An Efficient Solution towards SDLC Automation using Multi-Agent Integration through Crew AI

Authors

  • Nikhil Sagar Miriyala Research Scholar
  • Bharath Kumar Bandaru
  • Prakhar Mittal
  • Kiran Babu Macha
  • Rishi Venkat
  • Anu Rai

DOI:

https://doi.org/10.22399/ijcesen.2384

Keywords:

SDLC Automation, GenAI Tools, CrewAI, Multi-Agent Systems, AI-Orchestrated Integration, Workflow Engines

Abstract

Abstract

Cloud-native CI/CD pipelines are transforming corporate development, testing, and large-scale software deployment. GitOps-based tools, infrastructure-as-code, and container orchestration together provide a strong, automated, scalable software delivery method. Analyzing the influence of cloud-native CI/CD methodologies on deployment efficiency in commercial contexts, this study presents four main performance measures: deployment frequency, lead time for modifications, change failure rate, and mean time to recovery. Supported by a mix of peer-reviewed research and pragmatic case scenarios, this paper emphasizes the obvious benefits of operational stability and delivery speed attained through modern CI/CD platforms, including GitHub Actions, ArgoCD, Tekton, and Azure DevOps. Apart from evaluating performance, the study discusses the security, technological, and organizational issues usually faced during implementation. The final result provides tactical insights meant for use in corporate environments. The results offer a realistic, pragmatic view of how cloud-native CI/CD pipelines might improve dependability, adaptability, and competitiveness in large-scale software systems.

References

[1] S, S. (2017). A Study of Software Development Life Cycle Process Models. Social Science Research Network. https://doi.org/10.2139/SSRN.2988291 DOI: https://doi.org/10.2139/ssrn.2988291

[2] Anthony, A. R. V., Prasad, G. D., Randunuge, S. U., Alahakoon, S. R. A. M. P. A., Wijendra, D. R., & Krishara, J. (2020). Software development automation: An approach to automate the processes of SDLC. International Journal of Computer Applications, 175(37), 44-51. DOI: https://doi.org/10.5120/ijca2020920942

[3] Şimşek, T., Gülşeni, Ç., & Olcay, G. A. (2024). The Future of Software Development With GenAI: Evolving Roles of Software Personas. IEEE Engineering Management Review. DOI: https://doi.org/10.1109/EMR.2024.3454112

[4] Raghi, K. R., Sudha, K., & Sreeram, A. M. (2024, December). Software Development Automation Using Generative AI. In 2024 International Conference on Emerging Research in Computational Science (ICERCS) (pp. 1-6). IEEE. DOI: https://doi.org/10.1109/ICERCS63125.2024.10894980

[5] CrewAI, “Introduction,” Accessed: Feb 2025. [Online]. Available: https://docs.crewai.com/introduction

[6] Lakhamraju, M. V. (2024). The importance of data analytics in business process optimization: A focus on predictive process monitoring. African Journal of Biomedical Research, 27(3S), 6937–6941. https://doi.org/10.53555/AJBR.v27i3S.6645 DOI: https://doi.org/10.53555/AJBR.v27i3S.6645

[7] Khan, I. A., & Kumari, D. (2021). The role of analysis phase of SDLC for small scale business application-a review. International Journal of Humanities, Engineering, Science and Management, 2(01), 63-75.

[8] Jackson, V., Vaz Pereira, G., Prikladnicki, R., van der Hoek, A., Fortes, L., Araújo, C., ... & Ramos, D. (2025). Exploring GenAI in Software Development: Insights from a Case Study in a Large Brazilian Company.

[9] Maharana, T., Agrawal, N., Sharma, V., & Alkhayyat, A. (2024, November). An Intelligent Hybrid GenAI Model for Software Testing. In 2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC) (pp. 1-5). IEEE. DOI: https://doi.org/10.1109/ICEC59683.2024.10837186

[10] Tembhekar, P., Devan, M., & Jeyaraman, J. (2023). Role of GenAI in Automated Code Generation within DevOps Practices: Explore how Generative AI. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 2(2), 500-512. https://doi.org/10.60087/jklst.vol2.n2.p512 DOI: https://doi.org/10.60087/jklst.vol2.n2.p512

[11] Joshi, S. (2025). Review of autonomous systems and collaborative AI agent frameworks. International Journal of Science and Research Archive, 14(2), 961-972. DOI: https://doi.org/10.30574/ijsra.2025.14.2.0439

[12] Tomassi, A. (2024). Data Security and Privacy Concerns for Generative AI Platforms (Doctoral dissertation, Politecnico di Torino).

[13] Williams, J. J., & Teal, T. K. (2017). A vision for collaborative training infrastructure for bioinformatics. Annals of the New York Academy of Sciences, 1387(1), 54-60. DOI: https://doi.org/10.1111/nyas.13207

[14] Seghier, M. L. (2025). AI-powered peer review needs human supervision. Journal of Information, Communication and Ethics in Society, 23(1), 104-116. DOI: https://doi.org/10.1108/JICES-09-2024-0132

[15] Karmaker, S. K., Hassan, M. M., Smith, M. J., Xu, L., Zhai, C., & Veeramachaneni, K. (2021). Automl to date and beyond: Challenges and opportunities. Acm computing surveys (csur), 54(8), 1-36. DOI: https://doi.org/10.1145/3470918

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Published

2025-05-13

How to Cite

Nikhil Sagar Miriyala, Bharath Kumar Bandaru, Prakhar Mittal, Kiran Babu Macha, Rishi Venkat, & Anu Rai. (2025). An Efficient Solution towards SDLC Automation using Multi-Agent Integration through Crew AI. International Journal of Computational and Experimental Science and Engineering, 11(2). https://doi.org/10.22399/ijcesen.2384

Issue

Vol. 11 No. 2 (2025): IJCESEN

Section

Research Article

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Copyright (c) 2025 International Journal of Computational and Experimental Science and Engineering

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