The rapid evolution of enterprise-scale digital infrastructures has necessitated the development of intelligent, scalable, and autonomous system management frameworks capable of handling increasing complexity. Traditional centralized architectures face significant limitations in adaptability, fault tolerance, and scalability when applied to modern corporate ecosystems characterized by distributed resources, dynamic workloads, and heterogeneous system interactions. This paper proposes a Corporate Multi-Agent Structural Model (CMASM) designed to enable intelligent system oversight while ensuring expandable operational independence across enterprise environments.

The proposed model integrates principles from multi-agent systems (MAS), computational modeling, and adaptive system governance to create a decentralized yet coordinated architecture. By leveraging autonomous agents capable of decision-making, communication, and self-optimization, the framework enhances system resilience, scalability, and operational efficiency. The study draws upon theoretical foundations from agent-based modeling, distributed computation, and adaptive control systems to construct a robust structural paradigm applicable to corporate infrastructures.

A comprehensive analysis of existing frameworks highlights critical gaps in scalability, coordination, and governance, particularly in environments requiring real-time responsiveness and distributed decision-making. The CMASM addresses these gaps by introducing layered agent hierarchies, adaptive coordination protocols, and governance mechanisms aligned with enterprise objectives. The model also incorporates insights from computational biological systems and behavioral simulations to emulate dynamic system interactions and optimize performance under uncertainty.

The findings demonstrate that the proposed architecture significantly improves system adaptability, reduces operational bottlenecks, and enhances fault tolerance compared to conventional centralized approaches. Furthermore, the integration of governance-oriented agentic frameworks ensures compliance, transparency, and strategic alignment within corporate systems.

This research contributes to the advancement of intelligent enterprise architectures by providing a scalable, decentralized, and governance-driven model that supports long-term system evolution. The study also outlines future research directions, including the integration of advanced machine learning techniques and real-time adaptive control mechanisms to further enhance system intelligence and autonomy.

Multi-Agent Systems, Distributed Intelligence, Corporate Systems Architecture, Autonomous Agents

Digital energy. Ministry of Energy of the Russian Federation [Online]. Available: https://minenergo.gov.ru/node/11830 [Accessed: Feb. 06, 2019 ].

Energy Independence and Security Act of 2007.

Grid 2030: A National Vision for Electricity's Second 100 Years. Department of Energy Office of Electric Transmission and Distribution. July 2003.

Hanan J., Prnsinkiewicz P., Zalucki M., and Skirvin D., “Simulation of insect movement with respect to plant architecture and morphogenesis,” Computers and Electronics in Agriculture, vol. 35, pp. 255–269, 2002.

Karr J. R., Sanghvi J. C., Macklin D. N., Gutschow M. V., Jacobs J. M., Bolival B., “A whole-cell computational model predicts phenotype from genotype,” Cell, vol. 150, pp. 389–401, 2012.

Malawska A., and Topping C. J., “Evaluating the role of behavioral factors and practical constraints in the performance of an agent-based model of farmer decision making,” Agricultural Systems, vol. 143, pp. 136–146, 2016.

Martin K. R., Barua D., Kauffman A. L., Westrate L. M., Posner R. G., Hlavacek W. S., “Computational model for autophagic vesicle dynamics in single cells,” Autophagy, vol. 9, pp. 74–92, 2013.

The main provisions of the concept of an intelligent power system with an active-adaptive network. PJSC “FGC UES” [Online]. Available: http://www.fsk-ees.ru/upload/docs/ies-aas.pdf [Accessed: Nov. 26, 2018 ].

Venkiteela, P. (2026). An Enterprise Agentic Architecture Framework for Agentic AI Governance and Scalable Autonomy. Scientific Journal of Computer Science, 2(1), 1–17. https://doi.org/10.64539/sjcs.v2i1.2026.368

White N., and Hanan J., “A model of macadamia with application to pruning in orchards,” in XXIX IHC-Proc. Int. Symp. on Nut Crops, M. Wirthensohn, Ed. Brisbane, Queensland, Australia: International Society for Horticultural Science (ISHS), 2016, pp. 75–82.