Araştırma Makalesi
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Agentic artificial intelligence: A multidimensional framework for autonomous systems, sustainable impact, and scholarly evolution

Yıl 2025, Cilt: 1 Sayı: 2, 105 - 128, 27.12.2025

Öz

Agentic Artificial Intelligence (Agentic AI) signifies a significant progression in intelligent agents, transitioning from reactive to proactive functionalities, incorporating contextual reasoning, learning, and autonomous action. This research delineates four principal shortcomings in the current literature and introduces a comprehensive framework encompassing theoretical foundations, operational structures, and sustainability considerations. The goals are: (1) to make a list of agentic systems based on autonomy, memory, interaction, and learning; (2) to look at how low-code orchestration platforms like Sim and n8n affect agent-based workflows; (3) to see how Agentic AI helps with certain United Nations SDGs; and (4) to do a computational analysis of 218 Scopus-indexed publications from 2023 to 2025 to find research themes in Agentic AI. The study employs KNIME for data preparation and Parallel LDA for topic modeling, uncovering emerging research clusters and emphasizing the insufficient attention to ethical governance, algorithmic accountability, and environmental sustainability. The results show that to get the most out of Agentic AI for society, it is important to combine technical progress with human oversight, frameworks for explainability, and following ethics-by-design principles.

Kaynakça

  • Almulhim, A. I., & Yigitcanlar, T. (2025). Understanding Smart Governance of Sustainable Cities: A Review and Multidimensional Framework. Smart Cities, 8(4), 113.
  • Angubasu, J. (2022). The Implementation of Ai Self-triage Systems as a Digital Health Solution for Primary Healthcare in Kenya: Challenges and Prospects (Doctoral dissertation, University of Nairobi).
  • Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual review of psychology, 52(1), 1-26.
  • Bandura, A. (2008). Toward an agentic theory of the self. Advances in self research, 3, 15-49.
  • Barra, F. L., Rodella, G., Costa, A., Scalogna, A., Carenzo, L., Monzani, A., & Corte, F. D. (2025). From prompt to platform: an agentic AI workflow for healthcare simulation scenario design. Advances in Simulation, 10(1), 29.
  • Berthold, M., Cebron, N., Dill, F., Gabriel, T., Kötter, T., Meinl, T., ... & Wiswedel, B. (2008). Data Analysis, Machine Learning and Applications SE-38, Studies in Classification, Data Analysis, and Knowledge Organization.
  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research, 3(Jan), 993-1022.
  • Braun, V., & Clarke, V. (2019). Psikolojide tematik analizin kullanımı. Journal of Qualitative Research in Education, 7(2).
  • Cantero Gamito, M., & Marsden, C. T. (2024). Artificial intelligence co-regulation? The role of standards in the EU AI Act. International journal of law and information technology, 32, eaae011.
  • Cowls, J., Tsamados, A., Taddeo, M., & Floridi, L. (2023). The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations. Ai & Society, 38(1), 283-307.
  • Creswell, J. W., & Plano Clark, V. L. (2023). Revisiting mixed methods research designs twenty years later. Handbook of mixed methods research designs, 1(1), 21-36.
  • Delaney, Y., McCarthy, J., & Beecham, S. (2017, June). Convergent parallel design mixed methods case study in problem-based learning. In ECRM 2017 16th European Conference on Research Methods in Business and Management (p. 408). Academic Conferences and publishing limited.
  • Delen, D. (2024). Landscape of Tools for Business Analytics and Data Science–A Tutorial on KNIME.
  • Ergani, G. Ç. (2024). Sürdürülebilirlik Raporlaması Için Standartlara Uygun Veri Toplama ve Analitik Yaklaşımlar ile Karar Verme: Simülasyon Tabanlı bir Uygulama (Master's thesis, Marmara Universitesi (Turkey)).
  • Floratos, A., Smith, K., Ji, Z., Watkinson, J., & Califano, A. (2010). geWorkbench: an open source platform for integrative genomics. Bioinformatics, 26(14), 1779-1780.
  • Floridi, L. (2013). The ethics of information. Oxford University Press (UK).
  • Floridi, L., & Cowls, J. (2022). A unified framework of five principles for AI in society. Machine learning and the city: Applications in architecture and urban design, 535-545.
  • Goecks, J., Nekrutenko, A., Taylor, J., & Galaxy Team team@ galaxyproject. org. (2010). Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome biology, 11(8), R86.
  • Griggs, D., Stafford-Smith, M., Gaffney, O., Rockström, J., Öhman, M. C., Shyamsundar, P., ... & Noble, I. (2013). Sustainable development goals for people and planet. Nature, 495(7441), 305-307.
  • Hatch, S. G., Goodman, Z. T., Vowels, L., Hatch, H. D., Brown, A. L., Guttman, S., ... & Braithwaite, S. R. (2025). When ELIZA meets therapists: A Turing test for the heart and mind. PLOS Mental Health, 2(2), e0000145.
  • Hull, D., Wolstencroft, K., Stevens, R., Goble, C., Pocock, M. R., Li, P., & Oinn, T. (2006). Taverna: a tool for building and running workflows of services. Nucleic acids research, 34(suppl_2), W729-W732.
  • Grimmer, J., & Stewart, B. M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political analysis, 21(3), 267-297.
  • Guidance, W. H. O. (2021). Ethics and governance of artificial intelligence for health. World Health Organization.
  • Jagla, B., Wiswedel, B., & Coppée, J. Y. (2011). Extending KNIME for next-generation sequencing data analysis. Bioinformatics, 27(20), 2907-2909.
  • Jeong, C. (2025). Beyond Text: Implementing Multimodal Large Language Model-Powered Multi-Agent Systems Using a No-Code Platform. arXiv preprint arXiv:2501.00750.
  • Komtaş. (2025). Agentic yapay zeka (Agentic AI) nedir?, Erişim Tarihi:28.10.2025, Erişim adresi: https://www.komtas.com/glossary/agentic-yapay-zeka-nedir
  • Krippendorff, K. (2018). Content analysis: An introduction to its methodology. Sage publications.
  • Leal-Arcas, R. (2025). The Future of Global Economic Governance: Balancing Trade, Sustainability, and Social Justice. Sustainability, and Social Justice (February 16, 2025).
  • Lee, B. X., Kjaerulf, F., Turner, S., Cohen, L., Donnelly, P. D., Muggah, R., ... & Gilligan, J. (2016). Transforming our world: implementing the 2030 agenda through sustainable development goal indicators. Journal of public health policy, 37(Suppl 1), 13-31.
  • Linke, B. (2012). Conveyor-a workflow engine for bioinformatics analyses.
  • Ludäscher, B., Altintas, I., Berkley, C., Higgins, D., Jaeger, E., Jones, M., ... & Zhao, Y. (2006). Scientific workflow management and the Kepler system. Concurrency and computation: Practice and experience, 18(10), 1039-1065.
  • Mahela, O. P., Khosravy, M., Gupta, N., Khan, B., Alhelou, H. H., Mahla, R., ... & Siano, P. (2020). Comprehensive overview of multi-agent systems for controlling smart grids. CSEE Journal of Power and Energy Systems, 8(1), 115-131.
  • Nakicenovic, N., Messner, D., Zimm, C., Clarke, G., Rockström, J., Aguiar, A. P., ... & Yillia, P. (2019). TWI2050-The World in 2050 (2019). The Digital Revolution and Sustainable Development: Opportunities and Challenges. Report prepared by The World in 2050 initiative.
  • Nelson, L. K. (2020). Computational grounded theory: A methodological framework. Sociological methods & research, 49(1), 3-42.
  • Néron, B., Ménager, H., Maufrais, C., Joly, N., Maupetit, J., Letort, S., ... & Letondal, C. (2009). Mobyle: a new full web bioinformatics framework. Bioinformatics, 25(22), 3005-3011.
  • Newman, D., Asuncion, A., Smyth, P., & Welling, M. (2009). Distributed algorithms for topic models. Journal of Machine Learning Research, 10(8).
  • n8n.io. (2025). Flexible AI workflow automation for technical teams. https://n8n.io/, erişim tarihi: 10.10.2025
  • Okyay, E. K. (2020). Understanding the role of the national human rights institutions in implementing the sustainable development agenda: the case of Europe (Master's thesis, Middle East Technical University (Turkey)).
  • Parmaksız, H., & Akarsu, O. (2025). Diagnosing Core Topics in Digital Transformation Studies via Topic Model Approach. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, (57), 262-282.
  • Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International journal of artificial intelligence in education, 26(2), 582-599.
  • Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., ... & Bengio, Y. (2022). Tackling climate change with machine learning. ACM Computing Surveys (CSUR), 55(2), 1-96.
  • Rotar, C., & Zhang, Q. (2025). A design science research approach to Large Language Model-Based Agents for Requirements Specification (LLMBA4RS) in low-code applications. Requirements Engineering, 1-24.
  • Russell, S. J., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach, Global Edition 4e.
  • Sachs, J. D. (2015). The age of sustainable development. Columbia University Press.
  • Sachs, J. D., Schmidt-Traub, G., Mazzucato, M., Messner, D., Nakicenovic, N., & Rockström, J. (2019). Six transformations to achieve the sustainable development goals. Nature sustainability, 2(9), 805-814.
  • Schaeffer, J., & Plaat, A. (1997). Kasparov versus deep blue: The rematch.
  • Schneider, J. (2025). Generative to agentic ai: Survey, conceptualization, and challenges. arXiv preprint arXiv:2504.18875.
  • Searle, J. (1999). The chinese room.
  • Shinn, N., Cassano, F., Gopinath, A., Narasimhan, K., & Yao, S. (2023). Reflexion: Language agents with verbal reinforcement learning. Advances in Neural Information Processing Systems, 36, 8634-8652.
  • Shneiderman, B. (2022). Human-centered AI. Oxford University Press.
  • Silah, E., & Eğilmez, Ö. (2025). Eğitimde sürdürülebilir kalkınma hedefleri üzerine bir değerlendirme. Sürdürülebilirlik, Yönetim & Ekonomi Dergisi, 1(1), 58-90.
  • Sim AI Agent, (2025). Sim: Build and deploy AI agent workflows in minutes. GitHub. https://github.com/simstudioai/sim, erişim tarihi: 10.10.2025
  • Turan, T., Turan, G., & Kırbaş, İ. Yapay zekanın çevresel ayak izi: Enerji, su ve elektronik atık üzerine çok yönlü bir analiz. Uluslararası Mühendislik Tasarım ve Teknoloji Dergisi, 7(2), 78-89.
  • Turing, A. M. (1950). Mind. Mind, 59(236), 433-460.
  • Van Niekerk, A. J. (2020). Inclusive economic sustainability: SDGs and global inequality. Sustainability, 12(13), 5427.
  • Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., ... & Fuso Nerini, F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature communications, 11(1), 233.
  • Viswanadhapalli, V. (2025). The Future of Intelligent Automation: How Low-Code/No-Code Platforms are Transforming AI Decisioning. International Journal Of Engineering And Computer Science, 14(1), 26803-26825.
  • Wooldridge, M. (1999). Intelligent agents. Multiagent systems: A modern approach to distributed artificial intelligence, 1, 27-73.
  • Yao, L., Mimno, D., & McCallum, A. (2009, June). Efficient methods for topic model inference on streaming document collections. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 937-946).
  • Yu, C., Cheng, Z., Cui, H., Gao, Y., Luo, Z., Wang, Y., ... & Zhao, Y. (2025, May). A survey on agent workflow–status and future. In 2025 8th International Conference on Artificial Intelligence and Big Data (ICAIBD) (pp. 770-781). IEEE.

Ajan yapay zekâ: Otonom sistemler, sürdürülebilir etki ve akademik evrim için çok boyutlu bir çerçeve

Yıl 2025, Cilt: 1 Sayı: 2, 105 - 128, 27.12.2025

Öz

Ajan yapay zekâ (Ajan YZ), akıllı ajanlarda önemli bir ilerlemeyi ifade eder ve reaktif işlevlerden proaktif işlevlere geçiş yaparak bağlamsal muhakeme, öğrenme ve otonom eylemleri bir araya getirir. Bu çalışma, mevcut literatürdeki dört önemli eksikliği belirlemekte ve teorik temeller, operasyonel yapılar ve sürdürülebilirlik unsurlarını içeren kapsamlı bir çerçeve sunmaktadır. Amaçlar şunlardır: (1) özerklik, bellek, etkileşim ve öğrenme ile karakterize edilen ajan sistemlerinin bir listesini derlemek; (2) Sim ve n8n gibi düşük kodlu orkestrasyon platformlarının ajan tabanlı iş akışları üzerindeki etkisini incelemek; (3) Ajan YZ'nin belirli Birleşmiş Milletler Sürdürülebilir Kalkınma Hedeflerine (SDG'ler) nasıl katkıda bulunduğunu araştırmak; ve (4) Ajan YZ'deki araştırma temalarını belirlemek için 2023'ten 2025'e kadar Scopus indeksli 218 yayının hesaplamalı analizini yapmak. Araştırma, veri hazırlama için KNIME ve konu modelleme için Paralel LDA kullanarak, gelişmekte olan araştırma kümelerini ortaya koymakta ve etik yönetişim, algoritmik hesap verebilirlik ve çevresel sürdürülebilirlik konularına yeterince odaklanılmadığını vurgulamaktadır. Bulgular, Ajan YZ'nin toplumsal faydalarını en üst düzeye çıkarmak için teknolojik ilerlemenin insan denetimi, şeffaflık çerçeveleri ve tasarım aşamasında etik ilkelere bağlılık ile entegre edilmesi gerektiğini göstermektedir.

Kaynakça

  • Almulhim, A. I., & Yigitcanlar, T. (2025). Understanding Smart Governance of Sustainable Cities: A Review and Multidimensional Framework. Smart Cities, 8(4), 113.
  • Angubasu, J. (2022). The Implementation of Ai Self-triage Systems as a Digital Health Solution for Primary Healthcare in Kenya: Challenges and Prospects (Doctoral dissertation, University of Nairobi).
  • Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual review of psychology, 52(1), 1-26.
  • Bandura, A. (2008). Toward an agentic theory of the self. Advances in self research, 3, 15-49.
  • Barra, F. L., Rodella, G., Costa, A., Scalogna, A., Carenzo, L., Monzani, A., & Corte, F. D. (2025). From prompt to platform: an agentic AI workflow for healthcare simulation scenario design. Advances in Simulation, 10(1), 29.
  • Berthold, M., Cebron, N., Dill, F., Gabriel, T., Kötter, T., Meinl, T., ... & Wiswedel, B. (2008). Data Analysis, Machine Learning and Applications SE-38, Studies in Classification, Data Analysis, and Knowledge Organization.
  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research, 3(Jan), 993-1022.
  • Braun, V., & Clarke, V. (2019). Psikolojide tematik analizin kullanımı. Journal of Qualitative Research in Education, 7(2).
  • Cantero Gamito, M., & Marsden, C. T. (2024). Artificial intelligence co-regulation? The role of standards in the EU AI Act. International journal of law and information technology, 32, eaae011.
  • Cowls, J., Tsamados, A., Taddeo, M., & Floridi, L. (2023). The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations. Ai & Society, 38(1), 283-307.
  • Creswell, J. W., & Plano Clark, V. L. (2023). Revisiting mixed methods research designs twenty years later. Handbook of mixed methods research designs, 1(1), 21-36.
  • Delaney, Y., McCarthy, J., & Beecham, S. (2017, June). Convergent parallel design mixed methods case study in problem-based learning. In ECRM 2017 16th European Conference on Research Methods in Business and Management (p. 408). Academic Conferences and publishing limited.
  • Delen, D. (2024). Landscape of Tools for Business Analytics and Data Science–A Tutorial on KNIME.
  • Ergani, G. Ç. (2024). Sürdürülebilirlik Raporlaması Için Standartlara Uygun Veri Toplama ve Analitik Yaklaşımlar ile Karar Verme: Simülasyon Tabanlı bir Uygulama (Master's thesis, Marmara Universitesi (Turkey)).
  • Floratos, A., Smith, K., Ji, Z., Watkinson, J., & Califano, A. (2010). geWorkbench: an open source platform for integrative genomics. Bioinformatics, 26(14), 1779-1780.
  • Floridi, L. (2013). The ethics of information. Oxford University Press (UK).
  • Floridi, L., & Cowls, J. (2022). A unified framework of five principles for AI in society. Machine learning and the city: Applications in architecture and urban design, 535-545.
  • Goecks, J., Nekrutenko, A., Taylor, J., & Galaxy Team team@ galaxyproject. org. (2010). Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome biology, 11(8), R86.
  • Griggs, D., Stafford-Smith, M., Gaffney, O., Rockström, J., Öhman, M. C., Shyamsundar, P., ... & Noble, I. (2013). Sustainable development goals for people and planet. Nature, 495(7441), 305-307.
  • Hatch, S. G., Goodman, Z. T., Vowels, L., Hatch, H. D., Brown, A. L., Guttman, S., ... & Braithwaite, S. R. (2025). When ELIZA meets therapists: A Turing test for the heart and mind. PLOS Mental Health, 2(2), e0000145.
  • Hull, D., Wolstencroft, K., Stevens, R., Goble, C., Pocock, M. R., Li, P., & Oinn, T. (2006). Taverna: a tool for building and running workflows of services. Nucleic acids research, 34(suppl_2), W729-W732.
  • Grimmer, J., & Stewart, B. M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political analysis, 21(3), 267-297.
  • Guidance, W. H. O. (2021). Ethics and governance of artificial intelligence for health. World Health Organization.
  • Jagla, B., Wiswedel, B., & Coppée, J. Y. (2011). Extending KNIME for next-generation sequencing data analysis. Bioinformatics, 27(20), 2907-2909.
  • Jeong, C. (2025). Beyond Text: Implementing Multimodal Large Language Model-Powered Multi-Agent Systems Using a No-Code Platform. arXiv preprint arXiv:2501.00750.
  • Komtaş. (2025). Agentic yapay zeka (Agentic AI) nedir?, Erişim Tarihi:28.10.2025, Erişim adresi: https://www.komtas.com/glossary/agentic-yapay-zeka-nedir
  • Krippendorff, K. (2018). Content analysis: An introduction to its methodology. Sage publications.
  • Leal-Arcas, R. (2025). The Future of Global Economic Governance: Balancing Trade, Sustainability, and Social Justice. Sustainability, and Social Justice (February 16, 2025).
  • Lee, B. X., Kjaerulf, F., Turner, S., Cohen, L., Donnelly, P. D., Muggah, R., ... & Gilligan, J. (2016). Transforming our world: implementing the 2030 agenda through sustainable development goal indicators. Journal of public health policy, 37(Suppl 1), 13-31.
  • Linke, B. (2012). Conveyor-a workflow engine for bioinformatics analyses.
  • Ludäscher, B., Altintas, I., Berkley, C., Higgins, D., Jaeger, E., Jones, M., ... & Zhao, Y. (2006). Scientific workflow management and the Kepler system. Concurrency and computation: Practice and experience, 18(10), 1039-1065.
  • Mahela, O. P., Khosravy, M., Gupta, N., Khan, B., Alhelou, H. H., Mahla, R., ... & Siano, P. (2020). Comprehensive overview of multi-agent systems for controlling smart grids. CSEE Journal of Power and Energy Systems, 8(1), 115-131.
  • Nakicenovic, N., Messner, D., Zimm, C., Clarke, G., Rockström, J., Aguiar, A. P., ... & Yillia, P. (2019). TWI2050-The World in 2050 (2019). The Digital Revolution and Sustainable Development: Opportunities and Challenges. Report prepared by The World in 2050 initiative.
  • Nelson, L. K. (2020). Computational grounded theory: A methodological framework. Sociological methods & research, 49(1), 3-42.
  • Néron, B., Ménager, H., Maufrais, C., Joly, N., Maupetit, J., Letort, S., ... & Letondal, C. (2009). Mobyle: a new full web bioinformatics framework. Bioinformatics, 25(22), 3005-3011.
  • Newman, D., Asuncion, A., Smyth, P., & Welling, M. (2009). Distributed algorithms for topic models. Journal of Machine Learning Research, 10(8).
  • n8n.io. (2025). Flexible AI workflow automation for technical teams. https://n8n.io/, erişim tarihi: 10.10.2025
  • Okyay, E. K. (2020). Understanding the role of the national human rights institutions in implementing the sustainable development agenda: the case of Europe (Master's thesis, Middle East Technical University (Turkey)).
  • Parmaksız, H., & Akarsu, O. (2025). Diagnosing Core Topics in Digital Transformation Studies via Topic Model Approach. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, (57), 262-282.
  • Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International journal of artificial intelligence in education, 26(2), 582-599.
  • Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., ... & Bengio, Y. (2022). Tackling climate change with machine learning. ACM Computing Surveys (CSUR), 55(2), 1-96.
  • Rotar, C., & Zhang, Q. (2025). A design science research approach to Large Language Model-Based Agents for Requirements Specification (LLMBA4RS) in low-code applications. Requirements Engineering, 1-24.
  • Russell, S. J., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach, Global Edition 4e.
  • Sachs, J. D. (2015). The age of sustainable development. Columbia University Press.
  • Sachs, J. D., Schmidt-Traub, G., Mazzucato, M., Messner, D., Nakicenovic, N., & Rockström, J. (2019). Six transformations to achieve the sustainable development goals. Nature sustainability, 2(9), 805-814.
  • Schaeffer, J., & Plaat, A. (1997). Kasparov versus deep blue: The rematch.
  • Schneider, J. (2025). Generative to agentic ai: Survey, conceptualization, and challenges. arXiv preprint arXiv:2504.18875.
  • Searle, J. (1999). The chinese room.
  • Shinn, N., Cassano, F., Gopinath, A., Narasimhan, K., & Yao, S. (2023). Reflexion: Language agents with verbal reinforcement learning. Advances in Neural Information Processing Systems, 36, 8634-8652.
  • Shneiderman, B. (2022). Human-centered AI. Oxford University Press.
  • Silah, E., & Eğilmez, Ö. (2025). Eğitimde sürdürülebilir kalkınma hedefleri üzerine bir değerlendirme. Sürdürülebilirlik, Yönetim & Ekonomi Dergisi, 1(1), 58-90.
  • Sim AI Agent, (2025). Sim: Build and deploy AI agent workflows in minutes. GitHub. https://github.com/simstudioai/sim, erişim tarihi: 10.10.2025
  • Turan, T., Turan, G., & Kırbaş, İ. Yapay zekanın çevresel ayak izi: Enerji, su ve elektronik atık üzerine çok yönlü bir analiz. Uluslararası Mühendislik Tasarım ve Teknoloji Dergisi, 7(2), 78-89.
  • Turing, A. M. (1950). Mind. Mind, 59(236), 433-460.
  • Van Niekerk, A. J. (2020). Inclusive economic sustainability: SDGs and global inequality. Sustainability, 12(13), 5427.
  • Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., ... & Fuso Nerini, F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature communications, 11(1), 233.
  • Viswanadhapalli, V. (2025). The Future of Intelligent Automation: How Low-Code/No-Code Platforms are Transforming AI Decisioning. International Journal Of Engineering And Computer Science, 14(1), 26803-26825.
  • Wooldridge, M. (1999). Intelligent agents. Multiagent systems: A modern approach to distributed artificial intelligence, 1, 27-73.
  • Yao, L., Mimno, D., & McCallum, A. (2009, June). Efficient methods for topic model inference on streaming document collections. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 937-946).
  • Yu, C., Cheng, Z., Cui, H., Gao, Y., Luo, Z., Wang, Y., ... & Zhao, Y. (2025, May). A survey on agent workflow–status and future. In 2025 8th International Conference on Artificial Intelligence and Big Data (ICAIBD) (pp. 770-781). IEEE.
Toplam 60 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Sürdürülebilir Kalkınma ve Kamu Yararına Bilgi Sistemleri, Yapay Zeka (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Hüseyin Parmaksız 0000-0001-8455-5625

Gönderilme Tarihi 11 Ekim 2025
Kabul Tarihi 7 Kasım 2025
Yayımlanma Tarihi 27 Aralık 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 1 Sayı: 2

Kaynak Göster

APA Parmaksız, H. (2025). Ajan yapay zekâ: Otonom sistemler, sürdürülebilir etki ve akademik evrim için çok boyutlu bir çerçeve. Sürdürülebilirlik, Yönetim & Ekonomi Dergisi, 1(2), 105-128.