Agentic AIOps for Data-Intensive Hospitality and Agri Platforms
Keywords:
Generative AI Workflows, LLM-Based Automation, Compositional Network Architectures, Multi-Enterprise Platforms, Autonomous Task Planning, Data-Centric AI Systems, AIOps-Driven Operations, DataOps Foundations, Active Learning Engines, Intelligent Workflow Orchestration.Abstract
Recent developments in Generative Artificial Intelligence (GAI) have enabled large language models (LLMs) to be trained on domain- and task-specific corpora. The capabilities offered by GAI foundations support autonomous, agile, and compositional approaches to data-intensive tasks, while also opening new avenues for creativity in generating content for specific use cases. However, the application of Generative AI to the operation of complex, data-intensive systems — such as hospitality services and AgriTech platforms — remains limited. This work proposes a joint-use, compositional-network architecture that enables the automatic generation and execution of GAI-prompted task plans across multi-enterprise hospitality and AgriTech platforms. We explore two-way, GAI-enabled workflow automation and autonomic technology adoption within accumulate-and-release PaaS joint clouds, aiming to create further business value by applying AIOps concepts to two-sided flows of data, process, and business capability. A central challenge is the smart orchestration of parallel workflow automation, supported by emerging foundations in DataOps, Data-Centric AI, AIOps, and Platform Engineering. Achieving reliable data availability and quality requires focused data stewardship across both data consumers and providers, with Services, Data, and Firewall layers synchronized in a mirror-image architecture.
The emergence of modern distributed processing engines makes it feasible to operationalize Active Learner-based Decision Engine architectures. While GAI applications are sensitive to prompt quality, they can still perform well even when domain-specific corpora are sparse, since the learning objective is indirect rather than direct. Recent GAI advances have made it possible to rely on simpler teaching prompts, and data summarization techniques help generate accurate question-answer pairs for actively managed learning engines. Fully autonomous, self-optimizing run-book automation remains an aspiration rather than a current reality, largely due to the scarcity of suitable near-real-time training-support data. In this context, virtual tutors built on generic, prompt-based "teaching-by-example" approaches enable rapid testing through near-real-time output summarization. GAI systems thus act as active learners for Data-Centric AI, augment or replace existing query capabilities for event-based processing, and simplify the authoring of autonomic run-book content.
References
1. Yang, J., & Chew, E. (2021). A systematic review for service humanoid robotics model in hospitality. International Journal of Social Robotics, 13, 1397–1410.
2. Doborjeh, Z., Hemmington, N., Doborjeh, M., & Kasabov, N. (2022). Artificial intelligence: A systematic review of methods and applications in hospitality and tourism. International Journal of Contemporary Hospitality Management, 34(3), 1154–1176.
3. Pillai, R., Sivathanu, B., & Dwivedi, Y. K. (2022). Shopping intention at AI-powered automated retail stores: A post-COVID-19 pandemic perspective. Journal of Retailing and Consumer Services, 67, 102810.
4. Buhalis, D., O’Connor, P., & Leung, R. (2023). Smart hospitality: From smart cities and smart tourism towards agile business ecosystems in networked destinations. International Journal of Contemporary Hospitality Management, 35(1), 369–393.
5. MANGALAMPALLI, B. M., KOLLA, S. H., APPA RAO NAGUBANDI, D. R., & SEGIREDDY, A. R. (2025). AN INTELLIGENT, REAL-TIME DIGITAL FABRIC FOR HEALTHCARE AND FINANCIAL ECOSYSTEMS USING AUTONOMOUS LEARNING AND GENERATIVE SYSTEMS. TPM–Testing, Psychometrics, Methodology in Applied Psychology, 32(S9 (2025): Posted 15 December), 3070-3086.
6. Choudhury, A. H., & Datta, B. (2024). Artificial intelligence through the lens of hospitality employees: A systematic review. International Journal of Hospitality Management, 119, 103986.
7. Kong, H., Wang, K., Qiu, X., Cheung, C., & Bu, N. (2023). 30 years of artificial intelligence (AI) research relating to the hospitality and tourism industry. International Journal of Hospitality Management, 113, 103527.
8. Ivanov, S., Webster, C., & Berezina, K. (2022). Robots in tourism and hospitality: A research agenda. Tourism Management Perspectives, 41, 100959.
9. Filieri, R., Raguseo, E., & Vitari, C. (2022). What moderates the influence of online reviews on consumers’ hotel booking intentions? Tourism Management, 88, 104431.
10. Gong, Y., & Schroeder, A. (2022). A systematic literature review of data privacy and security research on smart tourism. Tourism Management Perspectives, 44, 101019.
11. Mariani, M., Perez-Vega, R., & Wirtz, J. (2022). AI in marketing, consumer research and psychology: A systematic literature review and research agenda. Psychology & Marketing, 39(4), 755–776.
12. Spanaki, K., Sivarajah, U., Fakhimi, M., Despoudi, S., & Irani, Z. (2021). Disruptive technologies in agricultural operations: A systematic review of AI-driven AgriTech research. Annals of Operations Research, 308, 1–29.
13. Astill, J., Dara, R. A., Campbell, M., Farber, J. M., Fraser, E. D. G., Sharif, S., & Yada, R. Y. (2020). Smart poultry management: Smart sensors, big data, and the Internet of Things. Computers and Electronics in Agriculture, 170, 105291.
14. Fountas, S., Mylonas, N., Malounas, I., Rodias, E., Hellmann, A., & Pekkeriet, E. (2019). Agricultural robotics for field operations. Sensors, 20(1), 267.
15. Ayaz, M., Ammad-Uddin, M., Sharif, Z., Mansour, A., & Aggoune, E. H. M. (2019). Internet-of-Things (IoT)-based smart agriculture: Toward making the fields talk. IEEE Access, 7, 129551–129583.
16. Elijah, O., Rahman, T. A., Orikumhi, I., Leow, C. Y., & Hindia, M. N. (2018). An overview of Internet of Things (IoT) and data analytics in agriculture: Benefits and challenges. IEEE Internet of Things Journal, 5(5), 3758–3773.
17. Mangalampalli, B. M., Bandi, V. D. V. K., Kolla, S. K., & Kumar, M. V. K. (2025). Towards Self-Evolving Healthcare Intelligence: Integrating Advanced Learning Systems with Real-Time Clinical Data Pipelines. Cultura: International Journal of Philosophy of Culture and Axiology, 22(12s), 464-486.
18. Talaviya, T., Shah, D., Patel, N., & Yagnik, H. (2020). Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture, 4, 58–73.
19. Khanna, A., & Kaur, S. (2020). Evolution of Internet of Things (IoT) and its significant impact in the field of precision agriculture. Computers and Electronics in Agriculture, 157, 218–231.
20. Ramesh, D., & Vardhan, B. V. (2023). Smart farming using artificial intelligence: A review. Engineering Applications of Artificial Intelligence, 120, 105899.
21. Farooq, M. S., Riaz, S., Abid, A., Abid, K., & Naeem, M. A. (2022). A survey on smart farming data, applications and techniques. Computers in Industry, 138, 103624.
22. Subeesh, A., & Mehta, C. R. (2021). Automation and digitization of agriculture using artificial intelligence and Internet of Things. Artificial Intelligence in Agriculture, 5, 278–291.
23. Li, J., Xu, M., Xiang, L., Chen, D., Zhuang, W., Yin, X., & Li, Z. (2024). Foundation models in smart agriculture: Basics, opportunities, and challenges. Computers and Electronics in Agriculture, 222, 109032.
24. Mangalampalli, B. M., Kolla, S. K., Bandi, V. D. V. K., Yandamuri, U. S., & Rani, P. S. (2025). Designing Intelligent Healthcare Ecosystems through Adaptive Data Integration and Autonomous Learning Systems. Vascular and Endovascular Review, 8(20s), 330-347.
25. Fountas, S., Espejo-García, B., Kypraios, I., & Sarri, D. (2024). Unleashing the potential of IoT, artificial intelligence, and UAVs in contemporary agriculture: A comprehensive review. Journal of Terramechanics, 115, 100986.
26. Li, L., Zhang, S., & Zhang, B. (2023). Recent advancements and challenges of AIoT application in smart agriculture: A review. Sensors, 23(5), 2383.
27. Li, X., et al. (2025). Foundation models in agriculture: A comprehensive review. Agriculture, 15(8), 847.
28. Schmidt, F., et al. (2020). Towards AIOps in edge computing environments. In Proceedings of the 2020 IEEE International Conference on Big Data (Big Data) (pp. 1276–1283). IEEE.
29. Wang, Y., Wang, Z., Xie, Z., Zhao, N., Chen, J., Zhang, W., Sui, K., & Pei, D. (2020). Practical and white-box anomaly detection through unsupervised and active learning. In Proceedings of the 2020 28th International Conference on Computer Communications and Networks. IEEE.
30. Meng, Y., Zhang, S., Sun, Y., Zhang, R., Hu, Z., Zhang, Y., Jia, C., Wang, Z., & Pei, D. (2020). Localizing failure root causes in a microservice through causality inference. In Proceedings of the 2020 IEEE/ACM International Symposium on Quality of Service. IEEE.
31. Kreuzberger, D., Kühl, N., & Hirschl, S. (2023). Machine learning operations (MLOps): Overview, definition, and architecture. Applied Sciences, 13(3), 1497.
32. Mangala, N., & Kolla, S. H. (2025). Real-Time Feature Engineering for Streaming AI Workloads Using PySpark and Azure Event Hubs. International Journal of Multidisciplinary Research in Science, Engineering, Technology & Management, 1(6), 93-105.
33. Diaz-de-Arcaya, J., Torre-Bastida, A. I., Zárate, G., Miñón, R., & Almeida, A. (2024). A joint study of the challenges, opportunities, and roadmap of MLOps and AIOps: A systematic survey. ACM Computing Surveys, 56(4), Article 84.
34. Zhang, L., Jia, T., Jia, M., Wu, Y., Liu, A., Yang, Y., Wu, Z., Hu, X., Yu, P. S., & Li, Y. (2025). A survey of AIOps in the era of large language models. ACM Computing Surveys, 58(2), Article 44.
Additional Files
Published
Data Availability Statement
The data supporting the findings of this study are included within the article.
Issue
Section
License
Copyright (c) 2026 Yannis Ioannidis (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). Authors retain full copyright of their published work. Under this license, others are free to share, copy, distribute, transmit, remix, transform, and build upon the published work for any purpose, including commercial use, provided that appropriate credit is given to the original authors, a link to the license is provided, and any changes made are clearly indicated. No additional restrictions may be applied that limit others from doing anything the license permits. All published articles are freely and permanently accessible online to readers worldwide without any subscription or access fees.
License URL: https://creativecommons.org/licenses/by/4.0/
