Agentic AI Orchestration for Cloud-Native Enterprise Economics
DOI:
https://doi.org/10.5281/tdqhhj23Keywords:
Cloud-Native Financial DevOps, Agentic FinOps Systems, Autonomous Financial Operations, Risk-Based Reliability Engineering, AI-Driven FinOps Analytics, Cloud Cost Optimization, Financial Governance Frameworks, Data-Driven Decision Systems, DevOps Risk Management, Enterprise FinOps Architecture.Abstract
Modern cloud-native Financial DevOps must fully harness the predictive and optimizing power of data and analytics. Realizing this requires organizations to build sufficient, appropriately specialized expertise to implement Financial DevOps practices that are agentic, autonomous, and orchestrated in support of senior business executives — while maintaining strong observability and risk management. These capabilities enable a shift away from traditional budgeting cycles and static resource-allocation models toward cloud-native, consumption-based FinOps at the enterprise level. To manage the operational risk introduced by autonomy, we propose a Risk-based Reliability Engineering framework for quantifying and mitigating this risk. Ethical, legal, and governance considerations further shape the role of an agentic fiduciary office, responsible for ensuring that design and implementation remain balanced and are supported by adequately specialized expertise.
While technical stakeholders in cloud-native FinOps teams naturally gravitate toward Cloud FinOps principles and processes, organizations benefit when other stakeholders take on distinct, business-facing responsibilities within Agentic AI-Driven Autonomous Financial DevOps. This division allows business and technical teams to communicate more effectively around financial topics. Achieving seamless Cloud Financial Operations (FinOps) delivery depends on several interlocking capabilities: robust Data Governance, Security, and Compliance; sustained attention to Data Quality; close alignment between the Cloud FinOps technical stack and data analytics/decision-making tools; a clear decision-making framework suited to data-driven digital business operations; integration of Risk-based Reliability Engineering within DevOps; and the adoption of Agentic AI-Driven Autonomous Financial DevOps itself.
References
1. Ahmad, S. G., Iqbal, T., Munir, E. U., & Ramzan, N. (2023). Cost optimization in cloud environment based on task deadline. Journal of Cloud Computing, 12, 9.
2. Cao, L., Huo, T., Li, S., Zhang, X., Chen, Y., Lin, G., Wu, F., Ling, Y., Zhou, Y., & Xie, Q. (2024). Cost optimization in edge computing: A survey. Artificial Intelligence Review, 57, 312.
3. Krishna AzithTejaGanti, V., Senthilkumar, K. P., Robinson L, T., Karunakaran, S., Pandugula, C., & Khatana, K. (2024, November). Energy-Efficient Real-Time Hybrid Deep Learning Framework for Adaptive Iot Intrusion Detection with Scalable and Dynamic Threat Mitigation. In Proceedings of the 3rd International Conference on Optimization Techniques in the Field of Engineering (ICOFE-2024).
4. Chen, W., Su, Y., Zuo, J., Yang, C., Yuan, C., Chan, C.-M., Yu, H., Lu, Y., Hung, Y.-H., Qian, C., Qin, Y., Cong, X., Xie, R., Liu, Z., Sun, M., & Zhou, J. (2024). AgentVerse: Facilitating multi-agent collaboration and exploring emergent behaviors. International Conference on Learning Representations.
5. Hassan, H. B., Barakat, S. A., & Sarhan, Q. I. (2021). Survey on serverless computing. Journal of Cloud Computing, 10, 39.
6. Jimenez, C. E., Yang, J., Wettig, A., Yao, S., Pei, K., Press, O., & Narasimhan, K. R. (2024). SWE-bench: Can language models resolve real-world GitHub issues? International Conference on Learning Representations.
7. Shyamala Anto Mary, P., Kalisetty, S., & Mandala, V. M. (2024, November). Advancing IoT Data Forecasting with Deep Learning Framework for Resilience Scalability and Real-World Applications. In Proceedings of the 3rd International Conference on Optimization Techniques in the Field of Engineering (ICOFE-2024).
8. Khan, A. Q., Matskin, M., Prodan, R., Bussler, C., Roman, D., & Soylu, A. (2024). Cost modelling and optimisation for cloud: A graph-based approach. Journal of Cloud Computing, 13, 147.
9. Khattab, O., Singhvi, A., Maheshwari, P., Zhang, Z., Santhanam, K., Vardhamanan, S., Haq, S., Sharma, A., Joshi, T. T., Moazam, H., Miller, H., Zaharia, M., & Potts, C. (2024). DSPy: Compiling declarative language model calls into self-improving pipelines. International Conference on Learning Representations.
10. Muthusamy, V., Rizk, Y., Kate, K., Venkateswaran, P., Isahagian, V., Gulati, A., & Dube, P. (2023). Towards large language model-based personal agents in the enterprise: Current trends and open problems. Findings of the Association for Computational Linguistics: EMNLP 2023, 6909–6921.
11. Osypanka, P., & Nawrocki, P. (2022). Resource usage cost optimization in cloud computing using machine learning. IEEE Transactions on Cloud Computing.
12. Pamisetty, A. (2024). Intelligent Supply Chain Optimization: AI Driven Data Synchronization and Decision Making for Modern Logistics. MSW Manag. J., 34(2), 804-817.
13. Qu, Z., Dawande, M., & Janakiraman, G. (2024). Technical note—Cloud cost optimization: Model, bounds, and asymptotics. Operations Research, 72(1), 132–150.
14. Qian, C., Liu, W., Liu, H., Chen, N., Dang, Y., Li, J., Yang, C., Chen, W., Su, Y., Cong, X., Xu, J., Li, D., Liu, Z., & Sun, M. (2024). ChatDev: Communicative agents for software development. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics, 15174–15186.
15. Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Hambro, E., Zettlemoyer, L., Cancedda, N., & Scialom, T. (2023). Toolformer: Language models can teach themselves to use tools. Advances in Neural Information Processing Systems.
16. Shafiei, H., Khonsari, A., & Mousavi, P. (2022). Serverless computing: A survey of opportunities, challenges, and applications. ACM Computing Surveys, 54(11s), 1–32.
17. Stupar, I., & Huljenic, D. (2023). Model-based cloud service deployment optimisation method for minimisation of application service operational cost. Journal of Cloud Computing, 12, 23.
18. Wang, L., Ma, C., Feng, X., Zhang, Z., Yang, H., Zhang, J., Chen, Z., Tang, J., Chen, X., Lin, Y., Zhao, W. X., Wei, Z., & Wen, J. (2024). A survey on large language model based autonomous agents. Frontiers of Computer Science, 18, 186345.
19. Danda, R. R., Yasmeen, Z., & Maguluri, K. K. (2020). AI-driven healthcare transformation: Machine learning, deep learning, and neural networks in insurance and wellness programs. JEC PUBLICATION.
20. Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q. V., & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35, 24824–24837.
21. Xie, T., Zhang, D., Chen, J., Li, X., Zhao, S., Cao, R., Hua, T. J., Cheng, Z., Shin, D., Lei, F., Liu, Y., Xu, Y., Zhou, S., Savarese, S., Xiong, C., Zhong, V., & Yu, T. (2024). OSWorld: Benchmarking multimodal agents for open-ended tasks in real computer environments. Advances in Neural Information Processing Systems, 37.
22. Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2023). ReAct: Synergizing reasoning and acting in language models. International Conference on Learning Representations.
23. Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., & Narasimhan, K. (2023). Tree of thoughts: Deliberate problem solving with large language models. Advances in Neural Information Processing Systems, 36.
24. Wu, Q., Bansal, G., Zhang, J., Wu, Y., Li, B., Zhu, E., Jiang, L., Zhang, X., Zhang, S., Awadallah, A., White, R. W., Burger, D., & Wang, C. (2024). AutoGen: Enabling next-gen LLM applications via multi-agent conversation. Proceedings of the Conference on Language Modeling.
25. Hong, S., Zhuge, M., Chen, J., Zheng, X., Cheng, Y., Zhang, C., Wang, J., Wang, Z., Yau, S. K. S., Lin, Z., Zhou, L., Ran, C., Xiao, B., Wu, J., & Schmidhuber, J. (2024). MetaGPT: Meta programming for multi-agent collaborative framework. International Conference on Learning Representations.
26. Deng, Y., Zhang, W., Zheng, Z., Chen, J., & Gu, X. (2024). Mind2Web: Towards a generalist agent for the web. Advances in Neural Information Processing Systems.
27. Pamisetty, V. (2024). Transforming taxation systems through predictive analytics and AI-driven compliance monitoring tools. Am Data Sci J Adv Comput, 3, 55-68.
28. Patil, S. G., Zhang, T., Wang, X., & Gonzalez, J. E. (2023). Gorilla: Large language model connected with massive APIs. Advances in Neural Information Processing Systems.
29. Qin, Y., Liang, S., Ye, Y., Zhu, K., Yan, L., Lu, Y., Lin, Y., Cong, X., Tang, Y., Qian, Z., Wang, S., Hong, R., & Che, W. (2024). ToolLLM: Facilitating large language models to master 16000+ real-world APIs. International Conference on Learning Representations.
30. Nakano, R., Hilton, J., Hesse, C., Schulman, J., & others. (2021). WebGPT: Browser-assisted question-answering with human feedback. arXiv preprint arXiv:2112.09332.
31. Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Hambro, E., Zettlemoyer, L., Cancedda, N., & Scialom, T. (2023). Toolformer: Language models can teach themselves to use tools. Advances in Neural Information Processing Systems, 36.
32. Ruan, J., Chen, Y., Zhang, B., Xu, Z., Bao, T., Du, G., Shi, S., Mao, H., Zeng, X., & Zhao, R. (2023). TPTU: Task planning and tool usage of large language model-based AI agents. Proceedings of the International Conference on Artificial Intelligence and Statistics.
33. Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., Zhang, M., Wang, J., Jin, S., Zhou, E., Zheng, R., Fan, X., Wang, X., Xia, L., & others. (2023). The rise and potential of large language model based agents: A survey. arXiv preprint arXiv:2309.07864.
34. Wang, Z., Ma, X., Xu, X., Feng, J., Li, Z., & others. (2023). Voyager: An open-ended embodied agent with large language models. Transactions on Machine Learning Research.
35. Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2023). ReAct: Synergizing reasoning and acting in language models. Proceedings of the International Conference on Learning Representations.
36. Qu, Z., Dawande, M., & Janakiraman, G. (2023). Cloud cost optimization: Model, bounds, and asymptotics. Operations Research.
37. Ahmad, S. G., Iqbal, T., Munir, E. U., & Ramzan, N. (2023). Cost optimization in cloud environment based on task deadline. Journal of Cloud Computing, 12, 9.
38. Castro, H., Harry, O., Olaoluwa, A., & Lagasca, L. (2024). Cloud cost optimization using AI and machine learning. Proceedings/Research publication.
39. Varadarajan, M. N., Rajkumar, N., Viji, C., & Mohanraj, A. (2024). AI-powered financial operation strategy for cloud computing cost optimization for future. Salud, Ciencia y Tecnología—Serie de Conferencias.
40. Osypanka, P., & Nawrocki, P. (2022). Resource usage cost optimization in cloud computing using machine learning. IEEE Transactions on Cloud Computing.
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Data Availability Statement
The datasets used in this study are the Azure Cost Management Logs (Microsoft Azure Enterprise Portal), the FinOps Foundation Benchmark (finops.org), and a DevOps Risk Event Log (internal synthetic data combined with public GitHub datasets). The synthetic risk event log is available from the corresponding author upon reasonable request.
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