Aug 2026· International journal of computer information systems and industrial management applications· 0 citations
TL;DR
Examination of application generators as first-class platform infrastructure across their full operational lifecycle establishes that generator value is not realized at project creation alone, and builds up over time through consistent delivery pipelines, less time spent on compliance remediation, faster onboarding, and measuring at the portfolio level.
Abstract
Enterprise front-end development at scale is characterized by a persistent tension between the need for organizational consistency and the demand for product-team autonomy. Left unresolved, this tension produces fragmented codebases, duplicated infrastructure decisions, inconsistent security postures, and compounding onboarding costs. Application generators address this problem by encoding architectural standards, security defaults, and delivery contracts directly into the project creation process. Rather than relying on documentation and developer discipline to propagate platform conventions, generators make correct behavior the automatic outcome of project initialization. This article examines application generators as first-class platform infrastructure across their full operational lifecycle. The scope covers scaffold design and baseline contract definition, governance and security enforcement through generated defaults, extensibility architecture that preserves product-team autonomy without sacrificing platform coherence, pipeline and observability integration, developer experience as a technical requirement, shared asset distribution, and the governance models required to prevent generator sprawl. The article further addresses the implications of AI-assisted development for generator design, arguing that embedded static analysis and dependency governance become especially critical as AI-generated code enters enterprise codebases at an increasing rate. The article establishes that generator value is not realized at project creation alone. It builds up over time through consistent delivery pipelines, less time spent on compliance remediation, faster onboarding, and measuring at the portfolio level. Sustaining that value requires treating the generator as a versioned, owned, and actively maintained platform product, governed by the same rigor applied to the applications it produces.
GitOps continuous delivery methods, using tools like Argo CD, make Git repositories the main source of truth for both application settings and cluster status, removing the need for manual deployment tasks and preventing configuration inconsistencies.
S. Kanchumarthi· World Journal of Advanced Re...· 0 citations
Enterprise software requires specification governance to transform probabilistic AI generation into deterministic, auditable engineering, and the SGRM framework is introduced, which defines four-component specification contracts, constrains stochastic generation via deterministic validation, and integrates generation, verification, and governance into a closed-loop architecture.
Enterprise IT organizations increasingly face challenges in delivering scalable, efficient, and
governed digital services across complex environments. ServiceNow, as a leading cloud-based IT
Service Management (ITSM) platform, offers extensive capabilities for automating workflows,
consolidating enterprise processes, and enabling data-driven decision-making. This study
proposes a conceptual and applied framework for the design, governance, and scalable delivery
of ServiceNow programs across organizations. The framework integrates strategic program
planning, modular architecture design, and best-practice governance structures to ensure
alignment with organizational objectives, regulatory compliance, and operational efficiency. Key
elements include the standardization of process workflows, role-based access controls, and
continuous performance monitoring to support iterative improvement and value realization.
Applied methodologies focus on phased program deployment, stakeholder engagement, and
change management strategies that mitigate operational risks and enhance adoption. The
framework further emphasizes scalability through reusable configuration patterns, integration
with enterprise systems, and automated orchestration of cross-functional processes. Case-based
scenarios illustrate how the framework addresses common challenges such as service delivery
bottlenecks, inconsistent process adoption, and governance gaps, while enabling measurable
performance improvements. This research contributes to both theory and practice by providing a
structured approach to ServiceNow program management, demonstrating how conceptual design
principles can be translated into practical, enterprise-scale implementations. Ultimately, the
proposed framework supports organizations in achieving resilient, adaptive, and high-performing
IT service operations, fostering sustainable growth and digital transformation. Future studies may
explore the integration of artificial intelligence, predictive analytics, and real-time operational
dashboards within the framework to further optimize service delivery and strategic decisionmaking.
Joseph Edivri· INTERNATIONAL JOURNAL OF SOC...· 0 citations
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.
Bhanu Kiran Kumar Muggalla· International Journal of Int...· 0 citations
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
Microservice architecture (MSA) has emerged as a dominant paradigm for designing and deploying distributed software systems, offering granular scalability, independent deployability, and technological heterogeneity at the cost of substantially increased operational complexity. Despite its widespread industry adoption, a coherent formal analytical basis and a generalisable evaluation framework for guiding MSA adoption decisions remain underdeveloped in the academic literature. This review synthesises scholarly research published between 2014 and April 2026 to examine the foundational concepts underpinning MSA, the enabling technologies that make it tractable in practice, and the formal analysis and quality assessment approaches proposed to guide architectural decision-making. The key technologies reviewed include containerisation, container orchestration, service mesh frameworks, application programming interface gateways, event-driven messaging systems, and distributed observability tooling. The review further examines migration strategies from monolithic systems, decomposition methodologies grounded in domain-driven design, and the application of formal verification methods to distributed service systems. A multi-dimensional evaluation framework is proposed, integrating organisational readiness, technical capability, security posture, and quality attribute trade-off analysis to support structured MSA adoption decisions. The review identifies significant gaps regarding standardised readiness assessment tools, empirically validated decomposition criteria, and formal methods applicable at the architectural level — gaps that point towards a pressing need for integrated, evidence-based frameworks capable of guiding practitioners through the complex socio-technical challenges of MSA adoption.
Philomène Mbala Ilunga, Camile Likotelo Binene, Papy Kabadi Lelo Odimba et al.· Asian Journal of Research in...· 0 citations
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