Integrating Generative AI and Agentic Systems into Enterprise Cloud Architecture: Frameworks and Governance Models
Main Article Content
Abstract
Artificial intelligence (AI) is an incredibly fast-developing field, characterized by the creation of generative models and agentic systems that are able to independently complete complicated tasks, create content and aid in decision-making processes. Although a lot of recent talk about generative AI has picked up since 2020, governance principles, architectural patterns, and fundamental rules have been established beforehand. In this paper, a thorough model is proposed to deploy generative AI and agentic systems in enterprise cloud architecture, based on the research and industry practices prior to 2020. It provides an overview of architectural layers, data pipelines, orchestration patterns and governance paradigms required to deploy scalable and secure. The research also delves into issues of data privacy, model interpretability, operational risk, and compliance. This paper aims to integrate the key insights from previous studies in cloud computing systems, machine learning systems, and autonomous agents to offer a well-defined strategy for enterprises in developing AI-based cloud computing architectures, with a strong business control intention. In this paper, the authors attempt to combine the prominent findings of the aforementioned bodies of literature related to cloud computing systems, machine learning systems and autonomous agents to offer an structured strategy for the enterprises searching for methods adopting AI-based cloud architectures while keeping robust business control intention.
Article Details
Section
How to Cite
References
1. Al-Shabandar, R., Lightbody, G., Browne, F., Liu, J., Wang, H. and Zheng, H., 2019, October. The application of artificial intelligence in financial compliance management. In Proceedings of the 2019 international conference on artificial intelligence and advanced manufacturing (pp. 1-6). https://dl.acm.org/doi/pdf/10.1145/3358331.3358339
2. Arora, A., 2017. Evaluating Ethical Challenges in Generative AI Development and Responsible Usage Guidelines. INTERNATIONAL JOURNAL OF RESEARCH IN ELECTRONICS AND COMPUTER ENGINEERING. https://www.academia.edu/download/122909992/Title_16_AA_Oct_17_.pdf
3. Balaganski, A., 2015. API Security Management. KuppingerCole Report, 70958, pp.20-27. https://cpl.thalesgroup.com/sites/default/files/content/analyst_research/Leadership-Compass-API-Security-and-Management-25.pdf
4. Benlian, A., Kettinger, W.J., Sunyaev, A., Winkler, T.J. and Guest Editors, 2018. The transformative value of cloud computing: a decoupling, platformization, and recombination theoretical framework. Journal of management information systems, 35(3), pp.719-739. https://www.jmis-web.org/articles/1394
5. Ferreira, L., Putnik, G., Cunha, M.M.C., Putnik, Z., Castro, H., Alves, C., Shah, V. and Varela, L., 2017. A cloud-based architecture with embedded pragmatics renderer for ubiquitous and cloud manufacturing. International Journal of Computer Integrated Manufacturing, 30(4-5), pp.483-500. https://ciencipca.ipca.pt/bitstreams/34e8e463-d801-4769-a371-7c4a2065bb40/download
6. Gelev, S., 2017. Requirements for next generation business transformation and their implementation in 5G architecture. International Journal of Computer Applications. https://www.academia.edu/download/91823956/IJCA_20CRC_20_20-_20Requirements_20for_20next_20generation_20business_20transformation_20and_20their_20implementation_20in_205G_20archit.pdf
7. Henfridsson, O. and Bygstad, B., 2013. The Generative Mechanisms of Digital Infrastructure Evolution1. MIS quarterly, 37(3), pp.907-931. https://www.researchgate.net/profile/Ola-Henfridsson/publication/285924538_The_Generative_Mechanisms_of_Digital_Infrastructure_Evolution/links/6730e6d92326b47637d6f2ed/The-Generative-Mechanisms-of-Digital-Infrastructure-Evolution.pdf
8. Iqbal, J. and Saleh, A., 2020. ARTIFICIAL INTELLIGENCE–DRIVEN DECISION SUPPORT SYSTEMS FOR SMART ENTERPRISES. International Research Journal of Advanced Science, 1(1), pp.1-11. https://irjas.com/index.php/sciencejournal/article/download/49/43
9. Kant, K., 2015. AI and ML in Predictive Consumer Analytics: A Conceptual Model for Personalized Marketing. https://www.researchgate.net/profile/Kamal-Kant-5/publication/398814585_AI_and_ML_in_Predictive_Consumer_Analytics_A_Conceptual_Model_for_Personalized_Marketing/links/6943d0c027359023a00db933/AI-and-ML-in-Predictive-Consumer-Analytics-A-Conceptual-Model-for-Personalized-Marketing.pdf
10. Kaur, H., 2020. Building Smart Applications: A Guide to Integrating AI and Machine Learning into Salesforce. https://www.researchgate.net/profile/John-Mathew-26/publication/396262619_Building_Smart_Applications_A_Guide_to_Integrating_AI_and_Machine_Learning_into_Salesforce/links/68e4c9ac02d6215259b9c52d/Building-Smart-Applications-A-Guide-to-Integrating-AI-and-Machine-Learning-into-Salesforce.pdf
11. Kop, M., 2019. AI & intellectual property: Towards an articulated public domain. Tex. Intell. Prop. LJ, 28, p.297. https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3409715
12. Lauterbach, A. and Bonime-Blanc, A., 2018. The artificial intelligence imperative: a practical roadmap for business. Bloomsbury Publishing USA. https://books.google.com/books?hl=en&lr=&id=_o3DEAAAQBAJ&oi=fnd&pg=PP1&dq=INTEGRATING+GENERATIVE+AI+AND+AGENTIC+SYSTEMS+INTO+ENTERPRISE+CLOUD+ARCHITECTURE:+FRAMEWORKS+AND+GOVERNANCE+MODELS&ots=2wkguTItnP&sig=RNrbpr4oCLi48IE1IHK9Z2rnaSM
13. Lauterbach, A., 2019. Artificial intelligence and policy: quo vadis?. Digital Policy, Regulation and Governance, 21(3), pp.238-263. https://www.emerald.com/dprg/article-pdf/21/3/238/580746/dprg-09-2018-0054.pdf
14. Nippatla, R.P., 2018. AI-Driven Cloud BI: Enhancing Predictive Analytics for Financial Insights. International Journal of Marketing Management. https://www.academia.edu/download/123203417/AI_Driven_Cloud_BI_Enhancing_Predictive_Analytics_for_Financial_Insights.pdf
15. Pantazis, E. and Gerber, D., 2018. A framework for generating and evaluating façade designs using a multi-agent system approach. International Journal of Architectural Computing, 16(4), pp.248-270. https://www.academia.edu/download/57885578/19_181128_IJAC_journal_01_PantazisGerber_published_version.pdf
16. Renda, A., 2019. Artificial Intelligence. Ethics, governance and policy challenges. CEPS Centre for European Policy Studies. https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3420810
17. Thomas, P., 2020. Multi-Agent Generative AI Frameworks for Adaptive Human–Machine Collaboration. https://www.researchgate.net/profile/Phillip-Thomas-14/publication/403113198_Multi-Agent_Generative_AI_Frameworks_for_Adaptive_Human-_Machine_Collaboration/links/69c362c260c0371a60eeaa3b/Multi-Agent-Generative-AI-Frameworks-for-Adaptive-Human-Machine-Collaboration.pdf
18. Wareham, J., Fox, P.B. and Cano Giner, J.L., 2014. Technology ecosystem governance. Organization science, 25(4), pp.1195-1215. https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=2201688