The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index; none of the 70 sources its claims cite is checked for support yet.
Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions
Open accessAbstract
This paper presents a comprehensive review of Artificial General Intelligence (AGI) and Agentic AI, examining their technological foundations, current capabilities, and future trajectories. The study identifies key technical distinctions between these AI paradigms, including their architectural requirements, computational demands, and learning mechanisms. We survey the current technical landscape, including specialized frameworks like OpenAI’s AGI classification system and emerging Agentic AI platforms such as Vectara-agentic and CrewAI. The paper also examines the hardware infrastructure and cloud services enabling these advanced AI systems, from NVIDIA’s specialized GPUs to large-scale projects like OpenAI’s proposed "Stargate" initiative and others. Our comparative analysis reveals that Agentic AI is rapidly transitioning from research to practical deployment across industries including legal services, DevOps, and enterprise automation, while AGI remains in the research phase with ongoing debates about its feasibility and timeline. The paper discusses critical challenges in both domains, including safety considerations, alignment problems, and governance requirements. We highlight how Agentic AI serves as a bridge between today’s generative AI capabilities and future AGI aspirations, offering autonomous functionality while avoiding some of AGI’s unresolved risks.
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In the paper
“Our comparative analysis reveals that Agentic AI is rapidly transitioning from research to practical deployment across industries including legal services, DevOps, and enterprise automation, while AGI remains in the research phase with ongoing debates about its feasibility and timeline.”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > Abstract
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Deloitte surveys indicate growing enterprise interest in Agentic AI solutions [34] , with some experts viewing it as a more practical nearterm focus than AGI [35] .”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > C) Industry Adoption and Investment
What the sources say
- Not checked yetcites [34] 34. TechDogs. Understanding The Difference Between AGI And Generative AI. Available from: https://www.techdogs.com/td-articles/trending- stories/understanding-the-difference-between-agi-and- generative-ai 47. Nangare S. Replicating Human Attitudes and Behaviors through Generative Agent Simulation. Sagar Nangare. Feb 2025.
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In the paper
“Agentic AI toolkits are bridging the gap between narrow generative models and the broader ambitions of AGI by enabling autonomous, multistep workflows and seamless tool integration [2,4,24] .”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > K) Summary and Outlook
What the sources say
- Not checked yetcites [2] 2. Agentic AI Complete Guide to the Future of Autonomous Systems. May 2025.
- Not checked yetcites [4] 4. Synechron. Agentic AI: The New Frontier in Generative AI. Available from: https://www.synechron.com/en- us/insight/agentic-ai-new-frontier-generative-ai
- Not checked yetcites [24] 24. Hypersonix. The Commerce Industry is Quick to Adopt Digital Transformation and is Leaning Into AI. Feb 2025. Available from: https://hypersonix.ai/blogs/gpt- genai-agenticai-agi-what-retailers-need-to-know 30. Hyperight. AI Agents: A Catalyst for Transformation by 2025. Available from: https://hyperight.com/ai-agents-a- catalyst-for-transformation-by-2025/
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Energy Efficiency: New architectures emphasize power efficiency via mixture-of-experts (MoE) models and sparsity techniques [40] .”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions >
What the sources say
- Not checked yetcites [40] 40. Marr B. Beyond ChatGPT: The 5 Toughest Challenges On The Path To AGI. Available from: https://bernardmarr.com/beyond-chatgpt-the-5- toughest-challenges-on-the-path-to-agi/
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Third, the technical requirements for these advanced AI systems are becoming increasingly demanding, from specialized hardware architectures [39] to novel cloud services supporting autonomous agent deployment [18] .”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > 8. Conclusion While AGI remains an ambitious and potentially distant goal [7] , Agentic AI represents a practical stepping stone that is already delivering value across industries [9,56] .The coming years will likely see continued progress in both areas, with several key developments on the horizon: Refinement of AGI benchmarks and measurement frameworks [57] Expansion of Agentic AI applications across sectors [18] Increased focus on safety and governance mechanisms [38] Development of hybrid systems combining generative and agentic capabilities [46] As these technologies evolve, it will be crucial to maintain balanced perspectives that acknowledge both their potential and limitations.Future research should focus on developing robust evaluation methodologies, safety protocols, and practical implementation frameworks to ensure these powerful technologies deliver maximum benefit while minimizing risks.This comprehensive review has examined the current state of Artificial General Intelligence (AGI) and Agentic AI, analyzing their technical foundations, capabilities, and trajectories.Our investigation reveals several key insights about the evolving AI landscape: First, while AGI remains an aspirational goal with significant technical hurdles [7,8] , Agentic AI has emerged as a practical intermediate step that is already demonstrating real-world value [9,10] .The development of frameworks like OpenAI's 5-level AGI classification system [23] and specialized Agentic AI platforms [24] indicates growing sophistication in both domains. Second, the comparative analysis highlights fundamental differences between AI paradigms.Where traditional narrow AI excels at specific tasks and generative AI at content creation, Agentic AI introduces autonomous decisionmaking capabilities [2] , while AGI promises (but has not yet achieved) human-level generalization [3] .This spectrum of capabilities suggests a maturation path for AI systems, with Agentic AI serving as a crucial bridge between current technologies and future AGI aspirations [51] .Third, the technical requirements for these advanced AI systems are becoming increasingly demanding, from specialized hardware architectures [39] to novel cloud services supporting autonomous agent deployment [18] .These infrastructure developments both enable and constrain progress in the field.Looking ahead, three critical priorities emerge for researchers and practitioners: Development of Robust Evaluation Frameworks: As Agentic AI systems become more autonomous and AGI research advances, standardized metrics and testing environments [25] will be essential for measuring progress and ensuring reliability.Safety and Governance Mechanisms: The unique risks posed by autonomous systems [50] and potential AGI [38] demand continued investment in alignment research and ethical frameworks.Practical Implementation Strategies: Organizations should focus on incremental adoption of Agentic AI solutions [34] while maintaining realistic expectations about AGI timelines.The rapid evolution of these technologies suggests that while AGI may remain years or decades away, Agentic AI is poised for near-term expansion across industries [20,29] .By maintaining balanced perspectives that acknowledge both the potential and limitations of these paradigms, the AI community can steer development toward beneficial outcomes while mitigating risks.Future research should particularly focus on hybrid architectures that combine the strengths of different approaches [21] , as well as interdisciplinary efforts to address the societal implications of increasingly autonomous AI systems [28,43] Table: 1 Table 1 : Comparison of AI Paradigms
What the sources say
- Not checked yetcites [39] 39. Amin S. Agentic AI vs AGI. LinkedIn. Available from: https://www.linkedin.com/pulse/agentic-ai-vs- agi-whats-difference-swapnil-amin-5ctoc/
- Not checked yetcites [18] 18. Cozzi P. Hypothesis of episodic memory in AI: another step towards Artificial General Intelligence. Tech4Future. Jul 2024.
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Borderline: one of the tests for a substantial claim is unclear. 1
- It may state two separate claims as one. 25
- Probably not a substantial claim. 16
None of the sources cited for this claim have been checked yet.
In the paper
“Computational Demands: GenAI systems require massive inference resources, but Agentic AI adds ongoing computation for decision-making [10] .AGI would theoretically need unprecedented scale [39] .”
What the sources say
- Not checked yetcites [10] 10. Golabek P. Agentic AI Revolutionizing DevOps and Kubernetes Management. Kubert. Jan 2025.
- Not checked yetcites [39] 39. Amin S. Agentic AI vs AGI. LinkedIn. Available from: https://www.linkedin.com/pulse/agentic-ai-vs- agi-whats-difference-swapnil-amin-5ctoc/
direct- Asserted: passes — The authors put it forward as true.
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Borderline substantial claim. It is unclear whether the paper’s conclusions depend on it. It may state two separate claims as one.
In the paper
“Artificial General Intelligence (AGI): Hypothetical systems with human-level generalization across diverse domains [3,11] .AGI remains unrealized but is actively researched [6] .”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions >
What the sources say
- Not checked yetcites [6] 6. VKTR. AGI in 2025? How Enterprise Leaders Should Prepare. Available from: https://www.vktr.com/ai- disruption/agi-in-2025-how-enterprise-leaders-should- prepare/
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
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In the paper
“Narrow AI: Optimized for specific tasks like fraud detection or predictive maintenance [49] . GenAI: Ideal for content creation, code generation, and data augmentation [16] . Agentic AI: Suited for autonomous customer service, DevOps automation [10] , and legal document analysis [20] . AGI: Potential future applications in scientific discovery and complex problem-solving [43] .”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > D) Use Case Differentiation
What the sources say
- Not checked yetcites [49] 49. Joshi S. Generative AI: Mitigating Workforce and Economic Disruptions While Strategizing Policy Responses for Governments and Companies. SSRN. 2025 Feb 12. DOI:10.2139/ssrn.5135229
- Not checked yetcites [16] 16. UBTI. Generative AI vs. Agentic AI -Let's Compare & Understand. May 2025. 13. HTCinc. Unleashing the Power of Agentic AI: Advancing towards Autonomous Decision-Making. 14. Shkurdoda A. Agentic AI And Next-Gen Automation with Accenture and NVIDIA. Neontri. Oct 2024. 15. SAS Voices. AI agents are here, but how autonomous should they be? Mar 2025. 16. JD Supra Editorial Team. Looking beyond generative AI: agentic AI's potential in legal services. JD Supra. 2024. Available from: https://www.jdsupra.com/legalnews/looking- beyond-generative-ai-agentic-ai-4983437/
- Not checked yetcites [10] 10. Golabek P. Agentic AI Revolutionizing DevOps and Kubernetes Management. Kubert. Jan 2025.
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“The AI field continues to evolve rapidly, with Agentic AI emerging as a practical middle ground between today's GenAI and future AGI aspirations [9] .”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > F) Safety Considerations
What the sources say
- Not checked yetcites [9] 9. Outshift by Cisco. 5 Levels of agentic AI intelligence for enterprise use. Available from: https://outshift.com/blog/agentic-ai-intelligence- for-enterprise-use
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Agentic AI systems are demonstrating increasingly sophisticated capabilities in various domains: DevOps and Kubernetes management [10] Legal services and document analysis [28] Retail and commerce applications [29] Enterprise automation and workflow management [30]”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > B) Agentic AI Advancements
What the sources say
- Not checked yetcites [10] 10. Golabek P. Agentic AI Revolutionizing DevOps and Kubernetes Management. Kubert. Jan 2025.
- Not checked yetcites [28] 28. Marshall M. From AGI to ROI: The 6 AI debates shaping enterprise strategy in 2024. VentureBeat. Jul 2024.
- Not checked yetcites [29] 29. IT Pro. OpenAI says it's charting a "path to AGI" with its next frontier AI model. May 2024. Available from: https://www.itpro.com/technology/artificial- intelligence/openai-says-its-charting-a-path-to-agi-with- its-next-frontier-ai-model 37. USAII. ChatGPT's Operator Mode: A Significant Step Toward AGI in 2025. Available from: https://www.usaii.org/ai-insights/chatgpts- operator-mode-a-significant-step-toward-agi-in-2025
boosted- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Deloitte surveys indicate that 78% of tech leaders see Agentic AI as a key enabler of sustainable value [34] , while PwC analysis suggests AI is fundamentally rewriting competitive playbooks [41] .”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > 5. Applications and Implications A) Business and Industry Impact
What the sources say
- Not checked yetcites [41] 41. Jadhav B. What Are AI Agents? How They Work in 2025. Aisera: Best Agentic AI For Enterprise. Nov 2024.
hedgeddirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“while AGI remains in the research phase with ongoing debates about its feasibility and timeline.”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > Abstract
What the sources say
- Not checked yetcites [5] 5. Andre D. Sam Altman Predicts First AI Workers as OpenAI Nears AGI Breakthrough! All About AI. Jan 2025.
- Not checked yetcites [6] 6. VKTR. AGI in 2025? How Enterprise Leaders Should Prepare. Available from: https://www.vktr.com/ai- disruption/agi-in-2025-how-enterprise-leaders-should- prepare/
- Not checked yetcites [7] 7. The Register. AGI is on the radar but far from reality, says Gartner. 2024 Aug 22. Available from: https://www.theregister.com/2024/08/22/gartner_ agi_hype_cycle/
- Not checked yetcites [8] 8. InformationWeek. Artificial General Intelligence in 2025? Good Luck With That. Available from: https://www.informationweek.com/machine- learning-ai/artificial-general-intelligence-in-2025-good- luck-with-that
plain- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“OpenAI has proposed a 5-level framework for measuring progress toward AGI, with current systems estimated to be at Level 1 [23,26] .”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > 3. Recent Technological Developments A) AGI Progress and Benchmarks
What the sources say
- Not checked yetcites [23] 23. National Law Review. Agentic Artificial Intelligence and the New Legal Playbook. Available from: https://natlawreview.com/article/thinking-lawyer- agentic-ai-and-new-legal-playbook
- Not checked yetcites [26] 26. BankInfoSecurity. OpenAI to Retain Nonprofit Oversight Amid For-Profit Shift. 2025 May 5. Available from: https://www.bankinfosecurity.com/openai-to- retain-nonprofit-oversight-amid-for-profit-shift-a-28290
hedged- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Major technology companies are investing heavily in both AGI research and Agentic AI applications.”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > C) Industry Adoption and Investment
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Modern AI systems can be categorized into several distinct paradigms with varying capabilities: Traditional/Narrow AI: Task-specific systems designed for particular applications (e.g., recommendation engines, computer vision) [12] .These represent the majority of current deployed AI systems.”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > B) Societal Implications
What the sources say
- Not checked yetcites [12] 12. GSDC. General AI vs Specific AI: A Comprehensive Comparison of the Future of AI. Available from: https://www.gsdcouncil.org/blogs/general-ai-vs- specific-ai-a-comprehensive-comparison-of-the-future- of-ai
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Modern AI systems can be categorized into several distinct paradigms with varying capabilities: Traditional/Narrow AI: Task-specific systems designed for particular applications (e.g., recommendation engines, computer vision) [12] .These represent the majority of current deployed AI systems.”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > B) Societal Implications
What the sources say
- Not checked yetcites [12] 12. GSDC. General AI vs Specific AI: A Comprehensive Comparison of the Future of AI. Available from: https://www.gsdcouncil.org/blogs/general-ai-vs- specific-ai-a-comprehensive-comparison-of-the-future- of-ai
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We highlight how Agentic AI serves as a bridge between today's generative AI capabilities and future AGI aspirations, offering autonomous functionality while avoiding some of AGI's unresolved risks.”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“This comparative analysis reveals that while these paradigms share technological foundations, they represent fundamentally different approaches to artificial intelligence with distinct capabilities and applications [51,52] .”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > F) Safety Considerations
What the sources say
- Not checked yetcites [51] 51. Joshi S. Leveraging prompt engineering to enhance financial market integrity and risk management. World J Adv Res Rev. 2025;25(1):1775-85. DOI:10.30574/wjarr.2025.25.1.0279
- Not checked yetcites [52] 52. Joshi S. Retraining US Workforce in the Age of Agentic Gen AI: Role of Prompt Engineering and Up-Skilling Initiatives. Int J Adv Res Sci Commun Technol (IJARSCT). 2025;5(1).
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
In the paper
“Deloitte surveys indicate growing enterprise interest in Agentic AI solutions [34] , with some experts viewing it as a more practical nearterm focus than AGI [35] .”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > C) Industry Adoption and Investment
What the sources say
- Not checked yetcites [35] 35. Basheer KCS. March 2025 GenAI Launches: Are We on The Brink of AGI? Analytics Vidhya. Apr 2025.
plain- Asserted: fails — The authors may not put it forward as true.
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Probably not a substantial claim. The authors may not put it forward as true. The paper’s conclusions may not depend on it.
In the paper
“Deloitte surveys indicate that 78% of tech leaders see Agentic AI as a key enabler of sustainable value [34] , while PwC analysis suggests AI is fundamentally rewriting competitive playbooks [41] .”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > 5. Applications and Implications A) Business and Industry Impact
What the sources say
- Not checked yetcites [34] 34. TechDogs. Understanding The Difference Between AGI And Generative AI. Available from: https://www.techdogs.com/td-articles/trending- stories/understanding-the-difference-between-agi-and- generative-ai 47. Nangare S. Replicating Human Attitudes and Behaviors through Generative Agent Simulation. Sagar Nangare. Feb 2025.
hedged- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Data Requirements: While narrow AI and GenAI typically require large, domain-specific datasets [45] , AGI systems aim for efficient learning from diverse data [47] .Agentic AI adds reinforcement learning from environmental feedback [19] .”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > C) Technical Distinctions
What the sources say
- Not checked yetcites [45] 45. Exploring Artificial General Intelligence: The Future of AI Unveiled. Jul 2024.
- Not checked yetcites [47] 47. Joshi S. Advancing innovation in financial stability: A comprehensive review of ai agent frameworks, challenges and applications. World J Adv Eng Technol Sci. 2025;14(2):117-26. DOI:10.30574/wjaets.2025.14.2.0071
- Not checked yetcites [19] 19. AI Innovation. OpenAI Defines 5 Stages Between GenAI and AGI. Available from: https://ainnovation.substack.com/p/openai- defines-5-stages-between-genai 24. Vectara. Vectara-agentic Agentic RAG. Aug 2024. Available from: https://www.vectara.com/blog/introducing- vectara-agentic
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Architectural Complexity: GenAI primarily uses transformer architectures [48] , while Agentic AI incorporates planning modules and memory systems [22] .AGI research explores hybrid neuro-symbolic approaches [21].”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > C) Technical Distinctions
What the sources say
- Not checked yetcites [48] 48. Joshi S. Agentic Generative AI and the Future U.S. Workforce: Advancing Innovation and National Competitiveness. SSRN. 2025 Feb 3. DOI:10.2139/ssrn.5126922
- Not checked yetcites [22] 22. Heilweil R. NASA appears to step back from the term 'artificial general intelligence'. FedScoop. Jun 2024.
- Not checked yetcites [21] 21. Cook J. OpenAI's 5 Levels Of Super AI (AGI To Outperform Human Capability). Forbes. 2024 Jul 16. Available from: https://www.forbes.com/sites/jodiecook/2024/07/ 16/openais-5-levels-of-super-ai-agi-to-outperform- human-capability/
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Developing systems with true understanding and reasoning capabilities [53] Creating AI that can generalize across diverse domains [49] Implementing robust memory and learning mechanisms [47] Achieving human-level adaptability and creativity [48]”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > 7. Challenges and Limitations A) Technical Challenges The path to AGI faces several significant technical hurdles:
What the sources say
- Not checked yetcites [53] 53. Joshi S. Review of autonomous systems and collaborative AI agent frameworks. Int J Sci Res Arch. 2025;14(2):961-72. DOI:10.30574/ijsra.2025.14.2.0439
- Not checked yetcites [49] 49. Joshi S. Generative AI: Mitigating Workforce and Economic Disruptions While Strategizing Policy Responses for Governments and Companies. SSRN. 2025 Feb 12. DOI:10.2139/ssrn.5135229
- Not checked yetcites [47] 47. Joshi S. Advancing innovation in financial stability: A comprehensive review of ai agent frameworks, challenges and applications. World J Adv Eng Technol Sci. 2025;14(2):117-26. DOI:10.30574/wjaets.2025.14.2.0071
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Narrow AI: Bias in training data and overfitting JonesAGIHypeDamaging2024?. GenAI: Misinformation risks and IP concerns [28] . Agentic AI: Unintended consequences of autonomous actions [50] . AGI: Existential risks and alignment problems [38] .”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > F) Safety Considerations
What the sources say
- Not checked yetcites [28] 28. Marshall M. From AGI to ROI: The 6 AI debates shaping enterprise strategy in 2024. VentureBeat. Jul 2024.
- Not checked yetcites [50] 50. Joshi S. Implementing Gen AI for Increasing Robustness of US Financial and Regulatory System. IJIREM. 2025;11(6). DOI:10.55524/ijirem.2024.11.6.19
- Not checked yetcites [38] 38. Kancherla N. Agentic AI vs discriminative AI: confused? LinkedIn. Available from: https://www.linkedin.com/pulse/genai-agentic-ai- discriminative-confused-heres-nagaraju-kancherla- xltac/
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“This spectrum of capabilities suggests a maturation path for AI systems, with Agentic AI serving as a crucial bridge between current technologies and future AGI aspirations [51] .”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > 8. Conclusion While AGI remains an ambitious and potentially distant goal [7] , Agentic AI represents a practical stepping stone that is already delivering value across industries [9,56] .The coming years will likely see continued progress in both areas, with several key developments on the horizon: Refinement of AGI benchmarks and measurement frameworks [57] Expansion of Agentic AI applications across sectors [18] Increased focus on safety and governance mechanisms [38] Development of hybrid systems combining generative and agentic capabilities [46] As these technologies evolve, it will be crucial to maintain balanced perspectives that acknowledge both their potential and limitations.Future research should focus on developing robust evaluation methodologies, safety protocols, and practical implementation frameworks to ensure these powerful technologies deliver maximum benefit while minimizing risks.This comprehensive review has examined the current state of Artificial General Intelligence (AGI) and Agentic AI, analyzing their technical foundations, capabilities, and trajectories.Our investigation reveals several key insights about the evolving AI landscape: First, while AGI remains an aspirational goal with significant technical hurdles [7,8] , Agentic AI has emerged as a practical intermediate step that is already demonstrating real-world value [9,10] .The development of frameworks like OpenAI's 5-level AGI classification system [23] and specialized Agentic AI platforms [24] indicates growing sophistication in both domains. Second, the comparative analysis highlights fundamental differences between AI paradigms.Where traditional narrow AI excels at specific tasks and generative AI at content creation, Agentic AI introduces autonomous decisionmaking capabilities [2] , while AGI promises (but has not yet achieved) human-level generalization [3] .This spectrum of capabilities suggests a maturation path for AI systems, with Agentic AI serving as a crucial bridge between current technologies and future AGI aspirations [51] .Third, the technical requirements for these advanced AI systems are becoming increasingly demanding, from specialized hardware architectures [39] to novel cloud services supporting autonomous agent deployment [18] .These infrastructure developments both enable and constrain progress in the field.Looking ahead, three critical priorities emerge for researchers and practitioners: Development of Robust Evaluation Frameworks: As Agentic AI systems become more autonomous and AGI research advances, standardized metrics and testing environments [25] will be essential for measuring progress and ensuring reliability.Safety and Governance Mechanisms: The unique risks posed by autonomous systems [50] and potential AGI [38] demand continued investment in alignment research and ethical frameworks.Practical Implementation Strategies: Organizations should focus on incremental adoption of Agentic AI solutions [34] while maintaining realistic expectations about AGI timelines.The rapid evolution of these technologies suggests that while AGI may remain years or decades away, Agentic AI is poised for near-term expansion across industries [20,29] .By maintaining balanced perspectives that acknowledge both the potential and limitations of these paradigms, the AI community can steer development toward beneficial outcomes while mitigating risks.Future research should particularly focus on hybrid architectures that combine the strengths of different approaches [21] , as well as interdisciplinary efforts to address the societal implications of increasingly autonomous AI systems [28,43] Table: 1 Table 1 : Comparison of AI Paradigms
What the sources say
- Not checked yetcites [51] 51. Joshi S. Leveraging prompt engineering to enhance financial market integrity and risk management. World J Adv Res Rev. 2025;25(1):1775-85. DOI:10.30574/wjarr.2025.25.1.0279
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Our investigation reveals several key insights about the evolving AI landscape: First, while AGI remains an aspirational goal with significant technical hurdles [7,8] , Agentic AI has emerged as a practical intermediate step that is already demonstrating real-world value [9,10] .”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > 8. Conclusion While AGI remains an ambitious and potentially distant goal [7] , Agentic AI represents a practical stepping stone that is already delivering value across industries [9,56] .The coming years will likely see continued progress in both areas, with several key developments on the horizon: Refinement of AGI benchmarks and measurement frameworks [57] Expansion of Agentic AI applications across sectors [18] Increased focus on safety and governance mechanisms [38] Development of hybrid systems combining generative and agentic capabilities [46] As these technologies evolve, it will be crucial to maintain balanced perspectives that acknowledge both their potential and limitations.Future research should focus on developing robust evaluation methodologies, safety protocols, and practical implementation frameworks to ensure these powerful technologies deliver maximum benefit while minimizing risks.This comprehensive review has examined the current state of Artificial General Intelligence (AGI) and Agentic AI, analyzing their technical foundations, capabilities, and trajectories.Our investigation reveals several key insights about the evolving AI landscape: First, while AGI remains an aspirational goal with significant technical hurdles [7,8] , Agentic AI has emerged as a practical intermediate step that is already demonstrating real-world value [9,10] .The development of frameworks like OpenAI's 5-level AGI classification system [23] and specialized Agentic AI platforms [24] indicates growing sophistication in both domains. Second, the comparative analysis highlights fundamental differences between AI paradigms.Where traditional narrow AI excels at specific tasks and generative AI at content creation, Agentic AI introduces autonomous decisionmaking capabilities [2] , while AGI promises (but has not yet achieved) human-level generalization [3] .This spectrum of capabilities suggests a maturation path for AI systems, with Agentic AI serving as a crucial bridge between current technologies and future AGI aspirations [51] .Third, the technical requirements for these advanced AI systems are becoming increasingly demanding, from specialized hardware architectures [39] to novel cloud services supporting autonomous agent deployment [18] .These infrastructure developments both enable and constrain progress in the field.Looking ahead, three critical priorities emerge for researchers and practitioners: Development of Robust Evaluation Frameworks: As Agentic AI systems become more autonomous and AGI research advances, standardized metrics and testing environments [25] will be essential for measuring progress and ensuring reliability.Safety and Governance Mechanisms: The unique risks posed by autonomous systems [50] and potential AGI [38] demand continued investment in alignment research and ethical frameworks.Practical Implementation Strategies: Organizations should focus on incremental adoption of Agentic AI solutions [34] while maintaining realistic expectations about AGI timelines.The rapid evolution of these technologies suggests that while AGI may remain years or decades away, Agentic AI is poised for near-term expansion across industries [20,29] .By maintaining balanced perspectives that acknowledge both the potential and limitations of these paradigms, the AI community can steer development toward beneficial outcomes while mitigating risks.Future research should particularly focus on hybrid architectures that combine the strengths of different approaches [21] , as well as interdisciplinary efforts to address the societal implications of increasingly autonomous AI systems [28,43] Table: 1 Table 1 : Comparison of AI Paradigms
What the sources say
- Not checked yetcites [7] 7. The Register. AGI is on the radar but far from reality, says Gartner. 2024 Aug 22. Available from: https://www.theregister.com/2024/08/22/gartner_ agi_hype_cycle/
- Not checked yetcites [8] 8. InformationWeek. Artificial General Intelligence in 2025? Good Luck With That. Available from: https://www.informationweek.com/machine- learning-ai/artificial-general-intelligence-in-2025-good- luck-with-that
- Not checked yetcites [9] 9. Outshift by Cisco. 5 Levels of agentic AI intelligence for enterprise use. Available from: https://outshift.com/blog/agentic-ai-intelligence- for-enterprise-use
- Not checked yetcites [10] 10. Golabek P. Agentic AI Revolutionizing DevOps and Kubernetes Management. Kubert. Jan 2025.
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The coming years will likely see continued progress in both areas, with several key developments on the horizon: Refinement of AGI benchmarks and measurement frameworks [57] Expansion of Agentic AI applications across sectors [18] Increased focus on safety and governance mechanisms [38] Development of hybrid systems combining generative and agentic capabilities [46]”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > 8. Conclusion While AGI remains an ambitious and potentially distant goal [7] , Agentic AI represents a practical stepping stone that is already delivering value across industries [9,56] .The coming years will likely see continued progress in both areas, with several key developments on the horizon: Refinement of AGI benchmarks and measurement frameworks [57] Expansion of Agentic AI applications across sectors [18] Increased focus on safety and governance mechanisms [38] Development of hybrid systems combining generative and agentic capabilities [46] As these technologies evolve, it will be crucial to maintain balanced perspectives that acknowledge both their potential and limitations.Future research should focus on developing robust evaluation methodologies, safety protocols, and practical implementation frameworks to ensure these powerful technologies deliver maximum benefit while minimizing risks.This comprehensive review has examined the current state of Artificial General Intelligence (AGI) and Agentic AI, analyzing their technical foundations, capabilities, and trajectories.Our investigation reveals several key insights about the evolving AI landscape: First, while AGI remains an aspirational goal with significant technical hurdles [7,8] , Agentic AI has emerged as a practical intermediate step that is already demonstrating real-world value [9,10] .The development of frameworks like OpenAI's 5-level AGI classification system [23] and specialized Agentic AI platforms [24] indicates growing sophistication in both domains. Second, the comparative analysis highlights fundamental differences between AI paradigms.Where traditional narrow AI excels at specific tasks and generative AI at content creation, Agentic AI introduces autonomous decisionmaking capabilities [2] , while AGI promises (but has not yet achieved) human-level generalization [3] .This spectrum of capabilities suggests a maturation path for AI systems, with Agentic AI serving as a crucial bridge between current technologies and future AGI aspirations [51] .Third, the technical requirements for these advanced AI systems are becoming increasingly demanding, from specialized hardware architectures [39] to novel cloud services supporting autonomous agent deployment [18] .These infrastructure developments both enable and constrain progress in the field.Looking ahead, three critical priorities emerge for researchers and practitioners: Development of Robust Evaluation Frameworks: As Agentic AI systems become more autonomous and AGI research advances, standardized metrics and testing environments [25] will be essential for measuring progress and ensuring reliability.Safety and Governance Mechanisms: The unique risks posed by autonomous systems [50] and potential AGI [38] demand continued investment in alignment research and ethical frameworks.Practical Implementation Strategies: Organizations should focus on incremental adoption of Agentic AI solutions [34] while maintaining realistic expectations about AGI timelines.The rapid evolution of these technologies suggests that while AGI may remain years or decades away, Agentic AI is poised for near-term expansion across industries [20,29] .By maintaining balanced perspectives that acknowledge both the potential and limitations of these paradigms, the AI community can steer development toward beneficial outcomes while mitigating risks.Future research should particularly focus on hybrid architectures that combine the strengths of different approaches [21] , as well as interdisciplinary efforts to address the societal implications of increasingly autonomous AI systems [28,43] Table: 1 Table 1 : Comparison of AI Paradigms
What the sources say
- Not checked yetcites [57] 57. Joshi S. The Synergy of Generative AI and Big Data for Financial Risk: Review of Recent Developments. IJFMR. 2025;7(1). DOI:https://doi.org/g82gmx
- Not checked yetcites [18] 18. Cozzi P. Hypothesis of episodic memory in AI: another step towards Artificial General Intelligence. Tech4Future. Jul 2024.
- Not checked yetcites [38] 38. Kancherla N. Agentic AI vs discriminative AI: confused? LinkedIn. Available from: https://www.linkedin.com/pulse/genai-agentic-ai- discriminative-confused-heres-nagaraju-kancherla- xltac/
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The development of frameworks like OpenAI's 5-level AGI classification system [23] and specialized Agentic AI platforms [24] indicates growing sophistication in both domains.”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > 8. Conclusion While AGI remains an ambitious and potentially distant goal [7] , Agentic AI represents a practical stepping stone that is already delivering value across industries [9,56] .The coming years will likely see continued progress in both areas, with several key developments on the horizon: Refinement of AGI benchmarks and measurement frameworks [57] Expansion of Agentic AI applications across sectors [18] Increased focus on safety and governance mechanisms [38] Development of hybrid systems combining generative and agentic capabilities [46] As these technologies evolve, it will be crucial to maintain balanced perspectives that acknowledge both their potential and limitations.Future research should focus on developing robust evaluation methodologies, safety protocols, and practical implementation frameworks to ensure these powerful technologies deliver maximum benefit while minimizing risks.This comprehensive review has examined the current state of Artificial General Intelligence (AGI) and Agentic AI, analyzing their technical foundations, capabilities, and trajectories.Our investigation reveals several key insights about the evolving AI landscape: First, while AGI remains an aspirational goal with significant technical hurdles [7,8] , Agentic AI has emerged as a practical intermediate step that is already demonstrating real-world value [9,10] .The development of frameworks like OpenAI's 5-level AGI classification system [23] and specialized Agentic AI platforms [24] indicates growing sophistication in both domains. Second, the comparative analysis highlights fundamental differences between AI paradigms.Where traditional narrow AI excels at specific tasks and generative AI at content creation, Agentic AI introduces autonomous decisionmaking capabilities [2] , while AGI promises (but has not yet achieved) human-level generalization [3] .This spectrum of capabilities suggests a maturation path for AI systems, with Agentic AI serving as a crucial bridge between current technologies and future AGI aspirations [51] .Third, the technical requirements for these advanced AI systems are becoming increasingly demanding, from specialized hardware architectures [39] to novel cloud services supporting autonomous agent deployment [18] .These infrastructure developments both enable and constrain progress in the field.Looking ahead, three critical priorities emerge for researchers and practitioners: Development of Robust Evaluation Frameworks: As Agentic AI systems become more autonomous and AGI research advances, standardized metrics and testing environments [25] will be essential for measuring progress and ensuring reliability.Safety and Governance Mechanisms: The unique risks posed by autonomous systems [50] and potential AGI [38] demand continued investment in alignment research and ethical frameworks.Practical Implementation Strategies: Organizations should focus on incremental adoption of Agentic AI solutions [34] while maintaining realistic expectations about AGI timelines.The rapid evolution of these technologies suggests that while AGI may remain years or decades away, Agentic AI is poised for near-term expansion across industries [20,29] .By maintaining balanced perspectives that acknowledge both the potential and limitations of these paradigms, the AI community can steer development toward beneficial outcomes while mitigating risks.Future research should particularly focus on hybrid architectures that combine the strengths of different approaches [21] , as well as interdisciplinary efforts to address the societal implications of increasingly autonomous AI systems [28,43] Table: 1 Table 1 : Comparison of AI Paradigms
What the sources say
- Not checked yetcites [23] 23. National Law Review. Agentic Artificial Intelligence and the New Legal Playbook. Available from: https://natlawreview.com/article/thinking-lawyer- agentic-ai-and-new-legal-playbook
- Not checked yetcites [24] 24. Hypersonix. The Commerce Industry is Quick to Adopt Digital Transformation and is Leaning Into AI. Feb 2025. Available from: https://hypersonix.ai/blogs/gpt- genai-agenticai-agi-what-retailers-need-to-know 30. Hyperight. AI Agents: A Catalyst for Transformation by 2025. Available from: https://hyperight.com/ai-agents-a- catalyst-for-transformation-by-2025/
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“While AGI remains an ambitious and potentially distant goal [7] , Agentic AI represents a practical stepping stone that is already delivering value across industries [9,56] .”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > 8. Conclusion While AGI remains an ambitious and potentially distant goal [7] , Agentic AI represents a practical stepping stone that is already delivering value across industries [9,56] .The coming years will likely see continued progress in both areas, with several key developments on the horizon: Refinement of AGI benchmarks and measurement frameworks [57] Expansion of Agentic AI applications across sectors [18] Increased focus on safety and governance mechanisms [38] Development of hybrid systems combining generative and agentic capabilities [46] As these technologies evolve, it will be crucial to maintain balanced perspectives that acknowledge both their potential and limitations.Future research should focus on developing robust evaluation methodologies, safety protocols, and practical implementation frameworks to ensure these powerful technologies deliver maximum benefit while minimizing risks.This comprehensive review has examined the current state of Artificial General Intelligence (AGI) and Agentic AI, analyzing their technical foundations, capabilities, and trajectories.Our investigation reveals several key insights about the evolving AI landscape: First, while AGI remains an aspirational goal with significant technical hurdles [7,8] , Agentic AI has emerged as a practical intermediate step that is already demonstrating real-world value [9,10] .The development of frameworks like OpenAI's 5-level AGI classification system [23] and specialized Agentic AI platforms [24] indicates growing sophistication in both domains. Second, the comparative analysis highlights fundamental differences between AI paradigms.Where traditional narrow AI excels at specific tasks and generative AI at content creation, Agentic AI introduces autonomous decisionmaking capabilities [2] , while AGI promises (but has not yet achieved) human-level generalization [3] .This spectrum of capabilities suggests a maturation path for AI systems, with Agentic AI serving as a crucial bridge between current technologies and future AGI aspirations [51] .Third, the technical requirements for these advanced AI systems are becoming increasingly demanding, from specialized hardware architectures [39] to novel cloud services supporting autonomous agent deployment [18] .These infrastructure developments both enable and constrain progress in the field.Looking ahead, three critical priorities emerge for researchers and practitioners: Development of Robust Evaluation Frameworks: As Agentic AI systems become more autonomous and AGI research advances, standardized metrics and testing environments [25] will be essential for measuring progress and ensuring reliability.Safety and Governance Mechanisms: The unique risks posed by autonomous systems [50] and potential AGI [38] demand continued investment in alignment research and ethical frameworks.Practical Implementation Strategies: Organizations should focus on incremental adoption of Agentic AI solutions [34] while maintaining realistic expectations about AGI timelines.The rapid evolution of these technologies suggests that while AGI may remain years or decades away, Agentic AI is poised for near-term expansion across industries [20,29] .By maintaining balanced perspectives that acknowledge both the potential and limitations of these paradigms, the AI community can steer development toward beneficial outcomes while mitigating risks.Future research should particularly focus on hybrid architectures that combine the strengths of different approaches [21] , as well as interdisciplinary efforts to address the societal implications of increasingly autonomous AI systems [28,43] Table: 1 Table 1 : Comparison of AI Paradigms
What the sources say
- Not checked yetcites [7] 7. The Register. AGI is on the radar but far from reality, says Gartner. 2024 Aug 22. Available from: https://www.theregister.com/2024/08/22/gartner_ agi_hype_cycle/
- Not checked yetcites [9] 9. Outshift by Cisco. 5 Levels of agentic AI intelligence for enterprise use. Available from: https://outshift.com/blog/agentic-ai-intelligence- for-enterprise-use
- Not checked yetcites [56] 56. Joshi S. Review of Data Pipelines and Streaming for Generative AI Integration: Challenges, Solutions, and Future Directions. Int J Res Publ Rev. 2025;6(2):2348- 57.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Measuring progress in AGI and Agentic AI requires robust benchmarking tools: OpenAI's AGI Levels: A five-level classification system for tracking AGI progress [23] .”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > E) Benchmarking and Evaluation Tools
What the sources say
- Not checked yetcites [23] 23. National Law Review. Agentic Artificial Intelligence and the New Legal Playbook. Available from: https://natlawreview.com/article/thinking-lawyer- agentic-ai-and-new-legal-playbook
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Ensuring reliable autonomous operation [19] Maintaining appropriate human oversight [50] Managing complex multi-agent interactions [54] Balancing autonomy with safety constraints [38]”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > 7. Challenges and Limitations A) Technical Challenges The path to AGI faces several significant technical hurdles:
What the sources say
- Not checked yetcites [19] 19. AI Innovation. OpenAI Defines 5 Stages Between GenAI and AGI. Available from: https://ainnovation.substack.com/p/openai- defines-5-stages-between-genai 24. Vectara. Vectara-agentic Agentic RAG. Aug 2024. Available from: https://www.vectara.com/blog/introducing- vectara-agentic
- Not checked yetcites [50] 50. Joshi S. Implementing Gen AI for Increasing Robustness of US Financial and Regulatory System. IJIREM. 2025;11(6). DOI:10.55524/ijirem.2024.11.6.19
- Not checked yetcites [54] 54. Joshi S. Review of Data Engineering and Data Lakes for Implementing GenAI in Financial Risk. SSRN. 2025 Jan 1. DOI:10.2139/ssrn.5123081
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These infrastructure developments both enable and constrain progress in the field.”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > 8. Conclusion While AGI remains an ambitious and potentially distant goal [7] , Agentic AI represents a practical stepping stone that is already delivering value across industries [9,56] .The coming years will likely see continued progress in both areas, with several key developments on the horizon: Refinement of AGI benchmarks and measurement frameworks [57] Expansion of Agentic AI applications across sectors [18] Increased focus on safety and governance mechanisms [38] Development of hybrid systems combining generative and agentic capabilities [46] As these technologies evolve, it will be crucial to maintain balanced perspectives that acknowledge both their potential and limitations.Future research should focus on developing robust evaluation methodologies, safety protocols, and practical implementation frameworks to ensure these powerful technologies deliver maximum benefit while minimizing risks.This comprehensive review has examined the current state of Artificial General Intelligence (AGI) and Agentic AI, analyzing their technical foundations, capabilities, and trajectories.Our investigation reveals several key insights about the evolving AI landscape: First, while AGI remains an aspirational goal with significant technical hurdles [7,8] , Agentic AI has emerged as a practical intermediate step that is already demonstrating real-world value [9,10] .The development of frameworks like OpenAI's 5-level AGI classification system [23] and specialized Agentic AI platforms [24] indicates growing sophistication in both domains. Second, the comparative analysis highlights fundamental differences between AI paradigms.Where traditional narrow AI excels at specific tasks and generative AI at content creation, Agentic AI introduces autonomous decisionmaking capabilities [2] , while AGI promises (but has not yet achieved) human-level generalization [3] .This spectrum of capabilities suggests a maturation path for AI systems, with Agentic AI serving as a crucial bridge between current technologies and future AGI aspirations [51] .Third, the technical requirements for these advanced AI systems are becoming increasingly demanding, from specialized hardware architectures [39] to novel cloud services supporting autonomous agent deployment [18] .These infrastructure developments both enable and constrain progress in the field.Looking ahead, three critical priorities emerge for researchers and practitioners: Development of Robust Evaluation Frameworks: As Agentic AI systems become more autonomous and AGI research advances, standardized metrics and testing environments [25] will be essential for measuring progress and ensuring reliability.Safety and Governance Mechanisms: The unique risks posed by autonomous systems [50] and potential AGI [38] demand continued investment in alignment research and ethical frameworks.Practical Implementation Strategies: Organizations should focus on incremental adoption of Agentic AI solutions [34] while maintaining realistic expectations about AGI timelines.The rapid evolution of these technologies suggests that while AGI may remain years or decades away, Agentic AI is poised for near-term expansion across industries [20,29] .By maintaining balanced perspectives that acknowledge both the potential and limitations of these paradigms, the AI community can steer development toward beneficial outcomes while mitigating risks.Future research should particularly focus on hybrid architectures that combine the strengths of different approaches [21] , as well as interdisciplinary efforts to address the societal implications of increasingly autonomous AI systems [28,43] Table: 1 Table 1 : Comparison of AI Paradigms
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Meanwhile, Agentic AI has gained traction as a practical approach to creating more autonomous and capable AI systems [9,10] .”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > 1. Introduction
What the sources say
- Not checked yetcites [9] 9. Outshift by Cisco. 5 Levels of agentic AI intelligence for enterprise use. Available from: https://outshift.com/blog/agentic-ai-intelligence- for-enterprise-use
- Not checked yetcites [10] 10. Golabek P. Agentic AI Revolutionizing DevOps and Kubernetes Management. Kubert. Jan 2025.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“OpenAI has outlined an ambitious path toward superintelligence while maintaining nonprofit oversight [32], [33] .”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > C) Industry Adoption and Investment
What the sources say
- Not checked yetcites [32] 32. PwC. AI rewrites the playbook: Is your business strategy keeping pace? Available from: https://www.pwc.com/us/en/tech-effect/ai- analytics/ai-business-strategy.html
- Not checked yetcites [33] 33. Inc42. How The Emergence Of AGI Is Redefining Startups. Available from: https://inc42.com/resources/how-the-emergence- of-agi-is-redefining-startups/ 43. Turing. 7 Reasons Why AGI Is Reshaping the Future of AI and Innovation. Available from: https://www.turing.com/blog/7-reasons-why-agi- is-reshaping-the-future 44. Cointelegraph. AI took giant strides in 2024, as AGI comes into view. Dec 2024. Available from: https://cointelegraph.com/news/ai-took-giant- strides-2024-agi-comes-into-view 45. Costa LD. GenAI: Not Magic Wand But Can Transform Your Business. Concentrix. Jun 2024.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Second, the comparative analysis highlights fundamental differences between AI paradigms.Where traditional narrow AI excels at specific tasks and generative AI at content creation, Agentic AI introduces autonomous decisionmaking capabilities [2] , while AGI promises (but has not yet achieved) human-level generalization [3] .”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > 8. Conclusion While AGI remains an ambitious and potentially distant goal [7] , Agentic AI represents a practical stepping stone that is already delivering value across industries [9,56] .The coming years will likely see continued progress in both areas, with several key developments on the horizon: Refinement of AGI benchmarks and measurement frameworks [57] Expansion of Agentic AI applications across sectors [18] Increased focus on safety and governance mechanisms [38] Development of hybrid systems combining generative and agentic capabilities [46] As these technologies evolve, it will be crucial to maintain balanced perspectives that acknowledge both their potential and limitations.Future research should focus on developing robust evaluation methodologies, safety protocols, and practical implementation frameworks to ensure these powerful technologies deliver maximum benefit while minimizing risks.This comprehensive review has examined the current state of Artificial General Intelligence (AGI) and Agentic AI, analyzing their technical foundations, capabilities, and trajectories.Our investigation reveals several key insights about the evolving AI landscape: First, while AGI remains an aspirational goal with significant technical hurdles [7,8] , Agentic AI has emerged as a practical intermediate step that is already demonstrating real-world value [9,10] .The development of frameworks like OpenAI's 5-level AGI classification system [23] and specialized Agentic AI platforms [24] indicates growing sophistication in both domains. Second, the comparative analysis highlights fundamental differences between AI paradigms.Where traditional narrow AI excels at specific tasks and generative AI at content creation, Agentic AI introduces autonomous decisionmaking capabilities [2] , while AGI promises (but has not yet achieved) human-level generalization [3] .This spectrum of capabilities suggests a maturation path for AI systems, with Agentic AI serving as a crucial bridge between current technologies and future AGI aspirations [51] .Third, the technical requirements for these advanced AI systems are becoming increasingly demanding, from specialized hardware architectures [39] to novel cloud services supporting autonomous agent deployment [18] .These infrastructure developments both enable and constrain progress in the field.Looking ahead, three critical priorities emerge for researchers and practitioners: Development of Robust Evaluation Frameworks: As Agentic AI systems become more autonomous and AGI research advances, standardized metrics and testing environments [25] will be essential for measuring progress and ensuring reliability.Safety and Governance Mechanisms: The unique risks posed by autonomous systems [50] and potential AGI [38] demand continued investment in alignment research and ethical frameworks.Practical Implementation Strategies: Organizations should focus on incremental adoption of Agentic AI solutions [34] while maintaining realistic expectations about AGI timelines.The rapid evolution of these technologies suggests that while AGI may remain years or decades away, Agentic AI is poised for near-term expansion across industries [20,29] .By maintaining balanced perspectives that acknowledge both the potential and limitations of these paradigms, the AI community can steer development toward beneficial outcomes while mitigating risks.Future research should particularly focus on hybrid architectures that combine the strengths of different approaches [21] , as well as interdisciplinary efforts to address the societal implications of increasingly autonomous AI systems [28,43] Table: 1 Table 1 : Comparison of AI Paradigms
What the sources say
- Not checked yetcites [2] 2. Agentic AI Complete Guide to the Future of Autonomous Systems. May 2025.
- Not checked yetcites [3] 3. TechTarget. What is Artificial General Intelligence? Search Enterprise AI. Available from: https://www.techtarget.com/searchenterpriseai/de finition/artificial-general-intelligence-AGI
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The rapid evolution of these technologies suggests that while AGI may remain years or decades away, Agentic AI is poised for near-term expansion across industries [20,29] .”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > 8. Conclusion While AGI remains an ambitious and potentially distant goal [7] , Agentic AI represents a practical stepping stone that is already delivering value across industries [9,56] .The coming years will likely see continued progress in both areas, with several key developments on the horizon: Refinement of AGI benchmarks and measurement frameworks [57] Expansion of Agentic AI applications across sectors [18] Increased focus on safety and governance mechanisms [38] Development of hybrid systems combining generative and agentic capabilities [46] As these technologies evolve, it will be crucial to maintain balanced perspectives that acknowledge both their potential and limitations.Future research should focus on developing robust evaluation methodologies, safety protocols, and practical implementation frameworks to ensure these powerful technologies deliver maximum benefit while minimizing risks.This comprehensive review has examined the current state of Artificial General Intelligence (AGI) and Agentic AI, analyzing their technical foundations, capabilities, and trajectories.Our investigation reveals several key insights about the evolving AI landscape: First, while AGI remains an aspirational goal with significant technical hurdles [7,8] , Agentic AI has emerged as a practical intermediate step that is already demonstrating real-world value [9,10] .The development of frameworks like OpenAI's 5-level AGI classification system [23] and specialized Agentic AI platforms [24] indicates growing sophistication in both domains. Second, the comparative analysis highlights fundamental differences between AI paradigms.Where traditional narrow AI excels at specific tasks and generative AI at content creation, Agentic AI introduces autonomous decisionmaking capabilities [2] , while AGI promises (but has not yet achieved) human-level generalization [3] .This spectrum of capabilities suggests a maturation path for AI systems, with Agentic AI serving as a crucial bridge between current technologies and future AGI aspirations [51] .Third, the technical requirements for these advanced AI systems are becoming increasingly demanding, from specialized hardware architectures [39] to novel cloud services supporting autonomous agent deployment [18] .These infrastructure developments both enable and constrain progress in the field.Looking ahead, three critical priorities emerge for researchers and practitioners: Development of Robust Evaluation Frameworks: As Agentic AI systems become more autonomous and AGI research advances, standardized metrics and testing environments [25] will be essential for measuring progress and ensuring reliability.Safety and Governance Mechanisms: The unique risks posed by autonomous systems [50] and potential AGI [38] demand continued investment in alignment research and ethical frameworks.Practical Implementation Strategies: Organizations should focus on incremental adoption of Agentic AI solutions [34] while maintaining realistic expectations about AGI timelines.The rapid evolution of these technologies suggests that while AGI may remain years or decades away, Agentic AI is poised for near-term expansion across industries [20,29] .By maintaining balanced perspectives that acknowledge both the potential and limitations of these paradigms, the AI community can steer development toward beneficial outcomes while mitigating risks.Future research should particularly focus on hybrid architectures that combine the strengths of different approaches [21] , as well as interdisciplinary efforts to address the societal implications of increasingly autonomous AI systems [28,43] Table: 1 Table 1 : Comparison of AI Paradigms
What the sources say
- Not checked yetcites [20] 20. Azhar A. OpenAI Develops New AGI Benchmark to Assess Potential Risks of Advanced AI. AIwire. Oct 2024.
- Not checked yetcites [29] 29. IT Pro. OpenAI says it's charting a "path to AGI" with its next frontier AI model. May 2024. Available from: https://www.itpro.com/technology/artificial- intelligence/openai-says-its-charting-a-path-to-agi-with- its-next-frontier-ai-model 37. USAII. ChatGPT's Operator Mode: A Significant Step Toward AGI in 2025. Available from: https://www.usaii.org/ai-insights/chatgpts- operator-mode-a-significant-step-toward-agi-in-2025
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Recent proposals emphasize the need for urgent safety measures as these technologies advance [38,55] .”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions >
What the sources say
- Not checked yetcites [38] 38. Kancherla N. Agentic AI vs discriminative AI: confused? LinkedIn. Available from: https://www.linkedin.com/pulse/genai-agentic-ai- discriminative-confused-heres-nagaraju-kancherla- xltac/
- Not checked yetcites [55] 55. Joshi S. Review of Data Engineering Frameworks (Trino and Kubernetes) for Implementing Generative AI in Financial Risk. Int J Res Publ Rev. 2025;6(2):1461-70. DOI:10.55248/gengpi.6.0225.0756
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“OpenAI's”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > Abstract
What the sources say
- Not checked yetcites [39] 39. Amin S. Agentic AI vs AGI. LinkedIn. Available from: https://www.linkedin.com/pulse/agentic-ai-vs- agi-whats-difference-swapnil-amin-5ctoc/
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Planning modules, memory systems, tool integration, reasoning components”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [27]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Multi-step, multi-layered tasks over extended time frames”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [27]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“More advanced models, like Vectara-agentic [27] and AutoGPT [34], can take text input, read the user's prompt to create outputs or undertake actions, and coordinate with other agents without human assistance.”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [27]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Dynamic adaptation to changing environments and goals”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [27]
The cited source says what the claim attributes to it.
In the citing paper
“Self-defined and executed goal-oriented behavior”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [27]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > 1. Introduction
“Section IV discusses challenges and limitations.Section V examines applications and implications, and Section VI concludes with future directions.”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > 7. Challenges and Limitations A) Technical Challenges The path to AGI faces several significant technical hurdles:
“## 7. Challenges and Limitations A) Technical Challenges The path to AGI faces several significant technical hurdles:”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions
“CrewAI: An open-source framework supporting the creation of collaborative AI agents with role specialization and task delegation [31] .”
§ Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions > References
“31. AI Business. Most Read: $500B Stargate AI Infrastructure Project Launched for Future of Technology.”
Citations
Cites
Joshi, S. (2025) · World Journal of Advanced Engineering Technology and Sciences · doi:10.30574/wjaets.2025.14.2.0071 · Cited by 48
Self-citationReview of autonomous systems and collaborative AI agent frameworks
Joshi, S. (2025) · International Journal of Science and Research Archive · doi:10.30574/ijsra.2025.14.2.0439 · Cited by 31
Self-citationImplementing Gen AI for Increasing Robustness of US Financial and Regulatory System
Joshi, S. (2024) · International Journal of Innovative Research in Engineering & Management · doi:10.55524/ijirem.2024.11.6.19 · Cited by 27
Self-citationLeveraging prompt engineering to enhance financial market integrity and risk management
Joshi, S. (2025) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2025.25.1.0279 · Cited by 23
Self-citationA Literature Review of Gen AI Agents in Financial Applications: Models and Implementations
Joshi, S. (2025) · International Journal of Science and Research (IJSR) · doi:10.21275/sr25125102816 · Cited by 21
Self-citationJoshi, S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5126922 · Cited by 15
Self-citationJoshi, S. (2025) · International Journal of Research Publication and Reviews · doi:10.55248/gengpi.6.0225.0756 · Cited by 15
Self-citationJoshi, S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5123081 · Cited by 11
Self-citationJoshi, S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5135229 · Cited by 8
Self-citation
Research works printed but not linked: 6
- [23]23. National Law Review. Agentic Artificial Intelligence and the New Legal Playbook. Available from: https://natlawreview.com/article/thinking-lawyer- agentic-ai-and-new-legal-playbook
- [56]56. Joshi S. Review of Data Pipelines and Streaming for Generative AI Integration: Challenges, Solutions, and Future Directions. Int J Res Publ Rev. 2025;6(2):2348- 57.
- [57]57. Joshi S. The Synergy of Generative AI and Big Data for Financial Risk: Review of Recent Developments. IJFMR. 2025;7(1). DOI:https://doi.org/g82gmx
- [58]58. Joshi S. The Transformative Role of Agentic GenAI in Shaping Workforce Development and Education in the US. SSRN. 2025 Feb 1. Available from: https://papers.ssrn.com/abstract=5133376
- [59]59. Joshi S. Review of Data Engineering Frameworks (Trino and Kubernetes) for Implementing Generative AI in Financial Risk. Int J Res Publ Rev. 2025;6(2):1461-70. DOI:10.55248/gengpi.6.0225.0756 · DOI
- [60]60. Joshi S. Review of autonomous systems and collaborative AI agent frameworks. Int J Sci Res Arch. 2025;14(2):961-72. DOI:10.30574/ijsra.2025.14.2.0439 · DOI · Paper page
Cited by
Sorted by citations
Bandi, A., Kongari, B., Naguru, R., Pasnoor, S., & Vilipala, S. V. (2025) · Future Internet · doi:10.3390/fi17090404 · Cited by 207
Agentic AI: A Perspective on Architecture, Frameworks and Applications
Raghavendra, P., & Saikia, M. J. (2026) · AI · doi:10.3390/ai7060219 · Cited by 4
Agentic Artificial Intelligence for Corporate Governance and Workforce Preparedness
Chahar, S., Kaswan, K. S., Agarwal, J., Dhatterwal, J. S., & Panwar, A. (2025) · Global AI Summit - International Conference on Artificial Intelligence and Emerging Technology (AI Summit) · doi:10.1109/aisummit66170.2025.11411150 · Cited by 2
Artificial General Intelligence: Current Progress, Safety Considerations, and Ethical Imperatives
Potdukhe, N., Pacharney, U., Mankar, M., Waghale, A., Gadbail, H. N., & Dube, N. (2026) · International Conference on Image Processing and Capsule Networks (ICIPCN) · doi:10.1109/icipcn67432.2026.11438447 · Cited by 1
Building Data Engineering Pipelines for Agentic AI in FinTech
Kasaram, C. R. (2025) · IEEE International Conference on Recent Trends in Computing and Smart Mobility (RCSM) · doi:10.1109/rcsm67767.2025.11507760
Metadata
- Authors
- Joshi, S.
- Affiliations
- Bar-Ilan University (IL)
- Corresponding author
- Joshi, Satyadhar
- ORCID
- Joshi, Satyadhar
- Venue
- International Journal of Multidisciplinary Research and Growth Evaluation
- Publisher
- Anfo Publication House
- Published
- 2025
- Volume
- 6
- Issue
- 3
- Pages
- 681-688
- ISSN
- 2582-7138 (ISSN-L)
- OpenAlex
- W4410479913
- Semantic Scholar
- 1f8df7e4e46cc42008ab31e2e43473d3bd77d70e
- Cited by
- 6
- References
- 9
- Citations by year
- 2026: 3 · 2025: 3
- FWCI
- 2.41
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc-sa
- Locations
- International Journal of Multidisciplinary Research and Growth Evaluation (publishedVersion, cc-by-nc-sa)
- PhilPapers (PhilPapers Foundation) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Psychiatry, Mental Health, Neuroscience
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- artificial general intelligence, agentic AI, generative AI, CrewAI, NVIDIA GPUs, Stargate, AI safety, AI alignment, AI governance, enterprise automation, large language models
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-11, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
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- This page
- CoChat. (2026). Citation check: Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions. https://app.cochat.ai/papers/10.54660/.ijmrge.2025.6.3.681-688
- The paper
- Joshi, S. (2025). Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions. International Journal of Multidisciplinary Research and Growth Evaluation. https://doi.org/10.54660/.ijmrge.2025.6.3.681-688
