AI-Driven Enterprise Platform Engineering: A Conceptual Framework for Autonomous Platform Operations, Infrastructure Automation, and Scalable Cloud-Native Developer Platforms

  • Authors

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

    DOI:

    https://doi.org/10.67228/30713315/IJAIDT-V9I3P101

    Published 07-05-2026

  • AI-Driven Platform Engineering, Autonomous Operations, Aiops, Internal Developer Platform, Infrastructure Automation, Cloud-Native Systems, Agentic AI, Developer Experience, Platform Governance, Self-Healing Infrastructure

    Issue

    Vol. 9 No. 3 (2026)

    Section

    Articles

    How to Cite

    [1]
    B. K. Kumar Muggalla, “AI-Driven Enterprise Platform Engineering: A Conceptual Framework for Autonomous Platform Operations, Infrastructure Automation, and Scalable Cloud-Native Developer Platforms”, IJAIDT, vol. 9, no. 3, pp. 01–37, Jul. 2026, doi: 10.67228/30713315/IJAIDT-V9I3P101.

  • Abstract

    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.

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