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Agentic AI in 6G Software Businesses: A Layered Maturity Model

T0 review · 2 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read The paper maps 29 motivators and 27 demotivators into five enabling and five inhibiting themes for agentic-AI adoption in 6G software businesses.

desk verdict Honest early-stage taxonomy of agentic AI adoption factors in 6G software businesses, but the headline counts aren't auditable and the maturity model is still just a promise. read the letter →

arxiv 2508.03393 v1 pith:IPYLNMFB submitted 2025-08-05 cs.SE cs.AI

Muhammad Zohaib , Muhammad Azeem Akbar , Sami Hyrynsalmi , Arif Ali Khan This is my paper
classification cs.SEcs.AI
keywords agenticAI6GsoftwarebusinessesmaturitymodelmotivatorsdemotivatorsthematicanalysisCMMIorganizationalreadiness
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper aims to give 6G software businesses a first structured map of what pushes them toward and pulls them away from adopting agentic AI systems—software made of self-directed agents that perceive, reason, and act with minimal human oversight. Drawing on an earlier 133-source multivocal literature review (covering peer-reviewed and grey literature) plus a targeted scan of 29 selected documents, the authors identify 29 motivators and 27 demotivators and group them into five high-level themes in each direction. The motivator themes are scalable autonomy, cost efficiency, adaptive intelligence, alignment with 6G architecture, and innovation/differentiation; the demotivator themes are technical immaturity, trust and accountability, integration complexity, organizational readiness, and cost/performance overheads. The paper positions this taxonomy as a feasibility assessment and an early phase of a broader effort to build a layered maturity model, grounded in CMMI (a software process-maturity framework), that would let organizations rate and advance their agent-first capabilities across Data, Business Logic, and Presentation layers.

What carries the argument

The load-bearing device is the five-theme taxonomy of motivators (M1–M5: Scalable Autonomy, Cost Efficiency, Adaptive Intelligence, Alignment with 6G Architecture, Innovation & Differentiation) and demotivators (D1–D5: Technical Immaturity, Trust and Accountability, Integration Complexity, Organizational Readiness, Cost and Performance Overheads). It is produced by Braun and Clarke's six-phase thematic analysis, a standard qualitative coding protocol, applied to 29 selected studies, with the earlier 133-source multivocal review supplying recurrent challenges. This taxonomy is what lets the authors argue that agentic adoption in 6G is not one decision but a layered socio-technical transition, and it becomes the input to the proposed AAISEMM maturity model, which would place the factors onto the software architectural dimensions Data, Business Logic, and Presentation and align them with CMMI-style maturity levels.

What would settle it

Run the same thematic coding on a fresh independent sample of post-2022 academic and industry documents about 6G agentic software; if the five-motivator and five-demotivator structure does not re-emerge, the taxonomy reflects the original 29 documents rather than the underlying adoption landscape. A practitioner survey that asks respondents to rate each of the 29 motivators and 27 demotivators for prevalence and impact would also test whether the themes are the ones that actually shape decisions.

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Extended reading notes

Core claim

The central discovery is a provisional thematic map of adoption forces: after reviewing the selected literature, the authors coded 29 motivators and 27 demotivators into sub-themes and then synthesized them into five motivator themes and five demotivator themes. They find that agentic software attracts 6G businesses mainly because it promises scalable autonomy, cost efficiency, adaptive intelligence, architectural alignment with 6G, and strategic innovation, while it is resisted by technical immaturity, trust and accountability gaps, integration complexity, organizational unreadiness, and cost/performance overhead. The paper argues that no existing maturity framework, including CMMI, covers reasoning-driven autonomy and multi-agent collaboration, so it proposes the Agentic AI Software Engineering Maturity Model (AAISEMM) as a future CMMI-grounded framework that would assess agentic capability across the Data, Business Logic, and Presentation layers. The current study is exploratory and conceptual by design and explicitly does not claim empirical generalization.

Load-bearing premise

The taxonomy generalizes only if the 29 targeted documents, together with the earlier 133-source review, are representative enough of 6G software businesses that the five motivator and five demotivator themes are real adoption patterns rather than artifacts of the author-defined search and screening choices.

Editorial extensions

If this is right

  • Businesses can treat the five motivator and five demotivator themes as a first diagnostic checklist for agentic-AI readiness before committing to 6G-oriented transformation projects.
  • The demotivator set points to where engineering and governance work must come first: runtime and API maturity, explainability, legacy integration, workforce upskilling, and inference-cost control.
  • The planned AAISEMM model would assess agent-first capabilities separately along the Data, Business Logic, and Presentation layers, so maturity becomes layered rather than one aggregate score.
  • The paper's planning-phase framing means the taxonomy is a starting point, not a finished standard; later expert interviews, surveys, and case studies are the intended route to validation.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A likely extension the paper leaves implicit is that the same five-plus-five taxonomy could serve as a generic agentic-adoption readiness checklist outside 6G, with the 6G context mainly sharpening latency, edge, and heterogeneity requirements.
  • The demotivators are plausibly the binding constraints in the near term: technical immaturity and inference cost are concrete engineering limits, while trust and organizational readiness require slower cultural and governance change; the paper does not weight the themes.
  • A testable refinement would be to score each individual motivator and demotivator for prevalence and impact across the Data, Business Logic, and Presentation layers, converting the qualitative map into a quantitative readiness instrument.
  • The three-layer maturity model implies that agentic capability will develop unevenly across layers within one organization, so a firm could be mature in business-logic automation while still weak in data-layer governance—a prediction the model's validation studies could check.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 5 minor

Summary. This paper reports a preliminary thematic mapping of motivators and demotivators for adopting agentic AI software in 6G software businesses. The authors conducted a targeted Google Scholar search (April–June 2025), selected 29 studies from 46 initial documents using three inclusion criteria, and applied Braun and Clarke's six-phase thematic analysis. They report 29 motivators and 27 demotivators, further categorized into five high-level themes in each group. The paper also positions this work as the planning phase of a broader initiative to develop an Agentic AI Software Engineering Maturity Model (AAISEMM) structured around the Data, Business Logic, and Presentation layers. The study is explicitly exploratory, and the authors state that results should not be interpreted as exhaustive or empirically generalized.

Significance. If the thematic map is trustworthy, this is a useful early structuring of a nascent area at the intersection of agentic AI and 6G software engineering. The paper has several strengths: it is transparent about its preliminary nature, it provides a Zenodo link to the selected studies, it follows a recognized qualitative analysis protocol (Braun and Clarke), and it grounds the work in a prior multivocal literature review, though that citation is mismapped. The main value lies as a feasibility assessment and a starting point for the proposed maturity model. The credibility of the central 29/27 counts and the five-theme structure is the key to that value, and the current manuscript does not yet make those counts auditable.

major comments (2)
  1. [Section III A and B; Section II] The abstract's headline claim of '29 motivators and 27 demotivators' is not supported by any code-level evidence in the manuscript. Section II states that 29 studies were selected for detailed analysis; Section III A then says the motivator analysis was 'based on 29 initial codes.' The manuscript nowhere explains why the number of initial codes equals the number of selected studies. If each document contributed exactly one code, then the '29 motivators' are not independently derived codes and the five-theme structure is a re-labeling of study-level observations rather than a thematic synthesis. For the 27 demotivators, Section III B does not even state the number of initial demotivator codes. No codebook, code-to-study mapping table, or audit trail is included in the preprint; the selected studies are relegated to a Zenodo link. Because these counts are the central deliverable, the paper must include a codebook with definitions and a full coding table linking each study to its initial codes, sub-themes, and final themes, or state explicitly whether each code corresponds to one document and justify that design choice.
  2. [Section II] The methodological chain from search to synthesis cannot be audited as written. The sentence '29 studies were selected for detailed analysis [11]' cites reference [11] (Kitchenham et al.'s systematic-review guidelines) rather than any screening record; the actual list is deferred to an external Zenodo link. The authors also state that the work is grounded in 'our earlier multivocal literature review (MLR), which examined 133 sources ... [16]', but reference [16] is B. Anuraj (2023) on agent-based orchestration on swarm edge devices, not the claimed MLR. This citation mismatch makes it impossible to verify the 133-source foundation. No inter-rater reliability check is reported. Please correct the reference and include a complete screening record — search string, database query, inclusion/exclusion decisions, and the full list of 46 initially identified and 29 selected studies — either in the paper or as a complete appendix.
minor comments (5)
  1. [Section III, opening paragraph] The phrase 'motivators and depositors' should read 'motivators and demotivators.'
  2. [Abstract] There is a stray space in '2 7 demotivators'; it should read '27 demotivators.'
  3. [Section I] The sentence 'The obtain the objectives of this research our goal is to develop...' is ungrammatical; it should begin 'To obtain the objectives of this research, our goal is to develop...'
  4. [Fig. 2 and Fig. 3] The figure captions have inconsistent capitalization and spacing; for example, 'Agentic Ai In 6g Software:Motivators' should be 'Agentic AI in 6G Software: Motivators.'
  5. [Section IV] The paper announces an Agentic AI Software Engineering Maturity Model (AAISEMM) but does not describe any of its structure, such as maturity levels, capability dimensions, or assessment instruments. If this is a feasibility study, the title and contribution (3) should be adjusted to avoid claiming a developed model, or a draft structure should be included.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the thematic map is a literature-synthesis output, not a prediction fitted to its own inputs.

full rationale

This paper makes no first-principles derivation and no numerical prediction; its reported 29 motivators and 27 demotivators are the result of a qualitative thematic analysis (inductive and deductive coding following Braun and Clarke) of 29 selected studies plus an earlier multivocal literature review. The five motivator and five demotivator themes are the output of that coding, not parameters fitted to a subset and then used to predict the same subset. The apparent numerical equality between "29 initial codes" (Sec. III-A) and "29 studies were selected" (Sec. II) is not asserted by the paper to be a one-code-per-study mapping; without the linked codebook this is an auditability concern, not a demonstrated reduction of the result to its inputs. The sentence "grounded in our earlier multivocal literature review (MLR)... [16]" mispoints to an unrelated reference and should be corrected, but a mismapped citation is a reporting flaw, not load-bearing self-citation. The maturity model is explicitly positioned as future work, so the paper does not claim to have derived or predicted the model from the taxonomy.

Assumptions & free parameters 0 free parameters · 4 assumptions · 1 invented entities

The central output rests on no fitted parameters. It rests on domain assumptions about 6G's direction, on a claimed gap in existing maturity frameworks, on an arbitrary three-layer decomposition, and on the representativeness of a small selected corpus. The model AAISEMM is an invented, unvalidated artifact at this stage.

assumptions (4)
  • domain assumption Agentic software will be the core enabler of 6G use cases such as autonomous industry, smart cities, adaptive eHealth, and edge computing.
    Section I states this as the motivating premise; the entire taxonomy presupposes that agentic systems are central to 6G, which is not established by data in this paper.
  • domain assumption Existing process maturity frameworks such as CMMI lack constructs for reasoning-driven autonomy, multi-agent collaboration, and runtime adaptation.
    Section I asserts this gap but provides no systematic comparison of CMMI or AI maturity models; the proposed model's justification depends on this gap claim.
  • ad hoc to paper The Data, Business Logic, and Presentation three-layer architecture is the right decomposition for agentic software maturity.
    Section IV introduces these layers as the foundation of AAISEMM without derivation or empirical support; the future model's structure is tied to this arbitrary choice.
  • domain assumption The 29 selected documents are representative of the literature on agentic AI adoption in 6G software businesses.
    Section II describes a Google Scholar search with author-defined inclusion criteria and no inter-rater reliability check; all themes rest on this representativeness assumption.
invented entities (1)
  • AAISEMM (Agentic AI Software Engineering Maturity Model)
    purpose: The envisioned layered maturity model for assessing agentic AI capabilities in 6G software businesses; only named and sketched, not specified or validated.
    Section IV introduces AAISEMM as a future artifact. No maturity levels, assessment dimensions, or validation data are provided, so no falsifiable handle exists outside this paper.

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Cite this review

Pith. "Pith review of Agentic AI in 6G Software Businesses: A Layered Maturity Model." pith.science (2026). https://pith.science/paper/IPYLNMFB

@misc{pith2026250803393,
  author       = {Pith},
  title        = {Pith review of: Agentic AI in 6G Software Businesses: A Layered Maturity Model},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IPYLNMFB}},
  note         = {Machine review of arXiv:2508.03393}
}
read the original abstract

The emergence of agentic AI systems in 6G software businesses presents both strategic opportunities and significant challenges. While such systems promise increased autonomy, scalability, and intelligent decision-making across distributed environments, their adoption raises concerns regarding technical immaturity, integration complexity, organizational readiness, and performance-cost trade-offs. In this study, we conducted a preliminary thematic mapping to identify factors influencing the adoption of agentic software within the context of 6G. Drawing on a multivocal literature review and targeted scanning, we identified 29 motivators and 27 demotivators, which were further categorized into five high-level themes in each group. This thematic mapping offers a structured overview of the enabling and inhibiting forces shaping organizational readiness for agentic transformation. Positioned as a feasibility assessment, the study represents an early phase of a broader research initiative aimed at developing and validating a layered maturity model grounded in CMMI model with the software architectural three dimensions possibly Data, Business Logic, and Presentation. Ultimately, this work seeks to provide a practical framework to help software-driven organizations assess, structure, and advance their agent-first capabilities in alignment with the demands of 6G.

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Reference graph

Works this paper leans on

40 extracted references · 33 canonical work pages · cited by 1 Pith paper

  1. [11]

    Systematic literature reviews in software engineering –a systematic literature review,

    B. Kitchenham, O. P. Brereton, D. Budgen, M. Turner, J. Bailey, and S. Linkman, "Systematic literature reviews in software engineering –a systematic literature review," Information and software technology, vol. 51, no. 1, pp. 7-15, 2009

    work page 2009

  2. [16]

    Agent -based orchestration on a swarm of edge devices,

    B. Anuraj, "Agent -based orchestration on a swarm of edge devices," in Proceedings of the 17th ACM international conference on distributed and event-based systems, 2023, pp. 199-202

    work page 2023

  3. [1]

    6G wireless communications networks: A comprehensive survey,

    M. Alsabah et al., "6G wireless communications networks: A comprehensive survey," Ieee Access, vol. 9, pp. 148191-148243, 2021

    work page 2021

  4. [2]

    The road towards 6G: A comprehensive survey,

    W. Jiang, B. Han, M. A. Habibi, and H. D. Schotten, "The road towards 6G: A comprehensive survey," IEEE Open Journal of the Communications Society, vol. 2, pp. 334-366, 2021

    work page 2021

  5. [3]

    A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,

    W. Saad, M. Bennis, and M. Chen, "A vision of 6G wireless systems: Applications, trends, technologies, and open research problems," IEEE network, vol. 34, no. 3, pp. 134-142, 2019

    work page 2019

  6. [4]

    Internet of agents: Fundamentals, applications, and challenges,

    Y. Wang et al., "Internet of agents: Fundamentals, applications, and challenges," arXiv preprint arXiv:2505.07176, 2025

    arXiv 2025

  7. [5]

    Optimizing DevOps methodologies with the integration of artificial intelligence,

    M. S. Ali and D. Puri, "Optimizing DevOps methodologies with the integration of artificial intelligence," in 2024 3rd International Conference for Innovation in Technology (INOCON), 2024: IEEE, pp. 1-5

    work page 2024

  8. [6]

    A framework for model -based design of agent- oriented software,

    H. Xu and S. M. Shatz, "A framework for model -based design of agent- oriented software," IEEE Transactions on software engineering, vol. 29, no. 1, pp. 15-30, 2003

    work page 2003

Show all 40 references
  1. [7]

    An architecture for decentralized, collaborative, and autonomous robots,

    S. Garcıa, C. Menghi, P. Pelliccione, T. Berger, and R. Wohlrab, "An architecture for decentralized, collaborative, and autonomous robots," in 2018 IEEE International Conference on Software Architecture (ICSA), 2018: IEEE, pp. 75-7509

    2018

  2. [8]

    A survey on model context protocol: Architecture, state -of- the-art, challenges and future directions,

    P. P. Ray, "A survey on model context protocol: Architecture, state -of- the-art, challenges and future directions," Authorea Preprints, 2025

    2025

  3. [9]

    Using Dapr with the Azure Logic Apps Runtime,

    R. Gatev, "Using Dapr with the Azure Logic Apps Runtime," in Introducing Distributed Application Runtime (Dapr) Simplifying Microservices Applications Development Through Proven and Reusable Patterns and Practices: Springer, 2021, pp. 291-296

    2021

  4. [10]

    The rise of agentic AI: implications, concerns, and the path forward,

    S. Murugesan, "The rise of agentic AI: implications, concerns, and the path forward," IEEE Intelligent Systems, vol. 40, no. 2, pp. 8-14, 2025

    2025

  5. [12]

    Guidelines for including grey literature and conducting multivocal literature reviews in software engineering,

    V. Garousi, M. Felderer, and M. V. Mäntylä, "Guidelines for including grey literature and conducting multivocal literature reviews in software engineering," Information and software technology, vol. 106, pp. 101 - 121, 2019

    2019

  6. [13]

    Using thematic analysis in psychology,

    V. Braun and V. Clarke, "Using thematic analysis in psychology," Qualitative research in psychology, vol. 3, no. 2, pp. 77-101, 2006

    2006

  7. [14]

    DTTA -Distributed, time -division multiple access based task allocation framework for swarm robots,

    M. V. Shenoy and K. Anupama, "DTTA -Distributed, time -division multiple access based task allocation framework for swarm robots," Defence Science Journal, vol. 67, no. 3, p. 316, 2017

    2017

  8. [15]

    Agentic ai: Autonomous intelligence for complex goals –a comprehensive survey,

    D. B. Acharya, K. Kuppan, and B. Divya, "Agentic ai: Autonomous intelligence for complex goals –a comprehensive survey," IEEe Access, 2025

    2025

  9. [17]

    A survey on resource scheduling approaches in multi -access edge computing environment: a deep reinforcement learning study,

    A. A. Ismail, N. E. Khalifa, and R. A. El-Khoribi, "A survey on resource scheduling approaches in multi -access edge computing environment: a deep reinforcement learning study," Cluster Computing, vol. 28, no. 3, p. 184, 2025

    2025

  10. [18]

    Wireless Resource Optimization for UAV Swarm Cooperative Sensing via Multi -agent Multi-task Deep Reinforcement Learning,

    M. Li, A. Dong, G. Wang, X. Tian, J. Yu, and F. Li, "Wireless Resource Optimization for UAV Swarm Cooperative Sensing via Multi -agent Multi-task Deep Reinforcement Learning," in International Conference on Wireless Artificial Intelligent Computing Systems and Applications, 20...

    2025

  11. [19]

    The Future is Agentic: Definitions, Perspectives, and Open Challenges of Multi -Agent Recommender Systems,

    R. Y. Maragheh and Y. Deldjoo, "The Future is Agentic: Definitions, Perspectives, and Open Challenges of Multi -Agent Recommender Systems," arXiv preprint arXiv:2507.02097, 2025

    2025 arXiv

  12. [20]

    Approaches to fault-tolerant and transactional mobile agent execution ---an algorithmic view,

    S. Pleisch and A. Schiper, "Approaches to fault-tolerant and transactional mobile agent execution ---an algorithmic view," ACM Computing Surveys (CSUR), vol. 36, no. 3, pp. 219-262, 2004

    2004

  13. [21]

    6G ecosystem: Current status and future perspective,

    J. R. Bhat and S. A. Alqahtani, "6G ecosystem: Current status and future perspective," Ieee Access, vol. 9, pp. 43134-43167, 2021

    2021

  14. [22]

    Self -Learning and Adaptive Networking Protocols and Algorithms for 6G Edge Nodes,

    A. Haldorai, M. Upadhyaya, G. J. Nehru, and D. Kapila, "Self -Learning and Adaptive Networking Protocols and Algorithms for 6G Edge Nodes," in 2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT), 2023: IEEE, pp. 1-10

    2023

  15. [23]

    Se-do: Navigating the 6g frontier with scalable and efficient devops for intelligent agents optimization,

    P. M. Tshakwanda, H. Kumar, S. T. Arzo, and M. Devetsikiotis, "Se-do: Navigating the 6g frontier with scalable and efficient devops for intelligent agents optimization," in 2024 IEEE 14th Annual Computing and Communication Workshop and Conference (CCWC), 2024: IEEE, pp. 0269-0277

    2024

  16. [24]

    Large Language Models in the 6G -Enabled Computing Continuum: a White Paper,

    M. Abel et al., "Large Language Models in the 6G -Enabled Computing Continuum: a White Paper," University of Oulu, 2025

    2025

  17. [25]

    Large -scale AI in telecom: Charting the roadmap for innovation, scalability, and enhanced digital experiences,

    A. Shahid et al., "Large -scale AI in telecom: Charting the roadmap for innovation, scalability, and enhanced digital experiences," arXiv preprint arXiv:2503.04184, 2025

    2025 arXiv

  18. [26]

    Self -evolving and transformative protocol architecture for 6g,

    L. Cai, J. Pan, W. Yang, X. Ren, and X. Shen, "Self -evolving and transformative protocol architecture for 6g," IEEE Wireless Communications, vol. 30, no. 4, pp. 178-186, 2022

    2022

  19. [27]

    Ai agents vs. agentic ai: A conceptual taxonomy, applications and challenges,

    R. Sapkota, K. I. Roumeliotis, and M. Karkee, "Ai agents vs. agentic ai: A conceptual taxonomy, applications and challenges," arXiv preprint arXiv:2505.10468, 2025

    2025

  20. [28]

    Enabling mobile AI agent in 6G era: Architecture and key technologies,

    Z. Chen, Q. Sun, N. Li, X. Li, and Y. Wang, "Enabling mobile AI agent in 6G era: Architecture and key technologies," IEEE Network, vol. 38, no. 5, pp. 66-75, 2024

    2024

  21. [29]

    The integral role of intelligent IoT system, cloud computing, artificial intelligence, and 5G in the user -level self-monitoring of COVID-19,

    S. Ahmed, J. Yong, and A. Shrestha, "The integral role of intelligent IoT system, cloud computing, artificial intelligence, and 5G in the user -level self-monitoring of COVID-19," Electronics, vol. 12, no. 8, p. 1912, 2023

    1912

  22. [30]

    Large language models in 6G security: challenges and opportunities,

    T. Nguyen, H. Nguyen, A. Ijaz, S. Sheikhi, A. V. Vasilakos, and P. Kostakos, "Large language models in 6G security: challenges and opportunities," arXiv preprint arXiv:2403.12239, 2024

    2024 arXiv

  23. [31]

    Next-Generation Emergency Operations: Advancing Crisis Response with Drones, Augmented Reality, and Autonomous Vehicles,

    S. Majumdar and B. B. Mallik, "Next-Generation Emergency Operations: Advancing Crisis Response with Drones, Augmented Reality, and Autonomous Vehicles," in 2025 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA), 2025 : IEEE, pp. 162-166

    2025

  24. [32]

    Hyper -Reliable Communications for Industrial Automation: From IIoT Devices to Integrated Networks to Edge Clouds,

    M. A. Abuibaid, "Hyper -Reliable Communications for Industrial Automation: From IIoT Devices to Integrated Networks to Edge Clouds," Carleton University, 2024

    2024

  25. [33]

    Wireless large AI model: Shaping the AI -native future of 6G and beyond,

    F. Zhu et al., "Wireless large AI model: Shaping the AI -native future of 6G and beyond," arXiv preprint arXiv:2504.14653, 2025

    2025 arXiv

  26. [34]

    Self -Adaptive Wireless Communication: Leveraging ML And Agentic AI In Smart Telecommunication Networks,

    G. K. Sheelam and V. B. Komaragiri, "Self -Adaptive Wireless Communication: Leveraging ML And Agentic AI In Smart Telecommunication Networks," Metallurgical and Materials Engineering, pp. 1381-1401, 2025

    2025

  27. [35]

    A novel approach for scalable and sustainable 6G networks,

    L. Blanco, E. Zeydan, S. Barrachina -Muñoz, F. Rezazadeh, L. Vettori, and J. Mangues-Bafalluy, "A novel approach for scalable and sustainable 6G networks," IEEE Open Journal of the Communications Society, vol. 5, pp. 1673-1692, 2024

    2024

  28. [36]

    Review of Autonomous and Collaborative Agentic AI and Multi-Agent Systems for Enterprise Applications,

    S. Joshi, "Review of Autonomous and Collaborative Agentic AI and Multi-Agent Systems for Enterprise Applications," 2025

    2025

  29. [37]

    The role of agentic ai in shaping a smart future: A systematic review,

    S. Hosseini and H. Seilani, "The role of agentic ai in shaping a smart future: A systematic review," Array, p. 100399, 2025

    2025

  30. [38]

    Artificial intelligence -based development strategy in dependent market economies –any room amidst big power rivalry?,

    A. Szalavetz, "Artificial intelligence -based development strategy in dependent market economies –any room amidst big power rivalry?," Central European Business Review, vol. 8, no. 4, pp. 40-54, 2019

    2019

  31. [39]

    Llms for explainable ai: A comprehensive survey,

    A. Bilal, D. Ebert, and B. Lin, "Llms for explainable ai: A comprehensive survey," arXiv preprint arXiv:2504.00125, 2025

    2025 arXiv

  32. [40]

    White paper on business of 6G,

    S. Yrjola et al., "White paper on business of 6G," arXiv preprint arXiv:2005.06400, 2020

    2005 arXiv

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Reviewed August 6, 2026 · model on record in the stance chip above.