A Novel AI-Native Architecture for Enterprise Angular Using LLM-Orchestrated Signal Reactivity and State Isolation
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V3I3P116Keywords:
AI-Native Architecture, Angular Signals, LLM Orchestration, State Isolation, Enterprise Angular, Signal-Based Reactivity, Large Language Models, Full-Stack Architecture, Generative AI, Microservices, Reactive Programming, Scalable Web Applications, State Management, Enterprise Software EngineeringAbstract
The frontend applications of modern enterprises are under greater and greater pressure as the user interface is highly dynamic, microservice distribution is used, as well as the need to display real-time responsiveness. Angular causes of traditional Angular that commonly employ reactive programming implemented by RXJS and a centralized state management package like NgRx typically experience issues such as tight state and scalability sinks with regards to developers. In addition, the methods are not intelligent in their adaptability and are thus not easily capable of optimising the behaviour of the UI dynamically in response to changing user interactions, contextual cues, and system state. The paper comes up with a new AI-native architecture of enterprise-scale Angular applications which combines the Large Language Model (LLM)-orchestration with a signal-based reactive model and an effective state isolation approach. The architecture permits adaptive and context-sensitive UI behaviors and efficient state transitions by using the Angular Signals to provide dependency-aware fine-grained updates and providing the intelligence of an LLM orchestration-layered level of intelligent decision-making. Experimental analysis shows that it has better rendering performance, lower latency, greater state consistency and higher productivity by developers. The suggested solution would mark a new trend in the paradigm of intelligent, scalable, and self-optimizing frontend systems and would be a step towards the development of AI-sensitive software architecture.
References
[1] George, O. (2019). Reactive state management in Angular applications. International Journal of Trend in Scientific Research and Development, 5(3), 1200–1205.
[2] Moritz, P., Nishihara, R., Wang, S., Tumanov, A., Liaw, R., Liang, E., ... & Stoica, I. (2018). Ray: A distributed framework for emerging {AI} applications. In 13th USENIX symposium on operating systems design and implementation (OSDI 18) (pp. 561-577).
[3] Austin, J., Odena, A., Nye, M., Bosma, M., Michalewski, H., Dohan, D., ... & Sutton, C. (2021). Program synthesis with large language models. arXiv preprint arXiv:2108.07732.
[4] Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. D. O., Kaplan, J., ... & Zaremba, W. (2021). Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374.
[5] Allamanis, M., Barr, E. T., Devanbu, P., & Sutton, C. (2018). A survey of machine learning for big code and naturalness. ACM Computing Surveys (CSUR), 51(4), 1-37.
[6] Nguyen, T. T., Vu, P. M., Pham, H. V., & Nguyen, T. T. (2018, May). Deep learning UI design patterns of mobile apps. In Proceedings of the 40th International Conference on Software Engineering: New Ideas and Emerging Results (pp. 65-68).
[7] Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., & Ng, R. (2021). Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1), 99-106.
[8] Tewari, A., Fried, O., Thies, J., Sitzmann, V., Lombardi, S., Sunkavalli, K., ... & Zollhöfer, M. (2020, May). State of the art on neural rendering. In Computer graphics forum (Vol. 39, No. 2, pp. 701-727).
[9] Shivakumar, S. K. (2020). Modern web performance optimization. Methods, Tools, and Patterns to Speed Up Digital Platforms.
[10] Tilkov, S., & Vinoski, S. (2010). Node. js: Using JavaScript to build high-performance network programs. IEEE Internet Computing, 14(6), 80-83.
[11] Ahlemann, F., Stettiner, E., Messerschmidt, M., & Legner, C. (Eds.). (2012). Strategic enterprise architecture management: challenges, best practices, and future developments. Springer Science & Business Media.
[12] Cincovic, J., Delcev, S., & Draskovic, D. (2019). Architecture of web applications based on Angular Framework: A Case Study. methodology, 7(7), 254-259.
[13] Moiseev, A., & Fain, Y. (2018). Angular Development with TypeScript. Simon and Schuster.
[14] Nurkiewicz, T., & Christensen, B. (2016). Reactive programming with RxJava: creating asynchronous, event-based applications. " O'Reilly Media, Inc.".
[15] Wei-Liang, T., & Mei Ling, C. (2019). Reactive Programming in Practice: Unlocking the Power of RxJS and NgRx in Modern Web Applications. International Journal of Trend in Scientific Research and Development, 3(4), 1925-1940.
[16] Hu, H., Wen, Y., Chua, T. S., & Li, X. (2014). Toward scalable systems for big data analytics: A technology tutorial. IEEE access, 2, 652-687.
[17] Shivakumar, S. K. (2014). Architecting high performing, scalable and available enterprise web applications. Morgan Kaufmann.
[18] Li, X., Eckert, M., Martinez, J. F., & Rubio, G. (2015). Context aware middleware architectures: Survey and challenges. Sensors, 15(8), 20570-20607.
[19] Bicocchi, N., Fontana, D., & Zambonelli, F. (2014, June). A self-aware, reconfigurable architecture for context awareness. In 2014 IEEE Symposium on Computers and Communications (ISCC) (pp. 1-7). IEEE.
[20] Limam, S., & Belalem, G. (2016). A self-adaptive conflict resolution with flexible consistency guarantee in the cloud computing. Multiagent and Grid Systems, 12(3), 217-238.
[21] Humphreys, M. (2005). Natural resources, conflict, and conflict resolution: Uncovering the mechanisms. Journal of conflict resolution, 49(4), 508-537.
[22] Moore, A. W., Hebert, M., & Shaneman, S. (2018, May). The AI stack: a blueprint for developing and deploying artificial intelligence. In Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX (Vol. 10635, pp. 45-54). SPIE.
[23] Kleppmann, M. (2017). Designing data-intensive applications: The big ideas behind reliable, scalable, and maintainable systems. " O'Reilly Media, Inc.".
[24] Haase, D., Larondelle, N., Andersson, E., Artmann, M., Borgström, S., Breuste, J., ... & Elmqvist, T. (2014). A quantitative review of urban ecosystem service assessments: concepts, models, and implementation. Ambio, 43(4), 413-433.
[25] Peltonen, S., Mezzalira, L., & Taibi, D. (2021). Motivations, benefits, and issues for adopting micro-frontends: A multivocal literature review. Information and Software Technology, 136, 106571.
[26] Chennareddy, R. K. (2020). Engineering Intelligence Systems Using Big Data and Cloud Architectures for Modern Data Intensive Applications. International Journal of AI, BigData, Computational and Management Studies, 1(2), 41-50.
[27] Chennareddy, R. K. (2021). Designing Data and Analytics Ecosystems for High Volume Transaction Processing Applications. International Journal of AI, BigData, Computational and Management Studies, 2(2), 95-106.
