2026· International Journal of Intelligent Automation & Robotics Engineering· Vol 9, pp. 01-22· 0 citations
TL;DR
An AI-enabled enterprise platform engineering framework for scalable developer platforms, intelligent infrastructure automation, and operational excellence is developed that indicates that combining self-service workflows with governed AI assistance can improve process consistency, reduce operational handoffs, strengthen continuous compliance, and support earlier detection and resolution of infrastructure failures.
Abstract
Enterprise platform engineering has emerged as a response to the operational complexity created by cloud-native applications, microservices, Kubernetes, and expanding software delivery toolchains. Yet many enterprises continue to rely on fragmented developer tools, manually coordinated infrastructure processes, inconsistent configurations, and reactive operational practices that increase cognitive load and delay service delivery. This study develops an AI-enabled enterprise platform engineering framework for scalable developer platforms, intelligent infrastructure automation, and operational excellence. Following a design-science approach, the study synthesizes evidence from 35 peer-reviewed publications and translates identified capabilities into an integrated architectural artifact. The framework combines an internal developer portal, reusable service templates, infrastructure as code, CI/CD and GitOps orchestration, cloud-native runtime services, policy-as-code controls, unified observability, and an AI intelligence layer for configuration assistance, anomaly detection, capacity forecasting, root-cause analysis, and remediation recommendations. Human approval gates, explainability controls, audit trails, and rollback mechanisms are incorporated to constrain high-risk automated actions. The framework is evaluated through criterion-based architectural analysis and comparative operational scenarios covering service onboarding, infrastructure provisioning, deployment, workload scaling, policy violations, and incident recovery. The evaluation indicates that combining self-service workflows with governed AI assistance can improve process consistency, reduce operational handoffs, strengthen continuous compliance, and support earlier detection and resolution of infrastructure failures. The study contributes a unified, measurable model that connects platform engineering with AIOps, DevSecOps, developer experience, and cloud governance. Practically, it provides enterprise technology leaders and platform teams with a phased basis for moving from fragmented DevOps tooling towards secure, observable, and progressively autonomous platform operations.
Cloud-native adoption has transformed enterprise software delivery, but it has also increased operational complexity, tool fragmentation, infrastructure dependencies, and developer cognitive load. Existing practices in DevOps, artificial intelligence for IT operations, infrastructure as code, internal developer platforms, and observability address parts of this problem, yet they are rarely integrated into a single governed architecture. This study develops the Autonomous Platform Engineering and Experience Architecture, known as APEXA, as a conceptual framework for AI-driven enterprise platform engineering. The framework-development method synthesizes established principles from platform engineering, AIOps, GitOps, cloud-native control planes, developer experience, and responsible artificial intelligence governance. APEXA consists of six interconnected layers: a developer-experience interface, declarative infrastructure automation, unified observability and operational intelligence, agentic decision support, policy and governance controls, and a continuous learning and feedback mechanism. The framework introduces graduated levels of operational autonomy, ranging from human-assisted recommendations to policy-bounded autonomous remediation, supported by approval gates, audit trails, rollback mechanisms, and risk-based escalation. Its expected contribution is a unified reference architecture that can help enterprises reduce manual infrastructure work, improve service reliability, support scalable self-service, and govern AI agents operating close to production control systems. The study concludes that autonomous platform operations should not depend solely on model intelligence. Their effectiveness requires reliable telemetry, declarative interfaces, constrained permissions, transparent decision records, and measurable operational and developer outcomes.
Bhanu Kiran Kumar Muggalla· International Journal of Art...· 0 citations
Enterprise IT environments have become increasingly complex due to the rapid growth of cloud computing, microservices architectures, DevOps practices, hybrid infrastructures, and continuous product evolution. Traditional management approaches often operate in silos, creating challenges in application development, infrastructure management, product migration, and operational support. This paper proposes a comprehensive Digital Twin Framework for Enterprise IT Operations that establishes a dynamic virtual representation of enterprise technology ecosystems. The framework integrates real-time operational data, application lifecycle information, infrastructure telemetry, migration intelligence, and support analytics into a unified digital environment. By leveraging technologies such as artificial intelligence, machine learning, observability platforms, predictive analytics, and automation, the proposed framework enables continuous monitoring, simulation, impact analysis, capacity planning, risk assessment, and proactive decision-making across the IT lifecycle. The framework bridges the gap between development and operations by providing end-to-end visibility of applications, infrastructure resources, business services, and migration dependencies. Furthermore, it supports scenario-based testing, root-cause analysis, performance optimization, and predictive maintenance, reducing operational risks and improving service reliability. A conceptual architecture is presented to demonstrate how digital twin capabilities can enhance collaboration among development, infrastructure, migration, and support teams while increasing organizational agility and operational resilience. The study contributes a unified approach to enterprise IT management and highlights the potential of digital twins as a strategic enabler for intelligent, data-driven IT operations.
Ganesh Kumar Gangannagari· International Journal of Eme...· 0 citations
The rapid evolution of cloud computing has transformed enterprise software development by enabling scalable infrastructure, distributed applications, microservices architectures, and increasingly frequent software releases. However, the growing complexity of cloud-native environments introduces challenges related to deployment latency, pipeline failures, resource inefficiency, service availability, and recovery from unsuccessful releases. This study examines the optimization of cloud-native Continuous Integration and Continuous Deployment (CI/CD) workflows through DevOps automation, with emphasis on improving deployment speed, resilience, and operational efficiency across enterprise environments. The proposed approach integrates automated code integration, testing, containerization, infrastructure as code, Kubernetes orchestration, continuous monitoring, policy-driven deployment, and automated rollback mechanisms within an integrated delivery pipeline. Performance is evaluated using deployment frequency, lead time, pipeline execution time, change failure rate, mean time to recovery, resource utilization, and service availability. The study further investigates pipeline bottlenecks and the contribution of automation to deployment consistency, fault detection, scalability, and recovery performance. The resulting framework provides a systematic basis for balancing rapid software delivery with reliability, resilience, and efficient infrastructure utilization across increasingly complex cloud-native enterprise systems and applications.
Destiny Pwul· International Journal of Res...· 0 citations
The rapid evolution of enterprise architecture necessitates innovative approaches to manage the increasing complexities of digital ecosystems. This paper explores the transformative potential of Artificial Intelligence (AI)-driven cloud solutions in modernizing enterprise architecture, with a focus on integrating DevOps and DataOps methodologies to achieve scalability. AI-powered tools and frameworks in cloud computing offer unparalleled scalability, operational efficiency, and real-time adaptability, enabling enterprises to remain competitive in a data-driven economy. By combining DevOps' focus on streamlining software development and operations with DataOps' emphasis on agile and automated data pipeline management, organizations can optimize workflow automation, accelerate deployment cycles, and enhance decision-making processes. AI further augments this synergy by facilitating predictive analytics, anomaly detection, and intelligent resource allocation, which are critical for achieving scalability and reliability in dynamic business environments. Case studies highlight the successful application of these technologies across various industries, showcasing measurable improvements in performance and cost efficiency. The paper also addresses challenges in adopting AI-driven cloud solutions, including data privacy, compliance, and skill gaps, offering actionable recommendations for mitigating these obstacles. Emphasis is placed on the need for collaborative strategies between IT and business teams to maximize the potential of integrated DevOps and DataOps frameworks.
Fatou Diop· International Journal of Art...· 0 citations
The Adaptive Risk-Driven DevSecOps Framework (ARDDSF) is proposed, a layered framework for securing multi-cloud enterprise systems in the era of agentic artificial intelligence that bridges DevSecOps automation, AI-assisted security analysis, Zero Trust policy enforcement, and multi-cloud governance.
Nitin Bodade· International Journal of Inn...· 0 citations
The findings indicate that IAAF provides an integrated and practical architectural approach for operationalizing intelligent API automation at enterprise scale, and enables organizations to strengthen governance, improve transaction reliability, optimize traffic management, and enhance operational visibility.
Jyothirmai Gurramula· International Journal of Eng...· 0 citations
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