Agentic AI Modernization: Transforming Institutional Infrastructure Through Orchestrated Multi-Agent LLM Framework.
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| Název: | Agentic AI Modernization: Transforming Institutional Infrastructure Through Orchestrated Multi-Agent LLM Framework. |
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| Autoři: | Damarched, Mahesh Kumar |
| Zdroj: | Journal of Computer Science & Technology Studies; 2026, Vol. 8 Issue 4, p1-24, 24p |
| Abstrakt: | While managing constrained funds and strict regulatory requirements, the higher education institutions are under unprecedented pressure to modernize outdated information systems, such as mainframe-based Student Information Systems (SIS), custom registration platforms, legacy Learning Management Systems (LMS) and Enterprise Resource Planning (ERP) deployments. The complexity of institutional governance is being overlooked by the conventional single-agent based approaches to legacy modernization, which is delaying the digital transformation and creating security vulnerabilities. In order to achieve end-to-end code analysis, intelligent planning, safe migration and rigorous validation through specialized agents coordinated by institutional governance patterns, this research presents a novel agentic architecture using multi-agent Large Language Model (LLM) frameworks, created especially for higher education legacy system modernization. Crucially, this research innovates the deployment process, where we suggest an on-premises implementation strategy that natively protects sensitive student and faculty data, while maintaining GDPR, CCPA and FERPA compliance. Which projects to be challenging by cloudbased solutions, as these introduce data residency and compliance complexities. For COBOL/MUMPS/PL-I legacy codebases, our research demonstrated 87% successful modernization rate, with a 65% decrease in manual intervention and a 78% improvement in documentation accuracy. By mapping multi-agent workflows to existing institutional governance structures, academic committees, change boards and divisional responsibility models, the framework accomplishes institutional alignment, thereby increasing the credibility and organizational compatibility of agentic modernization solutions. This study offers institutions a revolutionary route to modernization, that maintains institutional data sovereignty, while significantly cutting modernization timelines and costs by bridging cutting-edge AI research with useful higher education IT strategy. [ABSTRACT FROM AUTHOR] |
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| Databáze: | Complementary Index |
Nájsť tento článok vo Web of Science | Abstrakt: | While managing constrained funds and strict regulatory requirements, the higher education institutions are under unprecedented pressure to modernize outdated information systems, such as mainframe-based Student Information Systems (SIS), custom registration platforms, legacy Learning Management Systems (LMS) and Enterprise Resource Planning (ERP) deployments. The complexity of institutional governance is being overlooked by the conventional single-agent based approaches to legacy modernization, which is delaying the digital transformation and creating security vulnerabilities. In order to achieve end-to-end code analysis, intelligent planning, safe migration and rigorous validation through specialized agents coordinated by institutional governance patterns, this research presents a novel agentic architecture using multi-agent Large Language Model (LLM) frameworks, created especially for higher education legacy system modernization. Crucially, this research innovates the deployment process, where we suggest an on-premises implementation strategy that natively protects sensitive student and faculty data, while maintaining GDPR, CCPA and FERPA compliance. Which projects to be challenging by cloudbased solutions, as these introduce data residency and compliance complexities. For COBOL/MUMPS/PL-I legacy codebases, our research demonstrated 87% successful modernization rate, with a 65% decrease in manual intervention and a 78% improvement in documentation accuracy. By mapping multi-agent workflows to existing institutional governance structures, academic committees, change boards and divisional responsibility models, the framework accomplishes institutional alignment, thereby increasing the credibility and organizational compatibility of agentic modernization solutions. This study offers institutions a revolutionary route to modernization, that maintains institutional data sovereignty, while significantly cutting modernization timelines and costs by bridging cutting-edge AI research with useful higher education IT strategy. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 2709104X |
| DOI: | 10.32996/jcsts.2026.8.4.1 |