Articles
Responsible AI Agents at Enterprise Scale: A Unified Framework for ITSM, HRSD, and CSM
Abstract
Generative AI is increasingly used to orchestrate or augment enterprise operations across internal IT service management (ITSM), human resources service delivery (HRSD), and customer service management (CSM). A growing body of literature discusses the foundations of responsible generative AI; however, no comprehensive framework supports scalable deployments across multiple domains or connects generative AI to enterprise automation. A formalized approach to architecting responsible generative AI agents capable of automating ITSM, HRSD, and CSM workflows has yet to emerge. A unified framework integrating these three domains will support enterprise automation in a responsible manner.
Traditionally, ITSM, HRSD, and CSM automation applications are siloed, with reliance on uniform templates and a narrow focus on simple question–answer pairs. Responsible generative AI systems offer the ability to promote efficiency and effectiveness across a wider spectrum of operational tasks. The reference architecture, supporting guidelines, and domain-specific considerations outlined here serve as a blueprint for building or integrating responsible generative AI capabilities. With expansion beyond single-use agents into holistic, managed enterprise deployments featuring prebuilt connectivity, the full potential of generative AI can be realized.
Article Information
Journal |
International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
|---|---|
Volume (Issue) |
Vol. 8 No. 4 (2025): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
DOI |
|
Pages |
16909-16922 |
Published |
July 11, 2025 |
| Copyright | |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Mallesham Goli, Dimas Saputra (2025). Responsible AI Agents at Enterprise Scale: A Unified Framework for ITSM, HRSD, and CSM. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 8 No. 4 (2025): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 16909-16922. https://doi.org/10.15662/IJAESIT.2025.0804005 |
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22. Kolla, S. K., & Reddy, V. A. R. (2024). Evaluating Cloud-Native vs. Hybrid Architectures for Health Benefit Administration Systems. International Journal of Medical Toxicology and Legal Medicine, 27(5), 1042-1053.
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26. Meda, R. (2024). Agentic AI in Multi-Tiered Paint Supply Chains: A Case Study on Efficiency and Responsiveness. Journal of Compu-tational Analysis and Applications (JoCAAA), 33(08), 3994-4015.
27. Avinash Pamisetty, Vijaya Rama Raju Gottimukkala. (2024). Agentic AI-Driven Multi-Cloud Big Data Architecture For Predictive Demand, Credit Risk, And Inventory Financing In National Food Service Supply Chains. Metallurgical and Materials Engineering, 30(4), 959–975. https://doi.org/10.63278/mme.v30i4.1933
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30. Yandamuri, U. S. (2023). An Intelligent Analytics Framework Combining Big Data and Machine Learning for Business Forecasting. International Journal Of Finance, 36(6), 682-706.
31. Kolla, S. K. (2024). Federated Machine Learning On Big Healthcare Data For Privacy-Preserving Analytics. The Review of Diabetic Studies, 175-190.
32. Mahadevan, S. (2024). Empowering Manufacturing: Generative AI Revolutionizes ERP Application. Int. J. Innov. Sci. Res, 9, 593-595.
33. Mattaparthi, R. (2023). Deep Learning-Driven Combustion Anomaly Detection in Diesel Powertrains: A Multi-Sensor Fusion Approach for Real-Time ECM Adaptation. International Journal of Intelligent Systems and Applications in Engineering, 11, 1084.
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39. Casas, P., & Román, C. (2024). The impact of artificial intelligence in the early retirement decision. Empirica, 51, 583–618.
40. Mahadevan, S. (2024). Clouding the Future: Innovating Towards Net-Zero Emissions. International Journal of Computing and Engineering, 6(2), 17-23.
41. Kofman, P. (2024). Scoring the ethics of AI robo-advice: Why we need gateways and ratings. Journal of Business Ethics, 198, 21–33.
42. Kolla, S. K. (2024). Clinical Knowledge Intelligence through Deep Learning and Natural Language Understanding in Healthcare Platforms. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 7(3), 14088.
43. Bandi, V. D. V. K. (2024). AI-Driven Predictive Risk Modeling Architectures for Financial Systems. International Journal Of Finance, 37(3), 54-78.
44. Lazar, E., Qi, S., & Tunaru, R. (2024). Measures of model risk for continuous-time finance models. Journal of Financial Econometrics, 22(5), 1456–1481.
45. Davuluri, P. S. L. N. (2023). Integrating artificial intelligence into event-driven financial crime compliance platforms. International Journal of Finance, 36(6), 707-736.
46. Mattaparthi, R. (2023). Connected Fleet Intelligence: Edge-Centric Analytics and Computer Vision for Predictive Manufacturing and Asset Resilience. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(5), 9077-9088.
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