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Towards Role Definition in Agile AI-based System Development: Perspectives and Reflections
Open accessAbstract
With the evolution of AI, software engineering practices must adapt to the unique demands of AI-based systems, ranging from standalone intelligent agents to complex machine learning applications in fields like healthcare and autonomous vehicles.This paper investigates the roles required for AI-based system development from literature and professionals' perception and how we can integrate these roles with Scrum teams focusing on intelligent agents for multi-agent systems, pattern recognition systems, and evolutionary computation for optimization problems.Initially, a integrative review was conducted, and a survey was applied with 11 AI professionals allocated to different organizations.Our results indicate partial alignment of AI-based system development teams with existing literature regarding roles and responsibilities, highlighting the need for formalized collaborations.
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“Taken together, these findings suggest that multi-agent AI systems do not merely “add” risks on top of existing medical AI concerns; rather, they reconfigure the ethical landscape by making clinical agency emergent from interactions among multiple components and actors, thereby weakening traditional assumptions about traceability, contestability, and professional control (6, 15, 31, 43, 52).”
From Ethical issues in multi-agent AI systems for healthcare: a narrative review (2026) · cites this paper as [52]
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Research works printed but not linked: 30
- 10.1109/ICSE-SEIP.2019.00042 · DOI
- Gary Anthes. 2017. Artificial intelligence poised to ride a new wave. Commun. ACM 60 7 (2017) 19–21. 10.1145/3088342https://dl.acm.org/doi/10.1145/3088342 · DOI
- 10.1109/CBI49978.2020.00011 · DOI
- 10.1145/3460418.3479344 · DOI
- 10.1007/978-3-030-51110-4_2 · DOI
- 10.5555/1162264 · DOI
- G. Busquim H. Villamizar M. J. Lima and M. Kalinowski. 2024. On the Interaction between Software Engineers and Data Scientists when building Machine Learning-Enabled Systems. https://arxiv.org/abs/2402.05334 · DOI
- Li Deng. 2018. Artificial Intelligence in the Rising Wave of Deep Learning: The Historical Path and Future Outlook [Perspectives]. IEEE Signal Processing Magazine 35 (2018) 180–177. https://api.semanticscholar.org/CorpusID:8137237 · DOI
- 10.1145/3514094.3534187 · DOI
- C. T. Fosnot. 2013. Constructivism: Theory, perspectives, and practice. Teachers College Press.
- 10.1007/978-3-031-29786-1_15 · DOI
- 10.1007/978-3-031-29786-1_15 · DOI
- Chunxiao Jiang Ning Ge and Linling Kuang. 2020. AI-Enabled Next-Generation Communication Networks: Intelligent Agent and AI Router. IEEE Wireless Communications 27 6 (2020) 129–133. 10.1109/MWC.001.2000100 https://dl.acm.org/doi/10.1109/MWC.001.2000100 · DOI
- Marcos Kalinowski Daniel Mendez Görkem Giray Antonio Pedro Santos Alves Kelly Azevedo Tatiana Escovedo Hugo Villamizar Helio Lopes Teresa Baldassarre Stefan Wagner Stefan Biffl Jürgen Musil Michael Felderer Niklas Lavesson and Tony Gorschek. 2024. Naming the Pain in Machine Learning-Enabled Systems Engineering. https://arxiv.org/abs/2406.04359 · arXiv
- 10.1109/ICACITE53722.2022.9823890 · DOI
- Thanh Tung Khuat David Jacob Kedziora and Bogdan Gabrys. 2022. The Roles and Modes of Human Interactions with Automated Machine Learning Systems. · DOI
- Jian Ping Li Amin Ul Haq Salah Ud Din Jalaluddin Khan Asif Khan and Abdus Saboor. 2020. Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare. IEEE Access 8 (2020) 107562–107582. · DOI
- Silverio Martínez-Fernández Justus Bogner Xavier Franch Marc Oriol Julien Siebert Adam Trendowicz Anna Maria Vollmer and Stefan Wagner. 2022. Software Engineering for AI-Based Systems: A Survey. ACM Trans. Softw. Eng. Methodol. 31 2 (2022). 10.1145/3487043https://dl.acm.org/doi/10.1145/3487043 · DOI
- 10.1145/3510003.3510209 · DOI
- K. D. Norman and S. L. Yvonna. 2005. The Discipline and Practice of Qualitative Research. https://api.semanticscholar.org/CorpusID:149701443
- 10.1109/REW56159.2022.00039 · DOI
- Stuart Russell and Peter Norvig. 2009. Artificial Intelligence: A Modern Approach (3rd ed.). Prentice Hall Press, USA.
- Dan Simon. 2013. Evolutionary optimization algorithms : biologically-Inspired and population-based approaches to computer intelligence. https://api.semanticscholar.org/CorpusID:60429433
- 10.1109/REW.2019.00050 · DOI
- Zhanping Wang Juncang Tian and Kepeng Feng. 2022. Optimal allocation of regional water resources based on simulated annealing particle swarm optimization algorithm. Energy Reports 8 (2022) 9119–9126. 10.1016/j.egyr.2022.07.033 · DOI
- R. Whittemore and K. Knafl. 2005. The integrative review: updated methodology. J Adv Nurs. 52 5 (2005) 546–553. 10.1111/j.1365-2648.2005.03621.x · DOI · Paper page
- 10.1145/3387940.3391496 · DOI
- Jiawang Xie Yuzhi Zhao Dezhi Zhu Jianfeng Yan Jiaqun Li Ming Qiao Guangzhi He and Shengfa Deng. 2023. A Machine Learning-Combined Flexible Sensor for Tactile Detection and Voice Recognition. ACS Applied Materials & Interfaces 15 9 (2023) 12551–12559. 10.1021/acsami.2c22287 · DOI
- Guangyou Zhou Zhiwei Zhu and Sumei Luo. 2022. Location optimization of electric vehicle charging stations: Based on cost model and genetic algorithm. Energy 247 (2022) 123437. 10.1016/j.energy.2022.123437 · DOI
- Eduardo Zimelewicz Marcos Kalinowski Daniel Mendez Görkem Giray Antonio Pedro Santos Alves Niklas Lavesson Kelly Azevedo Hugo Villamizar Tatiana Escovedo Helio Lopes Stefan Biffl Juergen Musil Michael Felderer Stefan Wagner Teresa Baldassarre and Tony Gorschek. 2024. ML-Enabled Systems Model Deployment and Monitoring: Status Quo and Problems. https://arxiv.org/abs/2402.05333 · DOI
Cited by
Sorted by citations
Ethical issues in multi-agent AI systems for healthcare: a narrative review
Xie, Z., Wang, H., Dai, L., Wang, Z., Song, H., & Qian, J. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1792627 · Cited by 11
Metadata
- Authors
- Souza, F. C. M., Souza, A. C. C., Amorim, B. F., & Cordeiro, T. D.
- Affiliations
- Universidade Tecnológica Federal do Paraná (BR)
- Universidade Federal de Alagoas (BR)
- ORCID
- Venue
- Proceedings of the XXIII Brazilian Symposium on Software Quality
- Publisher
- ACM
- Published
- 2024-11-05
- Pages
- 220-230
- OpenAlex
- W4405673258
- Cited by
- 3
- References
- 0
- Citations by year
- 2026: 2 · 2025: 1
- FWCI
- 1.14
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Brazilian Symposium on Software Quality (SBQS) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Keywords
- agile software development, Scrum, role definition, intelligent agents, multi-agent systems, machine learning applications, evolutionary computation, pattern recognition, professional perceptions, team collaboration
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- CoChat. (2026). Citation check: Towards Role Definition in Agile AI-based System Development: Perspectives and Reflections. https://app.cochat.ai/papers/10.1145/3701625.3701661
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- Souza, F. C. M., Souza, A. C. C., Amorim, B. F., & Cordeiro, T. D. (2024). Towards Role Definition in Agile AI-based System Development: Perspectives and Reflections. Proceedings of the XXIII Brazilian Symposium on Software Quality. https://doi.org/10.1145/3701625.3701661