Enterprise Platform Engineering: An AI-Enabled Framework for Scalable Developer Platforms, Intelligent Infrastructure Automation, and Operational Excellence

  • Authors

    • Bhanu Kiran Kumar Muggalla Computer Information Systems, University of Central Missouri, USA. Author

    DOI:

    https://doi.org/10.67228/30715725/IJIARE-V9I3P101

    Published 07-05-2026

  • Platform Engineering, Artificial Intelligence, Internal Developer Platform, Infrastructure as Code, AIops, Developer Experience, Cloud-Native Infrastructure, Operational Excellence

    Issue

    Vol. 9 No. 3 (2026)

    Section

    Articles

    How to Cite

    [1]
    B. K. Kumar Muggalla, “Enterprise Platform Engineering: An AI-Enabled Framework for Scalable Developer Platforms, Intelligent Infrastructure Automation, and Operational Excellence”, IJIARE, vol. 9, no. 3, pp. 01–22, Jul. 2026, doi: 10.67228/30715725/IJIARE-V9I3P101.

  • 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.

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