A Cloud-Native Framework for RAG-Driven Enterprise Automation
Keywords:
Cloud-Native AI Orchestration, Enterprise AI Frameworks, LLM-Based Foundations, Retrieval-Augmented Generation, Enterprise AIGC Systems, AI Workflow Automation, High-Availability AI Platforms, Cloud-Native AI Infrastructure, AI Product Lifecycle Management, Intelligent Enterprise Operations.Abstract
The advent of new LLM technologies has opened new growth opportunities for enterprise AI, with capabilities such as LLM-based foundation models, Retrieval-Augmented Generation (RAG), and enterprise AI-generated content (AIGC) at the forefront. Cloud-Native AI Orchestration integrates these technology foundations into the broader enterprise ecosystem. Building on this, AgiAI provides an AI Orchestrator — grounded in an enterprise AI orchestration model and framework — that supports an organization's overall business requirements using cloud-native technology. This Enterprise AI Orchestration approach supports the entire AI business process of an enterprise while remaining simple, open, highly available, capable of parent-child computing, reusable, and aligned with the full process lifecycle.
The rapid advancement of AI and LLM technology has significantly impacted industries such as consulting, education, and manufacturing. Capabilities including foundation model readiness, Retrieval-Augmented Generation, enterprise content generation, and other AI functions not only have the potential to substantially reduce human labor in these domains, but also create new opportunities for AI-driven business growth. While many cloud-native technologies enable the rapid development and delivery of Cloud-Native AI capabilities and products, AI product delivery is not simply a matter of deployment — it also requires accounting for the product lifecycle, evolving business requirements, and enterprise-wide resource operations. Cloud-Native Enterprise AI Orchestration Technology addresses this by providing a cloud-native AI infrastructure purpose-built for enterprise AI, enabling enterprise AI initiatives to be carried out simply and efficiently.
References
1. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-T., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems, 33, 9459–9474.
2. Guu, K., Lee, K., Tung, Z., Pasupat, P., & Chang, M.-W. (2020). REALM: Retrieval-augmented language model pre-training. Proceedings of the 37th International Conference on Machine Learning, 3929–3938.
3. Karpukhin, V., Oğuz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-T. (2020). Dense passage retrieval for open-domain question answering. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 6769–6781.
4. Botlagunta Preethish Nandan, (2020), "Advanced Testing Frameworks for Next - Generation Semiconductor Devices Using Machine Learning", International Journal of Science and Research (IJSR), 9(12), 1911-1920. https://dx.doi.org/10.21275/SR20125160704, https://www.ijsr.net/getabstract.php?paperid=SR20125160704
5. Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020). BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 7871–7880.
6. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., … Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901.
7. Syed, R., Suriadi, S., Adams, M., Bandara, W., Leemans, S. J. J., Ouyang, C., ter Hofstede, A. H. M., van de Weerd, I., Wynn, M. T., & Reijers, H. A. (2020). Robotic process automation: Contemporary themes and challenges. Computers in Industry, 115, 103162.
8. González-Enríquez, J., Jiménez-Ramírez, A., & Domínguez-Mayo, F. J. (2020). Robotic process automation: A scientific and industrial systematic mapping study. IEEE Access, 8, 39113–39129.
9. Chakraborti, T., Isahagian, V., Khalaf, R., Khazaeni, Y., Muthusamy, V., Rizk, Y., & Unuvar, M. (2020). From robotic process automation to intelligent process automation: Emerging trends. In Business Process Management Workshops (pp. 177–182). Springer.
10. Fan, C.-F., Jindal, A., & Gerndt, M. (2020). Microservices vs. serverless: A performance comparison on a cloud-native web application. Proceedings of the 10th International Conference on Cloud Computing and Services Science, 204–215.
11. Chakilam, C., Suura, S. R., Koppolu, H. K. R., & Recharla, M. (2022). From Data to Cure: Leveraging Artificial Intelligence and Big Data Analytics in Accelerating Disease Research and Treatment Development. Journal of Survey in Fisheries Sciences. https://doi.org/10.53555/sfs.v9i3.3619
12. Izacard, G., & Grave, E. (2021). Leveraging passage retrieval with generative models for open domain question answering. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, 874–880.
13. Borgeaud, S., Mensch, A., Hoffmann, J., Cai, T., Rutherford, E., Millican, K., van den Driessche, G., Lespiau, J.-B., Damoc, B., Clark, A., de Las Casas, D., Guy, A., Menick, J., Ring, R., Hennigan, T., Huang, S., Maggiore, L., Jones, C., Cassirer, A., … Sifre, L. (2021). Improving language models by retrieving from trillions of tokens. arXiv preprint arXiv:2112.04426.
14. Nakano, R., Hilton, J., Balaji, S., Wu, J., Ouyang, L., Kim, C., Hesse, C., Jain, S., Kosaraju, V., Jiang, X., Cobbe, K., Eloundou, T., Krueger, G., Button, K., Knight, M., Chess, B., & Schulman, J. (2021). WebGPT: Browser-assisted question-answering with human feedback. arXiv preprint arXiv:2112.09332.
15. Pramod, D. (2022). Robotic process automation for industry: Adoption status, benefits, challenges and research agenda. Benchmarking: An International Journal, 29(5), 1562–1586.
16. Connected Mobility Platforms and Their Impact on Urban Transport Planning and Service Design. (2019). IJIREEICE, 7(12). https://doi.org/10.17148/ijireeice.2019.71208
17. Hofmann, P., Samp, C., & Urbach, N. (2020). Robotic process automation. Electronic Markets, 30, 99–106.
18. Güner, E. O., Han, S., & Juell-Skielse, G. (2020). Robotic process automation as routine capability: A literature review. In Proceedings of the 28th European Conference on Information Systems.
19. Li, Z., Guo, L., Cheng, J., Chen, Q., He, B., & Guo, M. (2022). The serverless computing survey: A technical primer for design architecture. ACM Computing Surveys, 54(10s), Article 220.
20. Shafiei, H., Khonsari, A., & Mousavi, P. (2022). Serverless computing: A survey of opportunities, challenges, and applications. ACM Computing Surveys, 54(11s), Article 239.
21. Hewage, N., & Meedeniya, D. (2022). Machine learning operations: A survey on MLOps tool support. arXiv preprint arXiv:2202.10169.
22. Zhang, Y., Sun, S., Gao, X., Fang, Y., Brockett, C., Galley, M., Gao, J., & Dolan, B. (2022). RetGen: A joint framework for retrieval and grounded text generation modeling. Proceedings of the AAAI Conference on Artificial Intelligence, 36(10), 11739–11747.
23. Mashetty, S., & Mukesh, A. (2022). Application of Machine Learning in Loan Default Forecasting. International Journal of Artificial Intelligence, Data Science and Machine Learning, 3, 123–132. https://doi.org/10.63282/3050-9262.ijaidsml-v3i4p114
24. Izacard, G., Lewis, P., Lomeli, M., Hosseini, L., Petroni, F., Schick, T., Dwivedi-Yu, J., Joulin, A., Riedel, S., & Grave, E. (2022). Atlas: Few-shot learning with retrieval augmented language models. arXiv preprint arXiv:2208.03299.
25. Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2022). ReAct: Synergizing reasoning and acting in language models. arXiv preprint arXiv:2210.03629.
26. Min, S., Lyu, X., Holtzman, A., Artetxe, M., Lewis, M., Hajishirzi, H., & Zettlemoyer, L. (2022). Rethinking the role of demonstrations: What makes in-context learning work? Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 11048–11064.
27. Gerlings, J., & Constantiou, I. (2022). Machine learning in transaction monitoring: The prospect of explainable artificial intelligence. Proceedings of the European Conference on Information Systems.
28. Paleti, S. (2021). Cognitive core banking: A data-engineered, AI-infused architecture for proactive risk compliance management. AI-Infused Architecture for Proactive Risk Compliance Management (December 21, 2021).
29. Plattfaut, R., & Borghoff, V. (2022). Robotic process automation: A literature-based research agenda. Journal of Information Systems, 36(2), 173–191.
30. Tertychnyi, P., Godgildieva, M., Dumas, M., & Ollikainen, M. (2022). Time-aware and interpretable predictive monitoring system for anti-money laundering. Machine Learning with Applications, 8, 100306.
Additional Files
Published
Issue
Section
License
Copyright (c) 2023 European Advanced Journal for Science & Engineering (EAJSE)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Articles published in the European Advanced Journal for Science & Engineering (EAJSE) are made freely available online immediately upon publication under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits unrestricted use, distribution, and reproduction in any medium or format, provided the original work is properly cited. Authors retain copyright of their work. By submitting to EAJSE, authors grant the journal the right of first publication. For details, visit: https://creativecommons.org/licenses/by/4.0/
