Agents, Advanced Reasoning & Plan-
ning, Persistent Memory, and Orchestration. The figure further
emphasizes emergent capabilities including Multi-Agent Col-
laboration, System Coordination, Shared Context, and Task
Decomposition, all encapsulated within a dotted boundary
that signifies the shift toward reflective, decentralized, and
goal-driven system architectures. This progression marks a
fundamental inflection point in intelligent agent design. This
section synthesizes findings from empirical frameworks such
as LangChain [93], AutoGPT [94], and TaskMatrix [152],
highlighting this progression in architectural sophistication.
1) Core Architectural Components of AI Agents: Foun-
dational AI Agents are typically composed of four primary
subsystems: perception, reasoning, action, and learning. These
subsystems form a closed-loop operational cycle, commonly
referred to as “Understand, Think, Act” from a user interface
perspective, or “Input, Processing, Action, Learning” in sys-
tems design literature [14], [153].
• Perception Module: This subsystem ingests input signals
from users (e.g., natural language prompts) or external
systems (e.g., APIs, file uploads, sensor streams). It is
responsible for pre-processing data into a format inter-
pretable by the agent’s reasoning module. For example,
in LangChain-based agents [93], [154], the perception
layer handles prompt templating, contextual wrapping,
and retrieval augmentation via document chunking and
embedding search.
• Knowledge Representation and Reasoning (KRR)
Module: At the core of the agent’s intelligence lies
the KRR module, which applies symbolic, statistical, or
hybrid logic to input data. Techniques include rule-based
logic (e.g., if-then decision trees), deterministic workflow
engines, and simple planning graphs. Reasoning in agents
like AutoGPT [30] is enhanced with function-calling
and prompt chaining to simulate thought processes (e.g.,
“step-by-step” prompts or intermediate tool invocations).
• Action Selection and Execution Module: This module
translates inferred decisions into external actions using
an action library. These actions may include sending
messages, updating databases, querying APIs, or pro-
ducing structured outputs. Execution is often managed
by middleware like LangChain’s “agent executor,” which
links LLM outputs to tool calls and observes responses
for subsequent steps [93].
• Basic Learning and Adaptation: Traditional AI Agents
feature limited learning mechanisms, such as heuristic
parameter adjustment [155], [156] or history-informed
context retention. For instance, agents may use simple
memory buffers to recall prior user inputs or apply
scoring mechanisms to improve tool selection in future
iterations.
Customization of these agents typically involves domain-
specific prompt engineering, rule injection, or workflow tem-
plates, distinguishing them from hard-coded automation scripts
by their ability to make context-aware decisions. Systems like
ReAct [132] exemplify this architecture, combining reasoning
and action in an iterative framework where agents simulate
internal dialogue before selecting external actions.
2) Architectural Enhancements in Agentic AI: Agentic AI
systems inherit the modularity of AI Agents but extend
their architecture to support distributed intelligence, inter-
agent communication, and recursive planning. The literature
documents a number of critical architectural enhancements
that differentiate Agentic AI from its predecessors [157],
[158].
• Ensemble of Specialized Agents: Rather than operating
as a monolithic unit, Agentic AI systems consist of
multiple agents, each assigned a specialized function e.g.,
a summarizer, a retriever, a planner. These agents inter-
act via communication channels (e.g., message queues,
blackboards, or shared memory). For instance, MetaGPT
[151] exemplify this approach by modeling agents after
corporate departments (e.g., CEO, CTO, engineer), where | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-21T00:48:59+00:00", "moddate": "2025-05-21T00:48:59+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "trapped": "/False", "source": "data\\raw\\ai_agents_vs_agentic_ai_2505.10468.pdf", "total_pages": 33, "page": 12, "page_label": "13", "loader_type": "pdf", "load_timestamp": "2025-05-21T10:22:32.134924"} |
Multi-Agent
Collaboration Task-Decomposition
Shared Context
System Coordination
AI Agents
Agentic AI
Fig. 8: Illustrating architectural evolution from traditional AI Agents to modern Agentic AI systems. It begins with core
modules Perception, Reasoning, and Action and expands into advanced components including Specialized Agents, Advanced
Reasoning & Planning, Persistent Memory, and Orchestration. The diagram further captures emergent properties such as Multi-
Agent Collaboration, System Coordination, Shared Context, and Task Decomposition, all enclosed within a dotted boundary
signifying layered modularity and the transition to distributed, adaptive agentic AI intelligence.
roles are modular, reusable, and role-bound.
• Advanced Reasoning and Planning: Agentic systems
embed recursive reasoning capabilities using frameworks
such as ReAct [132], Chain-of-Thought (CoT) prompting
[159], and Tree of Thoughts [160]. These mechanisms
allow agents to break down a complex task into multiple
reasoning stages, evaluate intermediate results, and re-
plan actions dynamically. This enables the system to
respond adaptively to uncertainty or partial failure.
• Persistent Memory Architectures: Unlike traditional
agents, Agentic AI incorporates memory subsystems to
persist knowledge across task cycles or agent sessions
[161], [162]. Memory types include episodic memory
(task-specific history) [163], [164], semantic memory
(long-term facts or structured data) [165], [166], and
vector-based memory for retrieval-augmented generation
(RAG) [167], [168]. For example, AutoGen [94] agents
maintain scratchpads for intermediate computations, en-
abling stepwise task progression.
• Orchestration Layers / Meta-Agents: A key innovation
in Agentic AI is the introduction of orchestrators meta-
agents that coordinate the lifecycle of subordinate agents,
manage dependencies, assign roles, and resolve conflicts.
Orchestrators often include task managers, evaluators, or
moderators. In ChatDev [149], for example, a virtual
CEO meta-agent distributes subtasks to departmental
agents and integrates their outputs into a unified strategic
response.
These enhancements collectively enable Agentic AI to sup-
port scenarios that require sustained context, distributed labor,
multi-modal coordination, and strategic adaptation. Use cases
range from research assistants that retrieve, summarize, and
draft documents in tandem (e.g., AutoGen pipelines [94])
to smart supply chain agents that monitor logistics, vendor
performance, and dynamic pricing models in parallel.
The shift from isolated perception–reasoning–action loops
to collaborative and reflective multi-agent workflows marks a
key inflection point in the architectural design of intelligent
systems. This progression positions Agentic AI as the next
stage of AI infrastructure capable not only of executing
predefined workflows but also of constructing, revising, and
managing complex objectives across agents with minimal
human supervision.
IV. A PPLICATION OF AI AGENTS AND AGENTIC AI
To illustrate the real-world utility and operational diver-
gence between AI Agents and Agentic AI systems, this study | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-21T00:48:59+00:00", "moddate": "2025-05-21T00:48:59+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "trapped": "/False", "source": "data\\raw\\ai_agents_vs_agentic_ai_2505.10468.pdf", "total_pages": 33, "page": 13, "page_label": "14", "loader_type": "pdf", "load_timestamp": "2025-05-21T10:22:32.134925"} |
Customer Support
Automation and
Internal Enterprise
Search
Email Filtering and
Prioritization
Personalized Content
Recommendation,
Basic Data Analysis
and Reporting
Autonomous
Scheduling
Assistants
Multi-Agent
Research Assistants
Intelligent Robotics
Coordination
Collaborative
Medical Decision
Support
Multi-Agent Game
AI & Adaptive
Workflow
Automation
Fig. 9: Categorized applications of AI Agents and Agentic AI across eight core functional domains.
synthesizes a range of applications drawn from recent litera-
ture, as visualized in Figure 9. We systematically categorize
and analyze application domains across two parallel tracks:
conventional AI Agent systems and their more advanced
Agentic AI counterparts. For AI Agents, four primary use
cases are reviewed: (1) Customer Support Automation and
Internal Enterprise Search, where single-agent models handle
structured queries and response generation; (2) Email Filtering
and Prioritization, where agents assist users in managing
high-volume communication through classification heuristics;
(3) Personalized Content Recommendation and Basic Data
Reporting, where user behavior is analyzed for automated
insights; and (4) Autonomous Scheduling Assistants, which
interpret calendars and book tasks with minimal user input.
In contrast, Agentic AI applications encompass broader and
more dynamic capabilities, reviewed through four additional
categories: (1) Multi-Agent Research Assistants that retrieve,
synthesize, and draft scientific content collaboratively; (2)
Intelligent Robotics Coordination, including drone and multi-
robot systems in fields like agriculture and logistics; (3)
Collaborative Medical Decision Support, involving diagnostic,
treatment, and monitoring subsystems; and (4) Multi-Agent
Game AI and Adaptive Workflow Automation, where decen-
tralized agents interact strategically or handle complex task
pipelines.
1) Application of AI Agents:
1) Customer Support Automation and Internal Enter-
prise Search: AI Agents are widely adopted in en-
terprise environments for automating customer support
and facilitating internal knowledge retrieval. In cus-
tomer service, these agents leverage retrieval-augmented
LLMs interfaced with APIs and organizational knowl-
edge bases to answer user queries, triage tickets, and
perform actions like order tracking or return initia-
tion [47]. For internal enterprise search, agents built
on vector stores (e.g., Pinecone, Elasticsearch) retrieve
semantically relevant documents in response to natu-
ral language queries. Tools such as Salesforce Ein-
stein https://www.salesforce.com/artificial-intelligence/,
Intercom Fin https://www.intercom.com/fin, and Notion
AI https://www.notion.com/product/ai demonstrate how
structured input processing and summarization capabil-
ities reduce workload and improve enterprise decision-
making.
A practical example (Figure 10a) of this dual func-
tionality can be seen in a multinational e-commerce
company deploying an AI Agent-based customer support
and internal search assistant. For customer support, the
AI Agent integrates with the company’s CRM (e.g.,
Salesforce) and fulfillment APIs to resolve queries such
as “Where is my order?” or “How can I return this
item?”. Within milliseconds, the agent retrieves con- | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-21T00:48:59+00:00", "moddate": "2025-05-21T00:48:59+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "trapped": "/False", "source": "data\\raw\\ai_agents_vs_agentic_ai_2505.10468.pdf", "total_pages": 33, "page": 14, "page_label": "15", "loader_type": "pdf", "load_timestamp": "2025-05-21T10:22:32.134926"} |
textual data from shipping databases and policy repos-
itories, then generates a personalized response using
retrieval-augmented generation. For internal enterprise
search, employees use the same system to query past
meeting notes, sales presentations, or legal documents.
When an HR manager types “summarize key benefits
policy changes from last year,” the agent queries a
Pinecone vector store embedded with enterprise doc-
umentation, ranks results by semantic similarity, and
returns a concise summary along with source links.
These capabilities not only reduce ticket volume and
support overhead but also minimize time spent searching
for institutional knowledge (like policies, procedures,
or manuals). The result is a unified, responsive system
that enhances both external service delivery and internal
operational efficiency using modular AI Agent architec-
tures.
2) Email Filtering and Prioritization: Within productivity
tools, AI Agents automate email triage through content
classification and prioritization. Integrated with systems
like Microsoft Outlook and Superhuman, these agents
analyze metadata and message semantics to detect ur-
gency, extract tasks, and recommend replies. They apply
user-tuned filtering rules, behavioral signals, and intent
classification to reduce cognitive overload. Autonomous
actions, such as auto-tagging or summarizing threads,
enhance efficiency, while embedded feedback loops en-
able personalization through incremental learning [63].
Figure10b illustrates a practical implementation of AI
Agents in the domain of email filtering and prioriti-
zation. In modern workplace environments, users are
inundated with high volumes of email, leading to cog-
nitive overload and missed critical communications. AI
Agents embedded in platforms like Microsoft Outlook
or Superhuman act as intelligent intermediaries that
classify, cluster, and triage incoming messages. These
agents evaluate metadata (e.g., sender, subject line) and
semantic content to detect urgency, extract actionable
items, and suggest smart replies. As depicted, the AI
agent autonomously categorizes emails into tags such
as “Urgent,” “Follow-up,” and “Low Priority,” while
also offering context-aware summaries and reply drafts.
Through continual feedback loops and usage patterns,
the system adapts to user preferences, gradually refining
classification thresholds and improving prioritization ac-
curacy. This automation offloads decision fatigue, allow-
ing users to focus on high-value tasks, while maintain-
ing efficient communication management in fast-paced,
information-dense environments.
3) Personalized Content Recommendation and Basic
Data Reporting: AI Agents support adaptive personal-
ization by analyzing behavioral patterns for news, prod-
uct, or media recommendations. Platforms like Amazon,
YouTube, and Spotify deploy these agents to infer user
preferences via collaborative filtering, intent detection,
and content ranking. Simultaneously, AI Agents in an-
(a)
(b)
(c)
(d)
Fig. 10: Applications of AI Agents in enterprise settings: (a)
Customer support and internal enterprise search; (b) Email
filtering and prioritization; (c) Personalized content recom-
mendation and basic data reporting; and (d) Autonomous
scheduling assistants. Each example highlights modular AI
Agent integration for automation, intent understanding, and
adaptive reasoning across operational workflows and user-
facing systems. | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-21T00:48:59+00:00", "moddate": "2025-05-21T00:48:59+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "trapped": "/False", "source": "data\\raw\\ai_agents_vs_agentic_ai_2505.10468.pdf", "total_pages": 33, "page": 15, "page_label": "16", "loader_type": "pdf", "load_timestamp": "2025-05-21T10:22:32.134927"} |
alytics systems (e.g., Tableau Pulse, Power BI Copi-
lot) enable natural-language data queries and automated
report generation by converting prompts to structured
database queries and visual summaries, democratizing
business intelligence access.
A practical illustration (Figure 10c) of AI Agents in
personalized content recommendation and basic data
reporting can be found in e-commerce and enterprise
analytics systems. Consider an AI agent deployed on a
retail platform like Amazon: as users browse, click, and
purchase items, the agent continuously monitors inter-
action patterns such as dwell time, search queries, and
purchase sequences. Using collaborative filtering and
content-based ranking, the agent infers user intent and
dynamically generates personalized product suggestions
that evolve over time. For example, after purchasing
gardening tools, a user may be recommended compat-
ible soil sensors or relevant books. This level of per-
sonalization enhances customer engagement, increases
conversion rates, and supports long-term user retention.
Simultaneously, within a corporate setting, an AI agent
integrated into Power BI Copilot allows non-technical
staff to request insights using natural language, for
instance, “Compare Q3 and Q4 sales in the Northeast.”
The agent translates the prompt into structured SQL
queries, extracts patterns from the database, and outputs
a concise visual summary or narrative report. This
application reduces dependency on data analysts and
empowers broader business decision-making through
intuitive, language-driven interfaces.
4) Autonomous Scheduling Assistants: AI Agents in-
tegrated with calendar systems autonomously manage
meeting coordination, rescheduling, and conflict reso-
lution. Tools like x.ai and Reclaim AI interpret vague
scheduling commands, access calendar APIs, and iden-
tify optimal time slots based on learned user preferences.
They minimize human input while adapting to dynamic
availability constraints. Their ability to interface with
enterprise systems and respond to ambiguous instruc-
tions highlights the modular autonomy of contemporary
scheduling agents.
A practical application of autonomous scheduling agents
can be seen in corporate settings as depicted in Fig-
ure 10d where employees manage multiple overlapping
responsibilities across global time zones. Consider an
executive assistant AI agent integrated with Google
Calendar and Slack that interprets a command like “Find
a 45-minute window for a follow-up with the product
team next week.” The agent parses the request, checks
availability for all participants, accounts for time zone
differences, and avoids meeting conflicts or working-
hour violations. If it identifies a conflict with a pre-
viously scheduled task, it may autonomously propose
alternative windows and notify affected attendees via
Slack integration. Additionally, the agent learns from
historical user preferences such as avoiding early Friday
meetings and refines its suggestions over time. Tools
like Reclaim AI and Clockwise exemplify this capabil-
ity, offering calendar-aware automation that adapts to
evolving workloads. Such assistants reduce coordination
overhead, increase scheduling efficiency, and enable
smoother team workflows by proactively resolving am-
biguity and optimizing calendar utilization.
TABLE X: Representative AI Agents (2023–2025): Applica-
tions and Operational Characteristics
Model / Reference Application
Area
Operation as AI Agent
ChatGPT Deep Re-
search Mode
OpenAI (2025) Deep
Research OpenAI
Research Analy-
sis / Reporting
Synthesizes hundreds of
sources into reports; functions
as a self-directed research
analyst.
Operator
OpenAI (2025) Opera-
tor OpenAI
Web Automation Navigates websites, fills forms,
and completes online tasks au-
tonomously.
Agentspace: Deep Re-
search Agent
Google (2025) Google
Agentspace
Enterprise
Reporting
Generates business
intelligence reports using
Gemini models.
NotebookLM Plus
Agent
Google (2025)
NotebookLM
Knowledge Man-
agement
Summarizes, organizes, and
retrieves data across Google
Workspace apps.
Nova Act
Amazon (2025) Ama-
zon Nova
Workflow
Automation
Automates browser-based
tasks such as scheduling, HR
requests, and email.
Manus Agent
Monica (2025) Manus
Agenthttps://manus.im/
Personal Task
Automation
Executes trip planning, site
building, and product compar-
isons via browsing.
Harvey
Harvey AI (2025) Har-
vey
Legal
Automation
Automates document drafting,
legal review, and predictive
case analysis.
Otter Meeting Agent
Otter.ai (2025) Otter
Meeting
Management
Transcribes meetings and pro-
vides highlights, summaries,
and action items.
Otter Sales Agent
Otter.ai (2025) Otter
sales agent
Sales
Enablement
Analyzes sales calls, extracts
insights, and suggests follow-
ups.
ClickUp Brain
ClickUp (2025)
ClickUp Brain
Project Manage-
ment
Automates task tracking, up-
dates, and project workflows.
Agentforce
Agentforce (2025)
Agentforce
Customer
Support
Routes tickets and generates
context-aware replies for sup-
port teams.
Microsoft Copilot
Microsoft (2024) Mi-
crosoft Copilot
Office Productiv-
ity
Automates writing, formula
generation, and summarization
in Microsoft 365.
Project Astra
Google DeepMind
(2025) Project Astra
Multimodal As-
sistance
Processes text, image, audio,
and video for task support and
recommendations.
Claude 3.5 Agent
Anthropic (2025)
Claude 3.5 Sonnet
Enterprise Assis-
tance
Uses multimodal input for rea-
soning, personalization, and
enterprise task completion.
2) Appications of Agentic AI:
1) Multi-Agent Research Assistants: Agentic AI systems
are increasingly deployed in academic and industrial
research pipelines to automate multi-stage knowledge
work. Platforms like AutoGen and CrewAI assign spe-
cialized roles to multiple agents retrievers, summarizers, | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-21T00:48:59+00:00", "moddate": "2025-05-21T00:48:59+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "trapped": "/False", "source": "data\\raw\\ai_agents_vs_agentic_ai_2505.10468.pdf", "total_pages": 33, "page": 16, "page_label": "17", "loader_type": "pdf", "load_timestamp": "2025-05-21T10:22:32.134928"} |
synthesizers, and citation formatters under a central
orchestrator. The orchestrator distributes tasks, manages
role dependencies, and integrates outputs into coherent
drafts or review summaries. Persistent memory allows
for cross-agent context sharing and refinement over
time. These systems are being used for literature re-
views, grant preparation, and patent search pipelines,
outperforming single-agent systems such as ChatGPT by
enabling concurrent sub-task execution and long-context
management [94].
For example, a real-world application of agentic AI as
depicted in Figure 11a is in the automated drafting of
grant proposals. Consider a university research group
preparing a National Science Foundation (NSF) sub-
mission. Using an AutoGen-based architecture, distinct
agents are assigned: one retrieves prior funded proposals
and extracts structural patterns; another scans recent
literature to summarize related work; a third agent aligns
proposal objectives with NSF solicitation language; and
a formatting agent structures the document per com-
pliance guidelines. The orchestrator coordinates these
agents, resolving dependencies (e.g., aligning methodol-
ogy with objectives) and ensuring stylistic consistency
across sections. Persistent memory modules store evolv-
ing drafts, feedback from collaborators, and funding
agency templates, enabling iterative improvement over
multiple sessions. Compared to traditional manual pro-
cesses, this multi-agent system significantly accelerates
drafting time, improves narrative cohesion, and ensures
regulatory alignment offering a scalable, adaptive ap-
proach to collaborative scientific writing in academia
and R&D-intensive industries.
2) Intelligent Robotics Coordination: In robotics and
automation, Agentic AI underpins collaborative behav-
ior in multi-robot systems. Each robot operates as a
task specialized agent such as pickers, transporters, or
mappers while an orchestrator supervises and adapts
workflows. These architectures rely on shared spatial
memory, real-time sensor fusion, and inter-agent syn-
chronization for coordinated physical actions. Use cases
include warehouse automation, drone-based orchard in-
spection, and robotic harvesting [151]. For instance,
agricultural drone swarms may collectively map tree
rows, identify diseased fruits, and initiate mechanical
interventions. This dynamic allocation enables real-time
reconfiguration and autonomy across agents facing un-
certain or evolving environments.
For example, in commercial apple orchards (Figure 11b),
Agentic AI enables a coordinated multi-robot system
to optimize the harvest season. Here, task-specialized
robots such as autonomous pickers, fruit classifiers,
transport bots, and drone mappers operate as agentic
units under a central orchestrator. The mapping drones
first survey the orchard and use vision-language models
(VLMs) to generate high-resolution yield maps and
identify ripe clusters. This spatial data is shared via a
centralized memory layer accessible by all agents. Picker
robots are assigned to high-density zones, guided by
path-planning agents that optimize routes around obsta-
cles and labor zones. Simultaneously, transport agents
dynamically shuttle crates between pickers and storage,
adjusting tasks in response to picker load levels and
terrain changes. All agents communicate asynchronously
through a shared protocol, and the orchestrator contin-
uously adjusts task priorities based on weather fore-
casts or mechanical faults. If one picker fails, nearby
units autonomously reallocate workload. This adaptive,
memory-driven coordination exemplifies Agentic AI’s
potential to reduce labor costs, increase harvest effi-
ciency, and respond to uncertainties in complex agricul-
tural environments far surpassing the rigid programming
of legacy agricultural robots [94], [151].
3) Collaborative Medical Decision Support: In high-
stakes clinical environments, Agentic AI enables dis-
tributed medical reasoning by assigning tasks such as
diagnostics, vital monitoring, and treatment planning
to specialized agents. For example, one agent may
retrieve patient history, another validates findings against
diagnostic guidelines, and a third proposes treatment op-
tions. These agents synchronize through shared memory
and reasoning chains, ensuring coherent and safe rec-
ommendations. Applications include ICU management,
radiology triage, and pandemic response. Real-world
pilots show improved efficiency and decision accuracy
compared to isolated expert systems [92].
For example, in a hospital ICU (Figure 11c), an agentic
AI system supports clinicians in managing complex
patient cases. A diagnostic agent continuously ana-
lyzes vitals and lab data for early detection of sepsis
risk. Simultaneously, a history retrieval agent accesses
electronic health records (EHRs) to summarize comor-
bidities and recent procedures. A treatment planning
agent cross-references current symptoms with clinical
guidelines (e.g., Surviving Sepsis Campaign), proposing
antibiotic regimens or fluid protocols. The orchestra-
tor integrates these insights, ensures consistency, and
surfaces conflicts for human review. Feedback from
physicians is stored in a persistent memory module,
allowing agents to refine their reasoning based on prior
interventions and outcomes. This coordinated system
enhances clinical workflow by reducing cognitive load,
shortening decision times, and minimizing oversight
risks. Early deployments in critical care and oncology
units have demonstrated increased diagnostic precision
and better adherence to evidence-based protocols, offer-
ing a scalable solution for safer, real-time collaborative
medical support.
4) Multi-Agent Game AI and Adaptive Workflow Au-
tomation: In simulation environments and enterprise
systems, Agentic AI facilitates decentralized task exe-
cution and emergent coordination. Game platforms like
AI Dungeon deploy independent NPC agents with goals, | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-21T00:48:59+00:00", "moddate": "2025-05-21T00:48:59+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "trapped": "/False", "source": "data\\raw\\ai_agents_vs_agentic_ai_2505.10468.pdf", "total_pages": 33, "page": 17, "page_label": "18", "loader_type": "pdf", "load_timestamp": "2025-05-21T10:22:32.134929"} |
Central Memory Layer
Retrieve prior
proposals Align with
solicitation
Structure the
document
Store evolving
drafts
Goal
Module
Memory
Store
(a) (b)
(c) (d)
Using Agentic AI to
coordinate robotic harvest
Fig. 11: Illustrative Applications of Agentic AI Across Domains: Figure 11 presents four real-world applications of agentic AI
systems. (a) Automated grant writing using multi-agent orchestration for structured literature analysis, compliance alignment,
and document formatting. (b) Coordinated multi-robot harvesting in apple orchards using shared spatial memory and task-
specific agents for mapping, picking, and transport. (c) Clinical decision support in hospital ICUs through synchronized agents
for diagnostics, treatment planning, and EHR analysis, enhancing safety and workflow efficiency. (d) Cybersecurity incident
response in enterprise environments via agents handling threat classification, compliance analysis, and mitigation planning.
In all cases, central orchestrators manage inter-agent communication, shared memory enables context retention, and feedback
mechanisms drive continual learning. These use cases highlight agentic AI’s capacity for scalable, autonomous task coordination
in complex, dynamic environments across science, agriculture, healthcare, and IT security.
memory, and dynamic interactivity to create emergent
narratives and social behavior. In enterprise workflows,
systems such as MultiOn and Cognosys use agents to
manage processes like legal review or incident esca-
lation, where each step is governed by a specialized
module. These architectures exhibit resilience, exception
handling, and feedback-driven adaptability far beyond
rule-based pipelines.
For example, in a modern enterprise IT environment
(as depicted in Figure 11d), Agentic AI systems are
increasingly deployed to autonomously manage cyber-
security incident response workflows. When a potential | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-21T00:48:59+00:00", "moddate": "2025-05-21T00:48:59+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "trapped": "/False", "source": "data\\raw\\ai_agents_vs_agentic_ai_2505.10468.pdf", "total_pages": 33, "page": 18, "page_label": "19", "loader_type": "pdf", "load_timestamp": "2025-05-21T10:22:32.134930"} |
threat is detected such as abnormal access patterns or
unauthorized data exfiltration, specialized agents are
activated in parallel. One agent performs real-time threat
classification using historical breach data and anomaly
detection models. A second agent queries relevant log
data from network nodes and correlates patterns across
systems. A third agent interprets compliance frameworks
(e.g., GDPR or HIPAA) to assess the regulatory sever-
ity of the event. A fourth agent simulates mitigation
strategies and forecasts operational risks. These agents
coordinate under a central orchestrator that evaluates
collective outputs, integrates temporal reasoning, and
issues recommended actions to human analysts. Through
shared memory structures and iterative feedback, the
system learns from prior incidents, enabling faster and
more accurate responses in future cases. Compared
to traditional rule-based security systems, this agentic
model enhances decision latency, reduces false positives,
and supports proactive threat containment in large-scale
organizational infrastructures [94].
V. C HALLENGES AND LIMITATIONS IN AI AGENTS AND
AGENTIC AI
To systematically understand the operational and theoret-
ical limitations of current intelligent systems, we present a
comparative visual synthesis in Figure 12, which categorizes
challenges and potential remedies across both AI Agents and
Agentic AI paradigms. Figure 12a outlines the four most
pressing limitations specific to AI Agents namely, lack of
causal reasoning, inherited LLM constraints (e.g., hallucina-
tions, shallow reasoning), incomplete agentic properties (e.g.,
autonomy, proactivity), and failures in long-horizon planning
and recovery. These challenges often arise due to their reliance
on stateless LLM prompts, limited memory, and heuristic
reasoning loops.
In contrast, Figure 12b identifies eight critical bottlenecks
unique to Agentic AI systems, such as inter-agent error cas-
cades, coordination breakdowns, emergent instability, scala-
bility limits, and explainability issues. These challenges stem
from the complexity of orchestrating multiple agents across
distributed tasks without standardized architectures, robust
communication protocols, or causal alignment frameworks.
Figure 13 complements this diagnostic framework by syn-
thesizing ten forward-looking design strategies aimed at mit-
igating these limitations. These include Retrieval-Augmented
Generation (RAG), tool-based reasoning [126], [127], [129],
agentic feedback loops (ReAct [132]), role-based multi-agent
orchestration, memory architectures, causal modeling, and
governance-aware design. Together, these three panels offer
a consolidated roadmap for addressing current pitfalls and
accelerating the development of safe, scalable, and context-
aware autonomous systems.
1) Challenges and Limitations of AI Agents: While AI
Agents have garnered considerable attention for their ability to
automate structured tasks using LLMs and tool-use interfaces,
the literature highlights significant theoretical and practical
TABLE XI: Representative Agentic AI Models (2023–2025):
Applications and Operational Characteristics
Model / Reference Application
Area
Operation as Agentic AI
Auto-GPT
[30]
Task Automation Decomposes high-level
goals, executes subtasks
via tools/APIs, and
iteratively self-corrects.
GPT Engineer
Open Source (2023)
GPT Engineer
Code Generation Builds entire codebases:
plans, writes, tests, and re-
fines based on output.
MetaGPT
[151])
Software Collab-
oration
Coordinates specialized
agents (e.g., coder, tester)
for modular multi-role
project development.
BabyAGI
Nakajima (2024)
BabyAGI
Project Manage-
ment
Continuously creates, pri-
oritizes, and executes sub-
tasks to adaptively meet
user goals.
V oyager
Wang et al. (2023)
[169]
Game
Exploration
Learns in Minecraft, in-
vents new skills, sets sub-
goals, and adapts strategy
in real time.
CAMEL
Liu et al. (2023) [170]
Multi-Agent
Simulation
Simulates agent societies
with communication, ne-
gotiation, and emergent
collaborative behavior.
Einstein Copilot
Salesforce (2024) Ein-
stein Copilot
Customer
Automation
Automates full support
workflows, escalates is-
sues, and improves via
feedback loops.
Copilot Studio
(Agentic Mode)
Microsoft (2025)
Github Agentic
Copilot
Productivity Au-
tomation
Manages documents,
meetings, and projects
across Microsoft 365 with
adaptive orchestration.
Atera AI Copilot
Atera (2025) Atera
Agentic AI
IT Operations Diagnoses/resolves IT is-
sues, automates ticketing,
and learns from evolving
infrastructures.
AES Safety Audit
Agent
AES (2025) AES
agentic
Industrial Safety Automates audits,
assesses compliance,
and evolves strategies to
enhance safety outcomes.
DeepMind Gato
(Agentic Mode)
Reed et al. (2022)
[171]
General Robotics Performs varied tasks
across modalities,
dynamically learns,
plans, and executes.
GPT-4o + Plugins
OpenAI (2024) GPT-
4O Agentic
Enterprise
Automation
Manages complex work-
flows, integrates external
tools, and executes adap-
tive decisions.
limitations that inhibit their reliability, generalization, and
long-term autonomy [132], [158]. These challenges arise from
both the architectural dependence on static, pretrained models
and the difficulty of instilling agentic qualities such as causal
reasoning, planning, and robust adaptation. The key challenges
and limitations (Figure 12a) of AI Agents are as summarized
into following five points:
1) Lack of Causal Understanding: One of the most foun-
dational challenges lies in the agents’ inability to reason
causally [172], [173]. Current LLMs, which form the
cognitive core of most AI Agents, excel at identifying | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-21T00:48:59+00:00", "moddate": "2025-05-21T00:48:59+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "trapped": "/False", "source": "data\\raw\\ai_agents_vs_agentic_ai_2505.10468.pdf", "total_pages": 33, "page": 19, "page_label": "20", "loader_type": "pdf", "load_timestamp": "2025-05-21T10:22:32.134932"} |
(a) (b)
Fig. 12: Illustration of Chellenges: (a) Key limitations of AI Agents including causality deficits and shallow reasoning. (b)
Amplified coordination and stability challenges in Agentic AI systems.
statistical correlations within training data. However, as
noted in recent research from DeepMind and conceptual
analyses by TrueTheta, they fundamentally lack the
capacity for causal modeling distinguishing between
mere association and cause-effect relationships [174]–
[176]. For instance, while an LLM-powered agent might
learn that visiting a hospital often co-occurs with illness,
it cannot infer whether the illness causes the visit or vice
versa, nor can it simulate interventions or hypothetical
changes.
This deficit becomes particularly problematic under
distributional shifts, where real-world conditions differ
from the training regime [177], [178]. Without such
grounding, agents remain brittle, failing in novel or
high-stakes scenarios. For example, a navigation agent
that excels in urban driving may misbehave in snow or
construction zones if it lacks an internal causal model
of road traction or spatial occlusion.
2) Inherited Limitations from LLMs: AI Agents, particu-
larly those powered by LLMs, inherit a number of intrin-
sic limitations that impact their reliability, adaptability,
and overall trustworthiness in practical deployments
[179]–[181]. One of the most prominent issues is the ten-
dency to produce hallucinations plausible but factually
incorrect outputs. In high-stakes domains such as legal
consultation or scientific research, these hallucinations
can lead to severe misjudgments and erode user trust
[182], [183]. Compounding this is the well-documented
prompt sensitivity of LLMs, where even minor varia-
tions in phrasing can lead to divergent behaviors. This
brittleness hampers reproducibility, necessitating metic-
ulous manual prompt engineering and often requiring
domain-specific tuning to maintain consistency across
interactions [184].
Furthermore, while recent agent frameworks adopt rea-
soning heuristics like Chain-of-Thought (CoT) [159],
[185] and ReAct [132] to simulate deliberative pro-
cesses, these approaches remain shallow in semantic
comprehension. Agents may still fail at multi-step in-
ference, misalign task objectives, or make logically
inconsistent conclusions despite the appearance of struc-
tured reasoning [132]. Such shortcomings underscore
the absence of genuine understanding and generalizable
planning capabilities.
Another key limitation lies in computational cost and
latency. Each cycle of agentic decision-making partic-
ularly in planning or tool-calling may require several
LLM invocations. This not only increases runtime la-
tency but also scales resource consumption, creating
practical bottlenecks in real-world deployments and
cloud-based inference systems. Furthermore, LLMs have
a static knowledge cutoff and cannot dynamically in-
tegrate new information unless explicitly augmented
via retrieval or tool plugins. They also reproduce the
biases of their training datasets, which can manifest as
culturally insensitive or skewed responses [186], [187].
Without rigorous auditing and mitigation strategies,
these issues pose serious ethical and operational risks,
particularly when agents are deployed in sensitive or
user-facing contexts.
3) Incomplete Agentic Properties: A major limitation
of current AI Agents is their inability to fully satisfy
the canonical agentic properties defined in foundational
literature, such as autonomy, proactivity, reactivity, and
social ability [142], [181]. While many systems mar-
keted as ”agents” leverage LLMs to perform useful
tasks, they often fall short of these fundamental cri-
teria in practice. Autonomy, for instance, is typically | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-21T00:48:59+00:00", "moddate": "2025-05-21T00:48:59+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "trapped": "/False", "source": "data\\raw\\ai_agents_vs_agentic_ai_2505.10468.pdf", "total_pages": 33, "page": 20, "page_label": "21", "loader_type": "pdf", "load_timestamp": "2025-05-21T10:22:32.134933"} |
partial at best. Although agents can execute tasks with
minimal oversight once initialized, they remain heavily
reliant on external scaffolding such as human-defined
prompts, planning heuristics, or feedback loops to func-
tion effectively [188]. Self-initiated task generation, self-
monitoring, or autonomous error correction are rare or
absent, limiting their capacity for true independence.
Proactivity is similarly underdeveloped. Most AI Agents
require explicit user instruction to act and lack the capac-
ity to formulate or reprioritize goals dynamically based
on contextual shifts or evolving objectives [189]. As a
result, they behave reactively rather than strategically,
constrained by the static nature of their initialization. Re-
activity itself is constrained by architectural bottlenecks.
Agents do respond to environmental or user input, but
response latency caused by repeated LLM inference calls
[190], [191], coupled with narrow contextual memory
windows [161], [192], inhibits real-time adaptability.
Perhaps the most underexplored capability is social
ability. True agentic systems should communicate and
coordinate with humans or other agents over extended
interactions, resolving ambiguity, negotiating tasks, and
adapting to social norms.
However, existing implementations exhibit brittle,
template-based dialogue that lacks long-term memory
integration or nuanced conversational context. Agent-
to-agent interaction is often hardcoded or limited to
scripted exchanges, hindering collaborative execution
and emergent behavior [101], [193]. Collectively, these
deficiencies reveal that while AI Agents demonstrate
functional intelligence, they remain far from meeting the
formal benchmarks of intelligent, interactive, and adap-
tive agents. Bridging this gap is essential for advancing
toward more autonomous, socially capable AI systems.
4) Limited Long-Horizon Planning and Recovery: A
persistent limitation of current AI Agents lies in their
inability to perform robust long-horizon planning, es-
pecially in complex, multi-stage tasks. This constraint
stems from their foundational reliance on stateless
prompt-response paradigms, where each decision is
made without an intrinsic memory of prior reasoning
steps unless externally managed. Although augmenta-
tions such as the ReAct framework [132] or Tree-
of-Thoughts [160] introduce pseudo-recursive reason-
ing, they remain fundamentally heuristic and lack true
internal models of time, causality, or state evolution.
Consequently, agents often falter in tasks requiring ex-
tended temporal consistency or contingency planning.
For example, in domains such as clinical triage or
financial portfolio management, where decisions depend
on prior context and dynamically unfolding outcomes,
agents may exhibit repetitive behaviors such as endlessly
querying tools or fail to adapt when sub-tasks fail or
return ambiguous results. The absence of systematic
recovery mechanisms or error detection leads to brittle
workflows and error propagation. This shortfall severely
limits agent deployment in mission-critical environments
where reliability, fault tolerance, and sequential coher-
ence are essential.
5) Reliability and Safety Concerns: AI Agents are not
yet safe or verifiable enough for deployment in critical
infrastructure [194]. The absence of causal reasoning
leads to unpredictable behavior under distributional shift
[173], [195]. Furthermore, evaluating the correctness of
an agent’s plan especially when the agent fabricates
intermediate steps or rationales remains an unsolved
problem in interpretability [110], [196]. Safety guaran-
tees, such as formal verification, are not yet available
for open-ended, LLM-powered agents. While AI Agents
represent a major step beyond static generative models,
their limitations in causal reasoning, adaptability, robust-
ness, and planning restrict their deployment in high-
stakes or dynamic environments. Most current systems
rely on heuristic wrappers and brittle prompt engineering
rather than grounded agentic cognition. Bridging this
gap will require future systems to integrate causal mod-
els, dynamic memory, and verifiable reasoning mech-
anisms. These limitations also set the stage for the
emergence of Agentic AI systems, which attempt to
address these bottlenecks through multi-agent collabo-
ration, orchestration layers, and persistent system-level
context.
2) Challenges and Limitations of Agentic AI: Agentic AI
systems represent a paradigm shift from isolated AI agents to
collaborative, multi-agent ecosystems capable of decomposing
and executing complex goals [14]. These systems typically
consist of orchestrated or communicating agents that interact
via tools, APIs, and shared environments [18], [39]. While
this architectural evolution enables more ambitious automa-
tion, it introduces a range of amplified and novel challenges
that compound existing limitations of individual LLM-based
agents. The current challenges and limitations of Agentic AI
are as follows:
1) Amplified Causality Challenges: One of the most
critical limitations in Agentic AI systems is the magni-
fication of causality deficits already observed in single-
agent architectures. Unlike traditional AI Agents that
operate in relatively isolated environments, Agentic AI
systems involve complex inter-agent dynamics, where
each agent’s action can influence the decision space of
others. Without a robust capacity for modeling cause-
effect relationships, these systems struggle to coordinate
effectively and adapt to unforeseen environmental shifts.
A key manifestation of this challenge is inter-agent
distributional shift , where the behavior of one agent
alters the operational context for others. In the absence
of causal reasoning, agents are unable to anticipate the
downstream impact of their outputs, resulting in coor-
dination breakdowns or redundant computations [197].
Furthermore, these systems are particularly vulnerable to
error cascades: a faulty or hallucinated output from one | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-21T00:48:59+00:00", "moddate": "2025-05-21T00:48:59+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "trapped": "/False", "source": "data\\raw\\ai_agents_vs_agentic_ai_2505.10468.pdf", "total_pages": 33, "page": 21, "page_label": "22", "loader_type": "pdf", "load_timestamp": "2025-05-21T10:22:32.134933"} |
agent can propagate through the system, compounding
inaccuracies and corrupting subsequent decisions. For
example, if a verification agent erroneously validates
false information, downstream agents such as summariz-
ers or decision-makers may unknowingly build upon that
misinformation, compromising the integrity of the entire
system. This fragility underscores the urgent need for
integrating causal inference and intervention modeling
into the design of multi-agent workflows, especially in
high-stakes or dynamic environments where systemic
robustness is essential.
2) Communication and Coordination Bottlenecks: A
fundamental challenge in Agentic AI lies in achieving
efficient communication and coordination across mul-
tiple autonomous agents. Unlike single-agent systems,
Agentic AI involves distributed agents that must col-
lectively pursue a shared objective necessitating precise
alignment, synchronized execution, and robust commu-
nication protocols. However, current implementations
fall short in these aspects. One major issue is goal
alignment and shared context , where agents often lack
a unified semantic understanding of overarching objec-
tives. This hampers sub-task decomposition, dependency
management, and progress monitoring, especially in
dynamic environments requiring causal awareness and
temporal coherence.
In addition, protocol limitations significantly hinder
inter-agent communication. Most systems rely on nat-
ural language exchanges over loosely defined interfaces,
which are prone to ambiguity, inconsistent formatting,
and contextual drift. These communication gaps lead
to fragmented strategies, delayed coordination, and de-
graded system performance. Furthermore, resource con-
tention emerges as a systemic bottleneck when agents
simultaneously access shared computational, memory,
or API resources. Without centralized orchestration or
intelligent scheduling mechanisms, these conflicts can
result in race conditions, execution delays, or outright
system failures. Collectively, these bottlenecks illustrate
the immaturity of current coordination frameworks in
Agentic AI, and highlight the pressing need for stan-
dardized communication protocols, semantic task plan-
ners, and global resource managers to ensure scalable,
coherent multi-agent collaboration.
3) Emergent Behavior and Predictability: One of the
most critical limitations of Agentic AI lies in managing
emergent behavior complex system-level phenomena
that arise from the interactions of autonomous agents.
While such emergence can potentially yield adaptive and
innovative solutions, it also introduces significant unpre-
dictability and safety risks [153], [198]. A key concern
is the generation of unintended outcomes , where agent
interactions result in behaviors that were not explicitly
programmed or foreseen by system designers. These
behaviors may diverge from task objectives, generate
misleading outputs, or even enact harmful actions par-
ticularly in high-stakes domains like healthcare, finance,
or critical infrastructure.
As the number of agents and the complexity of their
interactions grow, so too does the likelihood of system
instability. This includes phenomena such as infinite
planning loops, action deadlocks, and contradictory
behaviors emerging from asynchronous or misaligned
agent decisions. Without centralized arbitration mecha-
nisms, conflict resolution protocols, or fallback strate-
gies, these instabilities compound over time, making
the system fragile and unreliable. The stochasticity and
opacity of large language model-based agents further
exacerbate this issue, as their internal decision logic is
not easily interpretable or verifiable. Consequently, en-
suring the predictability and controllability of emergent
behavior remains a central challenge in designing safe
and scalable Agentic AI systems.
4) Scalability and Debugging Complexity: As Agen-
tic AI systems scale in both the number of agents
and the diversity of specialized roles, maintaining sys-
tem reliability and interpretability becomes increas-
ingly complex [199], [200]. A central limitation stems
from the black-box chains of reasoning characteristic
of LLM-based agents. Each agent may process inputs
through opaque internal logic, invoke external tools,
and communicate with other agents all of which occur
through multiple layers of prompt engineering, reason-
ing heuristics, and dynamic context handling. Tracing
the root cause of a failure thus requires unwinding
nested sequences of agent interactions, tool invocations,
and memory updates, making debugging non-trivial and
time-consuming.
Another significant constraint is the system’s non-
compositionality. Unlike traditional modular systems,
where adding components can enhance overall func-
tionality, introducing additional agents in an Agentic
AI architecture often increases cognitive load, noise,
and coordination overhead. Poorly orchestrated agent
networks can result in redundant computation, contradic-
tory decisions, or degraded task performance. Without
robust frameworks for agent role definition, communica-
tion standards, and hierarchical planning, the scalability
of Agentic AI does not necessarily translate into greater
intelligence or robustness. These limitations highlight
the need for systematic architectural controls and trace-
ability tools to support the development of reliable,
large-scale agentic ecosystems.
5) Trust, Explainability, and Verification: Agentic AI
systems pose heightened challenges in explainability and
verifiability due to their distributed, multi-agent architec-
ture. While interpreting the behavior of a single LLM-
powered agent is already non-trivial, this complexity is
multiplied when multiple agents interact asynchronously
through loosely defined communication protocols. Each
agent may possess its own memory, task objective, and
reasoning path, resulting in compounded opacity where | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-21T00:48:59+00:00", "moddate": "2025-05-21T00:48:59+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "trapped": "/False", "source": "data\\raw\\ai_agents_vs_agentic_ai_2505.10468.pdf", "total_pages": 33, "page": 22, "page_label": "23", "loader_type": "pdf", "load_timestamp": "2025-05-21T10:22:32.134935"} |
tracing the causal chain of a final decision or failure
becomes exceedingly difficult. The lack of shared, trans-
parent logs or interpretable reasoning paths across agents
makes it nearly impossible to determine why a particular
sequence of actions occurred or which agent initiated a
misstep.
Compounding this opacity is the absence of formal
verification tools tailored for Agentic AI. Unlike tra-
ditional software systems, where model checking and
formal proofs offer bounded guarantees, there exists
no widely adopted methodology to verify that a multi-
agent LLM system will perform reliably across all input
distributions or operational contexts. This lack of verifia-
bility presents a significant barrier to adoption in safety-
critical domains such as autonomous vehicles, finance,
and healthcare, where explainability and assurance are
non-negotiable. To advance Agentic AI safely, future
research must address the foundational gaps in causal
traceability, agent accountability, and formal safety guar-
antees.
6) Security and Adversarial Risks: Agentic AI architec-
tures introduce a significantly expanded attack surface
compared to single-agent systems, exposing them to
complex adversarial threats. One of the most critical
vulnerabilities lies in the presence of a single point of
compromise. Since Agentic AI systems are composed of
interdependent agents communicating over shared mem-
ory or messaging protocols, the compromise of even
one agent through prompt injection, model poisoning,
or adversarial tool manipulation can propagate malicious
outputs or corrupted state across the entire system. For
example, a fact-checking agent fed with tampered data
could unintentionally legitimize false claims, which are
then integrated into downstream reasoning by summa-
rization or decision-making agents.
Moreover, inter-agent dynamics themselves are suscepti-
ble to exploitation. Attackers can induce race conditions,
deadlocks, or resource exhaustion by manipulating the
coordination logic between agents. Without rigorous
authentication, access control, and sandboxing mech-
anisms, malicious agents or corrupted tool responses
can derail multi-agent workflows or cause erroneous
escalation in task pipelines. These risks are exacerbated
by the absence of standardized security frameworks for
LLM-based multi-agent systems, leaving most current
implementations defenseless against sophisticated multi-
stage attacks. As Agentic AI moves toward broader
adoption, especially in high-stakes environments, em-
bedding secure-by-design principles and adversarial ro-
bustness becomes an urgent research imperative.
7) Ethical and Governance Challenges: The distributed
and autonomous nature of Agentic AI systems intro-
duces profound ethical and governance concerns, par-
ticularly in terms of accountability, fairness, and value
alignment. In multi-agent settings, accountability gaps
emerge when multiple agents interact to produce an
outcome, making it difficult to assign responsibility
for errors or unintended consequences. This ambiguity
complicates legal liability, regulatory compliance, and
user trust, especially in domains such as healthcare,
finance, or defense. Furthermore, bias propagation and
amplification present a unique challenge: agents in-
dividually trained on biased data may reinforce each
other’s skewed decisions through interaction, leading to
systemic inequities that are more pronounced than in
isolated models. These emergent biases can be subtle
and difficult to detect without longitudinal monitoring
or audit mechanisms.
Additionally, misalignment and value drift pose serious
risks in long-horizon or dynamic environments. With-
out a unified framework for shared value encoding,
individual agents may interpret overarching objectives
differently or optimize for local goals that diverge from
human intent. Over time, this misalignment can lead
to behavior that is inconsistent with ethical norms or
user expectations. Current alignment methods, which are
mostly designed for single-agent systems, are inadequate
for managing value synchronization across heteroge-
neous agent collectives. These challenges highlight the
urgent need for governance-aware agent architectures,
incorporating principles such as role-based isolation,
traceable decision logging, and participatory oversight
mechanisms to ensure ethical integrity in autonomous
multi-agent systems.
8) Immature Foundations and Research Gaps: Despite
rapid progress and high-profile demonstrations, Agentic
AI remains in a nascent research stage with unresolved
foundational issues that limit its scalability, reliability,
and theoretical grounding. A central concern is the
lack of standard architectures . There is currently no
widely accepted blueprint for how to design, monitor,
or evaluate multi-agent systems built on LLMs . This
architectural fragmentation makes it difficult to compare
implementations, replicate experiments, or generalize
findings across domains. Key aspects such as agent
orchestration, memory structures, and communication
protocols are often implemented ad hoc, resulting in
brittle systems that lack interoperability and formal
guarantees.
Equally critical is the absence of causal foundations as
scalable causal discovery and reasoning remain unsolved
challenges [201]. Without the ability to represent and
reason about cause-effect relationships, Agentic AI sys-
tems are inherently limited in their capacity to generalize
safely beyond narrow training regimes [178], [202].
This shortfall affects their robustness under distributional
shifts, their capacity for proactive intervention, and
their ability to simulate counterfactuals or hypothetical
plans core requirements for intelligent coordination and
decision-making.
The gap between functional demos and principled de-
sign thus underscores an urgent need for foundational | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-21T00:48:59+00:00", "moddate": "2025-05-21T00:48:59+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "trapped": "/False", "source": "data\\raw\\ai_agents_vs_agentic_ai_2505.10468.pdf", "total_pages": 33, "page": 23, "page_label": "24", "loader_type": "pdf", "load_timestamp": "2025-05-21T10:22:32.134936"} |
Retrieval-Augmented
Generation (RAG)
Tool-Augmented
Reasoning (Function
Calling)
Agentic Loop:
Reasoning, Action,
Observation
Reflexive and Self-
Critique Mechanisms
Programmatic Prompt
Engineering Pipelines
Causal Modeling
and Simulation-
Based Planning
Governance-Aware
Architectures
(Accountability +
Role Isolation)
Monitoring,
Auditing, and
Explainability
Pipelines
Memory Architectures
(Episodic, Semantic,
Vector)
Multi-Agent
Orchestration with
Role Specialization
Fig. 13: Ten emerging architectural and algorithmic solutions such as RAG, tool use, memory, orchestration, and reflexive
mechanisms addressing reliability, scalability, and explainability across both paradigms (AI Agents and Agentic AI)
research in multi-agent system theory, causal infer-
ence integration, and benchmark development. Only
by addressing these deficiencies can the field progress
from prototype pipelines to trustworthy, general-purpose
agentic frameworks suitable for deployment in high-
stakes environments.
VI. P OTENTIAL SOLUTIONS AND FUTURE ROADMAP
The potential solutions (as illustrated in Figure 13) to these
challenges and limitations of AI agents and Agentic AI are
summarized in the following points:
1) Retrieval-Augmented Generation (RAG): For AI
Agents, Retrieval-Augmented Generation mitigates hal-
lucinations and expands static LLM knowledge by
grounding outputs in real-time data [203]. By embed-
ding user queries and retrieving semantically relevant
documents from vector databases like FAISS Faiss or
Pinecone Pinecone, agents can generate contextually
valid responses rooted in external facts. This is par-
ticularly effective in domains such as enterprise search
and customer support, where accuracy and up-to-date
knowledge are essential.
In Agentic AI systems, RAG serves as a shared ground-
ing mechanism across agents. For example, a summa-
rizer agent may rely on the retriever agent to access
the latest scientific papers before generating a synthesis.
Persistent, queryable memory allows distributed agents
to operate on a unified semantic layer, mitigating in-
consistencies due to divergent contextual views. When
implemented across a multi-agent system, RAG helps
maintain shared truth, enhances goal alignment, and
reduces inter-agent misinformation propagation.
2) Tool-Augmented Reasoning (Function Calling): AI
Agents benefit significantly from function calling, which
extends their ability to interact with real-world systems
[167], [204]. Agents can query APIs, run local scripts,
or access structured databases, thus transforming LLMs
from static predictors into interactive problem-solvers
[131], [162]. This allows them to dynamically retrieve
weather forecasts, schedule appointments, or execute
Python-based calculations, all beyond the capabilities of
pure language modeling.
For Agentic AI, function calling supports agent level
autonomy and role differentiation. Agents within a team
may use APIs to invoke domain-specific actions such as
querying clinical databases or generating visual charts
based on assigned roles. Function calls become part
of an orchestrated pipeline, enabling fluid delegation
across agents [205]. This structured interaction reduces
ambiguity in task handoff and fosters clearer behavioral
boundaries, especially when integrated with validation
protocols or observation mechanisms [14], [18].
3) Agentic Loop: Reasoning, Action, Observation: AI
Agents often suffer from single-pass inference limita-
tions. The ReAct pattern introduces an iterative loop
where agents reason about tasks, act by calling tools
or APIs, and then observe results before continuing.
This feedback loop allows for more deliberate, context-
sensitive behaviors. For example, an agent may verify
retrieved data before drafting a summary, thereby re-
ducing hallucination and logical errors. In Agentic AI,
this pattern is critical for collaborative coherence. ReAct
enables agents to evaluate dependencies dynamically
reasoning over intermediate states, re-invoking tools
if needed, and adjusting decisions as the environment | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-21T00:48:59+00:00", "moddate": "2025-05-21T00:48:59+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "trapped": "/False", "source": "data\\raw\\ai_agents_vs_agentic_ai_2505.10468.pdf", "total_pages": 33, "page": 24, "page_label": "25", "loader_type": "pdf", "load_timestamp": "2025-05-21T10:22:32.134937"} |
evolves. This loop becomes more complex in multi-
agent settings where each agent’s observation must be
reconciled against others’ outputs. Shared memory and
consistent logging are essential here, ensuring that the
reflective capacity of the system is not fragmented across
agents [132].
4) Memory Architectures (Episodic, Semantic, Vector):
AI Agents face limitations in long-horizon planning and
session continuity. Memory architectures address this by
persisting information across tasks [206]. Episodic mem-
ory allows agents to recall prior actions and feedback,
semantic memory encodes structured domain knowl-
edge, and vector memory enables similarity-based re-
trieval [207]. These elements are key for personalization
and adaptive decision-making in repeated interactions.
Agentic AI systems require even more sophisticated
memory models due to distributed state management.
Each agent may maintain local memory while accessing
shared global memory to facilitate coordination. For ex-
ample, a planner agent might use vector-based memory
to recall prior workflows, while a QA agent references
semantic memory for fact verification. Synchronizing
memory access and updates across agents enhances
consistency, enables context-aware communication, and
supports long-horizon system-level planning.
5) Multi-Agent Orchestration with Role Specialization:
In AI Agents, task complexity is often handled via mod-
ular prompt templates or conditional logic. However,
as task diversity increases, a single agent may become
overloaded [208], [209]. Role specialization splitting
tasks into subcomponents (e.g., planner, summarizer) al-
lows lightweight orchestration even within single-agent
systems by simulating compartmentalized reasoning. In
Agentic AI, orchestration is central. A meta-agent or
orchestrator distributes tasks among specialized agents,
each with distinct capabilities. Systems like MetaGPT
and ChatDev exemplify this: agents emulate roles such
as CEO, engineer, or reviewer, and interact through
structured messaging. This modular approach enhances
interpretability, scalability, and fault isolation ensuring
that failures in one agent do not cascade without con-
tainment mechanisms from the orchestrator.
6) Reflexive and Self-Critique Mechanisms: AI Agents
often fail silently or propagate errors. Reflexive mech-
anisms introduce the capacity for self-evaluation [210],
[211]. After completing a task, agents can critique their
own outputs using a secondary reasoning pass, increas-
ing robustness and reducing error rates. For example,
a legal assistant agent might verify that its drafted
clause matches prior case laws before submission. For
Agentic AI, reflexivity extends beyond self-critique to
inter-agent evaluation. Agents can review each other’s
outputs e.g., a verifier agent auditing a summarizer’s
work. Reflexion-like mechanisms ensure collaborative
quality control and enhance trustworthiness [212]. Such
patterns also support iterative improvement and adaptive
replanning, particularly when integrated with memory
logs or feedback queues [213], [214].
7) Programmatic Prompt Engineering Pipelines: Man-
ual prompt tuning introduces brittleness and reduces
reproducibility in AI Agents. Programmatic pipelines
automate this process using task templates, context
fillers, and retrieval-augmented variables [215], [216].
These dynamic prompts are structured based on task
type, agent role, or user query, improving generalization
and reducing failure modes associated with prompt
variability. In Agentic AI, prompt pipelines enable scal-
able, role-consistent communication. Each agent type
(e.g., planner, retriever, summarizer) can generate or
consume structured prompts tailored to its function. By
automating message formatting, dependency tracking,
and semantic alignment, programmatic prompting pre-
vents coordination drift and ensures consistent reasoning
across diverse agents in real time [14], [167].
8) Causal Modeling and Simulation-Based Planning: AI
Agents often operate on statistical correlations rather
than causal models, leading to poor generalization under
distribution shifts. Embedding causal inference allows
agents to distinguish between correlation and causation,
simulate interventions, and plan more robustly. For
instance, in supply chain scenarios, a causally aware
agent can simulate the downstream impact of shipment
delays. In Agentic AI, causal reasoning is vital for safe
coordination and error recovery. Agents must anticipate
how their actions impact others requiring causal graphs,
simulation environments, or Bayesian inference layers.
For example, a planning agent may simulate different
strategies and communicate likely outcomes to others,
fostering strategic alignment and avoiding unintended
emergent behaviors. To enforce cooperative behavior,
agents can be governed by a structured planning ap-
proach such as STRIPS or PDDL (Planning Domain
Definition Language), where the environment is modeled
with defined actions, preconditions, and effects. Inter-
agent dependencies are encoded such that one agent’s
action enables another’s, and a centralized or distributed
planner ensures that all agents contribute to a shared
goal. This unified framework supports strategic align-
ment, anticipatory planning, and minimizes unintended
emergent behaviors in multi-agent systems.
9) Monitoring, Auditing, and Explainability Pipelines:
AI Agents lack transparency, complicating debugging
and trust. Logging systems that record prompts, tool
calls, memory updates, and outputs enable post-hoc
analysis and performance tuning. These records help
developers trace faults, refine behavior, and ensure
compliance with usage guidelines especially critical in
enterprise or legal domains. For Agentic AI, logging and
explainability are exponentially more important. With
multiple agents interacting asynchronously, audit trails
are essential for identifying which agent caused an error
and under what conditions. Explainability pipelines that | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-21T00:48:59+00:00", "moddate": "2025-05-21T00:48:59+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "trapped": "/False", "source": "data\\raw\\ai_agents_vs_agentic_ai_2505.10468.pdf", "total_pages": 33, "page": 25, "page_label": "26", "loader_type": "pdf", "load_timestamp": "2025-05-21T10:22:32.134938"} |
AI Agents
Proactive
Intelligence
Tool
Integration
Causal
Reasoning
Continuous
Learning
Trust &
Safety
Agentic AI
Multi-Agent
Scaling
Unified Or-
chestration
Persistent
Memory
Simulation
Planning
Ethical
Governance
Domain-
Specific
Systems
Fig. 14: Mindmap visualization of the future roadmap for AI Agents and Agentic AI.
integrate across agents (e.g., timeline visualizations or
dialogue replays) are key to ensuring safety, especially
in regulatory or multi-stakeholder environments.
10) Governance-Aware Architectures (Accountability
and Role Isolation): AI Agents currently lack built-
in safeguards for ethical compliance or error attribution.
Governance-aware designs introduce role-based access
control, sandboxing, and identity resolution to ensure
agents act within scope and their decisions can be
audited or revoked. These structures reduce risks in
sensitive applications such as healthcare or finance.
In Agentic AI, governance must scale across roles,
agents, and workflows. Role isolation prevents rogue
agents from exceeding authority, while accountability
mechanisms assign responsibility for decisions and trace
causality across agents. Compliance protocols, ethical
alignment checks, and agent authentication ensure safety
in collaborative settings paving the way for trustworthy
AI ecosystems.
AI Agents are projected to evolve significantly through
enhanced modular intelligence focused on five key domains as
depicted in Figure 14 as : proactive reasoning, tool integration,
causal inference, continual learning, and trust-centric opera-
tions. The first transformative milestone involves transitioning
from reactive to Proactive Intelligence, where agents initiate
tasks based on learned patterns, contextual cues, or latent
goals rather than awaiting explicit prompts. This advancement
depends heavily on robust Tool Integration, enabling agents to
dynamically interact with external systems, such as databases,
APIs, or simulation environments, to fulfill complex user
tasks. Equally critical is the development of Causal Reasoning,
which will allow agents to move beyond statistical correlation,
supporting inference of cause-effect relationships essential
for tasks involving diagnosis, planning, or prediction. To
maintain relevance over time, agents must adopt frameworks
for Continuous Learning , incorporating feedback loops and
episodic memory to adapt their behavior across sessions and
environments. Lastly, to build user confidence, agents must
prioritize Trust & Safety mechanisms through verifiable out-
put logging, bias detection, and ethical guardrails especially
as their autonomy increases. Together, these pathways will
redefine AI Agents from static tools into adaptive cognitive
systems capable of autonomous yet controllable operation in
dynamic digital environments.
Agentic AI, as a natural extension of these foundations,
emphasizes collaborative intelligence through multi-agent co-
ordination, contextual persistence, and domain-specific orches-
tration. Future systems (Figure 14 right side) will exhibit
Multi-Agent Scaling , enabling specialized agents to work in
parallel under distributed control for complex problem-solving
mirroring team-based human workflows. This necessitates a
layer of Unified Orchestration, where meta-agents or orches-
trators dynamically assign roles, monitor task dependencies,
and mediate conflicts among subordinate agents. Sustained
performance over time depends on Persistent Memory archi-
tectures, which preserve semantic, episodic, and shared knowl-
edge for agents to coordinate longitudinal tasks and retain
state awareness. Simulation Planning is expected to become
a core feature, allowing agent collectives to test hypotheti-
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before real-world execution. Moreover, Ethical Governance
frameworks will be essential to ensure responsible deployment
defining accountability, oversight, and value alignment across
autonomous agent networks. Finally, tailored Domain-Specific
Systems will emerge in fields like law, medicine, and sup-
ply chains, leveraging contextual specialization to outperform
generic agents. This future positions Agentic AI not merely
as a coordination layer on the top of AI Agents, but as a new
paradigm for collective machine intelligence with adaptive
planning, recursive reasoning, and collaborative cognition at
its core.
A transformative direction for future AI systems is intro-
duced by the Absolute Zero: Reinforced Self-play Reasoning
with Zero Data (AZR) framework, which reimagines the learn-
ing paradigm for AI Agents and Agentic AI by removing
dependency on external datasets [217]. Traditionally, both AI
Agents and Agentic AI architectures have relied on human-
annotated data, static knowledge corpora, or preconfigured
environments factors that constrain scalability and adaptability
in open-world contexts. AZR addresses this limitation by
enabling agents to autonomously generate, validate, and solve
their own tasks, using verifiable feedback mechanisms (e.g.,
code execution) to ground learning. This self-evolving mech-
anism opens the door to truly autonomous reasoning agents
capable of self-directed learning and adaptation in dynamic,
data-scarce environments.
In the context of Agentic AI—where multiple specialized
agents collaborate within orchestrated workflows AZR lays
the groundwork for agents to not only specialize but also co-
evolve. For instance, scientific research pipelines could consist
of agents that propose hypotheses, run simulations, validate
findings, and revise strategies—entirely through self-play and
verifiable reasoning, without continuous human oversight. By
integrating the AZR paradigm, such systems can maintain
persistent growth, knowledge refinement, and task flexibility
across time. Ultimately, AZR highlights a future in which AI
agents transition from static, pretrained tools to intelligent,
self-improving ecosystems—positioning both AI Agents and
Agentic AI at the forefront of next-generation artificial intel-
ligence.
VII. C ONCLUSION
In this study, we presented a comprehensive literature-based
evaluation of the evolving landscape of AI Agents and Agentic
AI, offering a structured taxonomy that highlights foundational
concepts, architectural evolution, application domains, and key
limitations. Beginning with a foundational understanding, we
characterized AI Agents as modular, task-specific entities with
constrained autonomy and reactivity. Their operational scope
is grounded in the integration of LLMs and LIMs, which
serve as core reasoning modules for perception, language
understanding, and decision-making. We identified generative
AI as a functional precursor, emphasizing its limitations in
autonomy and goal persistence, and examined how LLMs
drive the progression from passive generation to interactive
task completion through tool augmentation.
This study then explored the conceptual emergence of
Agentic AI systems as a transformative evolution from isolated
agents to orchestrated, multi-agent ecosystems. We analyzed
key differentiators such as distributed cognition, persistent
memory, and coordinated planning that distinguish Agentic
AI from conventional agent models. This was followed by
a detailed breakdown of architectural evolution, highlight-
ing the transition from monolithic, rule-based frameworks to
modular, role specialized networks facilitated by orchestra-
tion layers and reflective memory architectures. Additionally,
this study then surveyed application domains in which these
paradigms are deployed. For AI Agents, we illustrated their
role in automating customer support, internal enterprise search,
email prioritization, and scheduling. For Agentic AI, we
demonstrated use cases in collaborative research, robotics,
medical decision support, and adaptive workflow automation,
supported by practical examples and industry-grade systems.
Finally, this study provided a deep analysis of the challenges
and limitations affecting both paradigms. For AI Agents,
we discussed hallucinations, shallow reasoning, and planning
constraints, while for Agentic AI, we addressed amplified
causality issues, coordination bottlenecks, emergent behavior,
and governance concerns. These insights offer a roadmap for
future development and deployment of trustworthy, scalable
agentic systems.
ACKNOWLEDGEMENT
This work was supported by the National Science Founda-
tion and the United States Department of Agriculture, National
Institute of Food and Agriculture through the “Artificial Intel-
ligence (AI) Institute for Agriculture” Program under Award
AWD003473, and AWD004595, Accession Number 1029004,
”Robotic Blossom Thinning with Soft Manipulators”. The
publication of the article in OA mode was financially sup-
ported by HEAL-Link.
DECLARATIONS
The authors declare no conflicts of interest.
STATEMENT ON AI W RITING ASSISTANCE
ChatGPT and Perplexity were utilized to enhance grammat-
ical accuracy and refine sentence structure; all AI-generated
revisions were thoroughly reviewed and edited for relevance.
Additionally, ChatGPT-4o was employed to generate realistic
visualizations.
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in Data Studio
Dataset Card for RAGAS Golden Dataset Documents
A small, mixed‐format corpus to compare PDF, API, and web‐based document loader output from the LangChain ecosystem.
The code to run the Prefect prefect_docloader_pipeline.py pipeline is available in the RAGAS Golden Dataset Pipeline repository.
While several enhancements are planned for future iterations, the hands-on insights gained from this grassroots exploration of document loader behaviors proved too valuable -- things that become less obvious with additional complexity.
The "aha" moment
I was shocked to discover how dramatically the choice of extraction method affects both content quality and metadata richness. These differences directly impact retrieval accuracy in ways I never anticipated until seeing the evidence side-by-side.
Dataset Description
This dataset contains 56 examples drawn from three ArXiv preprints (IDs: 2505.10468, 2505.06913, 2505.06817) in May 2025, each loaded via:
- PDF pages (50 records — one per page)
- ArXiv metadata summaries (3 records)
- Web HTML dumps (3 records)
Each item includes:
page_content— the extracted text (page or summary)metadata_json— a JSON‐string of only non‐empty metadata fields
Why this dataset?
It lets you deep-dive on how different loader backends (PDF vs. arXiv API vs. web scrape) affect text extraction and metadata completeness in RAG ingestion pipelines.
Provenance
Source: arXiv.org preprints (May 2025)
Loaders:
PyPDFDirectoryLoaderfor PDFArxivLoader.get_summaries_as_docs()for APIWebBaseLoaderfor HTML
Processing:
- Tag each document with its loader type (
pdf/arxiv/webbase). - Prune out empty metadata fields.
- Serialize remaining metadata to a single JSON string.
- Bundle into a Hugging Face
Datasetviadatasets.Dataset.from_list().
- Tag each document with its loader type (
Dataset Structure
| Field | Type | Description |
|---|---|---|
page_content |
string | Full text extracted from the page/summary. |
metadata_json |
string | JSON‐encoded flat dict of non‐empty metadata. |
All examples live in the train split (56 total).
Intended Uses
- Benchmarking: Compare loader accuracy and completeness.
- Pipeline Prototyping: Rapidly test ingestion logic in RAG systems.
- Filtering Experiments: Use loader‐specific metadata to explore retrieval strategies.
Out-of-Scope
- Large-scale LLM training (too small).
- Tasks requiring human annotations or sensitive data.
Limitations & Risks
- Domain bias: Only three computer-science preprints, same month.
- Scale: Toy-scale corpus... unsuitable for production training.
Citation
@misc{branson2025ragas_gold_docs,
title = {RAGAS Golden Dataset Documents},
author = {Branson, Don},
year = {2025},
howpublished = {\\url{https://huggingface.co/datasets/dwb2023/ragas-golden-dataset-documents}},
}
Maintainers & Contact
- Maintainer: Don Branson dwb2023
- Repository: https://github.com/donbr/ragas-golden-dataset-pipeline
- Hub Page: https://huggingface.co/datasets/dwb2023/ragas-golden-dataset-documents
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