Autonomous Software Architecture Optimization Using AI for Large-Scale Enterprise Information Systems

Paper ID: EIJTEM_2026_13_2_1-8

Author's Name: Pradeep Kumar Mulluri

Volume: 13

Issue: 2

Year: 2026

Page No: 1-8

Abstract:

In this context, enterprise information systems are becoming large-scale characterized by rapid complexity growth, dynamic workloads, heterogeneous technologies and with strong requirements in terms of performance, scalability and security. Conventional approaches to software architecture efficiency optimization are usually based on designer’s intuition, static rules and expert-driven overrides which can hardly keep pace with volatile operational conditions. To address these issues, this paper introduces an AI based autonomous software architecture optimization framework for large-scale enterprise systems. The introduced methodology combines principles of machine learning, reinforcement learning and intelligent agents which are employed to observe the behavior of systems over time, respond to complex data about micro-architectural performance measures, and suggest or implement changes in an automatic way. Critical architectural features including component placement, service orchestration, resource management, fault tolerance and scalability are dynamically reconfigured at runtime according to workload characteristics and system constraints. The proposed framework utilizes feedback driven learning loops, to achieve self-adaptive, self-healing, and self-optimizing even with minimal human involvement. Extensive experimentation on enterprise-grade optimization use-cases show substantial gains in system’s performance, cost optimizations, and operational reliability over the traditional rule-driven and static-optimized solutions. The findings suggest that AI-based automatic architecture optimization would be a scalable and sustainable solution to cope with the increasing complexity of enterprise information systems, which mitigates the operational expense as well as improves long-term robustness of our systems.

Keywords: Autonomous Software Architecture, AI-Driven Optimization, Enterprise Information Systems, Self-Adaptive Systems, Machine Learning, Reinforcement Learning

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