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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... | {"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_agent... |
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... | {"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_agent... |
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 ... | {"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_agent... |
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: Methodolog... | {"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_agent... |
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 suppo... | {"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_agent... |
[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... | {"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_agent... |
• 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 divers... | {"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_agent... |
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 analy... | {"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_agent... |
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 engagi... | {"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_agent... |
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... | {"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_agent... |
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 tri... | {"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_agent... |
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 Funct... | {"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_agent... |
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 t... | {"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_agent... |
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 Special... | {"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_agent... |
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
Mul... | {"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_agent... |
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 benefit... | {"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_agent... |
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... | {"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_agent... |
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 literatu... | {"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_agent... |
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 application... | {"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_agent... |
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 pa... | {"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_agent... |
(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
an... | {"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_agent... |
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 cor... | {"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_agent... |
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... | {"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_agent... |
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 ... | {"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_agent... |
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 I... | {"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_agent... |
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 (Episodi... | {"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_agent... |
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... | {"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_agent... |
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, lever... | {"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_agent... |
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arXiv:2505.06817v1 [cs.AI] 11 May 2025
Control Plane as a Tool: A Scalable Design Pattern for
Agentic AI Systems
Sivasathivel Kandasamy
sivasathivel@yahoo.com
May 13, 2025
Abstract
Agentic AI systems represent a new frontier in artificial intelligence, where agents—often
based on large language models (LLMs)—interact... | {"producer": "pikepdf 8.15.1", "creator": "arXiv GenPDF (tex2pdf:f38b2be)", "author": "Sivasathivel Kandasamy", "doi": "https://doi.org/10.48550/arXiv.2505.06817", "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea... |
• Autonomous Decision-Making: Dynamic task planning and real-time behavioral adapta-
tion.
• Multi-Tool Integration: Composition across APIs, search interfaces, and databases.
• Contextual Reasoning: Use of memory and history for iterative improvement.
• Composable Workflows: Encapsulation of agents as modular, role-or... | {"producer": "pikepdf 8.15.1", "creator": "arXiv GenPDF (tex2pdf:f38b2be)", "author": "Sivasathivel Kandasamy", "doi": "https://doi.org/10.48550/arXiv.2505.06817", "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea... |
• Governance and Observability: Ensuring traceability and enforcement of tool usage poli-
cies [15, 11, 3].
• Memory Synchronization: Maintaining consistent state across workflows [6, 10].
• Cross-Agent Coordination: Preventing task collisions and misaligned objectives [15, 17].
• Adaptability vs. Safety: Controlling e... | {"producer": "pikepdf 8.15.1", "creator": "arXiv GenPDF (tex2pdf:f38b2be)", "author": "Sivasathivel Kandasamy", "doi": "https://doi.org/10.48550/arXiv.2505.06817", "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea... |
(a) Agents-Tool Separation Through Control Plane
(b) Agents as Tool Through Control Plane
Figure 1: Figures show how control plane help with the interaction of agents and tools
• Governance and Observability: Tool usage should be auditable, allowing the enforcement
of organizational or safety policies.
• Cross-Framewo... | {"producer": "pikepdf 8.15.1", "creator": "arXiv GenPDF (tex2pdf:f38b2be)", "author": "Sivasathivel Kandasamy", "doi": "https://doi.org/10.48550/arXiv.2505.06817", "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea... |
Figure 2: Control Plane Architecture
5 | {"producer": "pikepdf 8.15.1", "creator": "arXiv GenPDF (tex2pdf:f38b2be)", "author": "Sivasathivel Kandasamy", "doi": "https://doi.org/10.48550/arXiv.2505.06817", "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea... |
appropriate modules viz Registration Module, Invocation Module and Feedback Integration
Module. The main goal of the Registration Module is to register the interacting agents, tools,
validation rules and metrics.
The Invocation Module, module helps the invoking agents to query a tool or other regis-
tered agents. Input... | {"producer": "pikepdf 8.15.1", "creator": "arXiv GenPDF (tex2pdf:f38b2be)", "author": "Sivasathivel Kandasamy", "doi": "https://doi.org/10.48550/arXiv.2505.06817", "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea... |
Table 1: Similarities Between Control Plane and MCP
Feature Description
Tool Registration Both systems require structured metadata or schema reg-
istration for external tools. MCP uses JSON schema; the
Control Plane maintains a Tool Registry.
Input Validation Both validate tool inputs using schema constraints. MCP
enfo... | {"producer": "pikepdf 8.15.1", "creator": "arXiv GenPDF (tex2pdf:f38b2be)", "author": "Sivasathivel Kandasamy", "doi": "https://doi.org/10.48550/arXiv.2505.06817", "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea... |
Table 2: Key Differences Between Control Plane and MCP
Aspect Control Plane (This Work) Model Context Protocol (MCP)
Architecture Type External modular orchestrator Embedded schema-based interface
Routing Strategy Rule-based and similarity-based rout-
ing via Routing Handler
Implicit function selection via schema-
matc... | {"producer": "pikepdf 8.15.1", "creator": "arXiv GenPDF (tex2pdf:f38b2be)", "author": "Sivasathivel Kandasamy", "doi": "https://doi.org/10.48550/arXiv.2505.06817", "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea... |
[15] Eric Wu and et al. Autogen: Enabling next-generation multi-agent llm applications. arXiv
preprint arXiv:2309.12307, 2023.
[16] Muhan Xu and et al. Hierarchical planning with llms: A modular framework. arXiv preprint
arXiv:2311.09541, 2023.
[17] Shinn Yao and et al. React: Synergizing reasoning and acting in langua... | {"producer": "pikepdf 8.15.1", "creator": "arXiv GenPDF (tex2pdf:f38b2be)", "author": "Sivasathivel Kandasamy", "doi": "https://doi.org/10.48550/arXiv.2505.06817", "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea... |
RedTeamLLM: an Agentic AI framework for offensive security
Brian Challita1 , Pierre Parrend1,2 ,
1Laboratoire de Recherche de l’EPITA, 14-16 Rue V oltaire, 94270 Le Kremlin-Bicˆetre, France
2ICube, UMR 7357, Universit´e de Strasbourg, CNRS, 300 bd S´ebastien Brant - CS 10413 - F-67412
Illkirch Cedex
{brian.challita, pi... | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-13T00:52:17+00:00", "moddate": "2025-05-13T00:52:17+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "templateversion": "IJCAI.2025.0", "trapped": "/False", "sour... |
and likely impact of proliferation of agentic AI frameworks
are high. Understanding their mechanism to leverage these
tools for defensive operations, and for being able to antic-
ipate their malicious exploitation, is therefore an urgent re-
quirement for the community.
We therefore propose the RedTeamLLM model to the ... | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-13T00:52:17+00:00", "moddate": "2025-05-13T00:52:17+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "templateversion": "IJCAI.2025.0", "trapped": "/False", "sour... |
definition step, with a given subgoal. If the goal is achieved,
the pipeline terminates. The main limits of this architecture,
whether it is used with prompting or with complex pipelines,
is the absence of memory, which requires each prompt to em-
bed all context and knowledge about previous analysis steps.
Since the c... | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-13T00:52:17+00:00", "moddate": "2025-05-13T00:52:17+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "templateversion": "IJCAI.2025.0", "trapped": "/False", "sour... |
not useful unless the whole process is automated, not requir-
ing human interaction during the process. Thus, integrating a
tool call of an interactive terminal access within this context
is rudimentary.
Consolidated requirements for our penetration-testing
agent are thus:
1. Dynamic Plan Correction— Handling subtask o... | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-13T00:52:17+00:00", "moddate": "2025-05-13T00:52:17+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "templateversion": "IJCAI.2025.0", "trapped": "/False", "sour... |
Figure 3: Database schema for Memory management Model
agentic AI models: attack surface expansion, data manipula-
tion and prompt injection, API usage and sensitive data ex-
posure [Khan et al., 2024]. Its five key components, shown
in Figure 4 are: 1) a dedicated authentication, authorization
and session management mo... | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-13T00:52:17+00:00", "moddate": "2025-05-13T00:52:17+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "templateversion": "IJCAI.2025.0", "trapped": "/False", "sour... |
3. Summarizer The summarizer is a stateless LLM ses-
sion: for each request, it summarizes the given command’s
output. Because this session does not maintain context about
the agent’s overall goal, it sometimes omits important infor-
mation. We plan to address this limitation in future work.
5.2 Sample Run
A sample run... | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-13T00:52:17+00:00", "moddate": "2025-05-13T00:52:17+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "templateversion": "IJCAI.2025.0", "trapped": "/False", "sour... |
6.3 Reasoning: a strong optimization lever
The ablation study aims to evaluate the contribution of rea-
soning to the RedTeamLLM framework. Figure 7 shows the
number of tool calls without and with reasoning for the 5 use
cases. Every LLM session can have tool calls. A tool calls
is a specific API response from an LLM s... | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-13T00:52:17+00:00", "moddate": "2025-05-13T00:52:17+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "templateversion": "IJCAI.2025.0", "trapped": "/False", "sour... |
References
[Acharya et al., 2025] Deepak Bhaskar Acharya,
Karthigeyan Kuppan, and B Divya. Agentic ai: Au-
tonomous intelligence for complex goals–a comprehen-
sive survey. IEEE Access, 2025.
[Bi et al., 2024] Zhen Bi, Ningyu Zhang, Yinuo Jiang,
Shumin Deng, Guozhou Zheng, and Huajun Chen. When
do program-of-thought wo... | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-13T00:52:17+00:00", "moddate": "2025-05-13T00:52:17+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "templateversion": "IJCAI.2025.0", "trapped": "/False", "sour... |
in large language models: Techniques and applications.
arXiv preprint arXiv:2402.07927, 2024.
[Shavit et al., 2023] Yonadav Shavit, Sandhini Agarwal,
Miles Brundage, Steven Adler, Cullen O’Keefe, Rosie
Campbell, Teddy Lee, Pamela Mishkin, Tyna Eloundou,
Alan Hickey, et al. Practices for governing agentic ai sys-
tems. ... | {"producer": "pdfTeX-1.40.25", "creator": "LaTeX with hyperref", "creationdate": "2025-05-13T00:52:17+00:00", "moddate": "2025-05-13T00:52:17+00:00", "ptex.fullbanner": "This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5", "templateversion": "IJCAI.2025.0", "trapped": "/False", "sour... |
This study critically distinguishes between AI Agents and Agentic AI,
offering a structured conceptual taxonomy, application mapping, and challenge
analysis to clarify their divergent design philosophies and capabilities. We
begin by outlining the search strategy and foundational definitions,
characterizing AI Agents a... | {"Entry ID": "http://arxiv.org/abs/2505.10468v3", "Published": "2025-05-20", "Title": "AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges", "Authors": "Ranjan Sapkota, Konstantinos I. Roumeliotis, Manoj Karkee", "loader_type": "arxiv", "arxiv_id": "2505.10468", "load_timestamp": "2025-05-21T10... |
From automated intrusion testing to discovery of zero-day attacks before
software launch, agentic AI calls for great promises in security engineering.
This strong capability is bound with a similar threat: the security and
research community must build up its models before the approach is leveraged by
malicious actors ... | {"Entry ID": "http://arxiv.org/abs/2505.06913v1", "Published": "2025-05-11", "Title": "RedTeamLLM: an Agentic AI framework for offensive security", "Authors": "Brian Challita, Pierre Parrend", "loader_type": "arxiv", "arxiv_id": "2505.06913", "load_timestamp": "2025-05-21T10:22:32.514374", "doc_type": "summary"} |
Agentic AI systems represent a new frontier in artificial intelligence, where
agents often based on large language models(LLMs) interact with tools,
environments, and other agents to accomplish tasks with a degree of autonomy.
These systems show promise across a range of domains, but their architectural
underpinnings r... | {"Entry ID": "http://arxiv.org/abs/2505.06817v1", "Published": "2025-05-11", "Title": "Control Plane as a Tool: A Scalable Design Pattern for Agentic AI Systems", "Authors": "Sivasathivel Kandasamy", "loader_type": "arxiv", "arxiv_id": "2505.06817", "load_timestamp": "2025-05-21T10:22:33.018866", "doc_type": "summary"} |
AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges
I Introduction
I-A Methodology Overview
I-A1 Search Strategy
II Foundational Understanding of AI Agents
II-1 Overview of Core Characteristics of AI Agents
II-2 Foundational Models: The Role of LLMs and LIMs
II-3 G... | {"source": "https://arxiv.org/html/2505.10468v1", "title": "AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges", "language": "en", "loader_type": "webbase", "source_url": "https://arxiv.org/html/2505.10468v1", "load_timestamp": "2025-05-21T10:22:31.627898", "doc_type": "html"} |
RedTeamLLM: an Agentic AI framework for offensive security
1 Introduction
2 State of the Art
2.1 Research challenges for Agentic AI
2.2 Cognitive Architectures
2.3 Agentic AI and cybersecurity
3 Requirements
4 RedTeamLLM
4.1 The Architecture
4.2 Features
4.3 Memory management
4.4 The Security M... | {"source": "https://arxiv.org/html/2505.06913v1", "title": "RedTeamLLM: an Agentic AI framework for offensive security", "language": "en", "loader_type": "webbase", "source_url": "https://arxiv.org/html/2505.06913v1", "load_timestamp": "2025-05-21T10:22:31.908137", "doc_type": "html"} |
Control Plane as a Tool: A Scalable Design Pattern for Agentic AI Systems
1 Introduction
2 Proposed Design Pattern: Control Plane as a Tool
2.1 Design Goals
2.2 Pattern Structure
3 Comparison with Model Context Protocol
Disclaimer.
3.1 Similarities Between the Control Plane and MCP
3.2 Key Diff... | {"source": "https://arxiv.org/html/2505.06817v1", "title": "Control Plane as a Tool: A Scalable Design Pattern for Agentic AI Systems", "language": "en", "loader_type": "webbase", "source_url": "https://arxiv.org/html/2505.06817v1", "load_timestamp": "2025-05-21T10:22:32.275895", "doc_type": "html"} |
Subsets and Splits
Loader Type Metadata Fields
Reveals the number and variety of metadata fields for each loader type, providing insight into the different types of information available across the dataset.
Loader Type Analysis
Reveals the distribution of records by loader type, including the average page content length, which can help identify different types of data sources and their relative contributions.
Metadata JSON Analysis
The query provides a detailed analysis of the metadata_json field in the dataset, identifying the presence of key fields and estimating field counts, which helps in understanding the structure and content of the JSON data without parsing it.