Cognitive Platform Engineering for Autonomous Cloud Operations
Abstract
Modern DevOps practices have accelerated software delivery through automation, CI/CD pipelines, and observability tooling,but these approaches struggle to keep pace with the scale and dynamism of cloud-native systems. As telemetry volume grows and configuration drift increases, traditional, rule-driven automation often results in reactive operations, delayed remediation, and dependency on manual expertise. This paper introduces Cognitive Platform Engineering, a next-generation paradigm that integrates sensing, reasoning, and autonomous action directly into the platform lifecycle. This paper propose a four-plane reference architecture that unifies data collection, intelligent inference, policy-driven orchestration, and human experience layers within a continuous feedback loop. A prototype implementation built with Kubernetes, Terraform, Open Policy Agent, and ML-based anomaly detection demonstrates improvements in mean time to resolution, resource efficiency, and compliance. The results show that embedding intelligence into platform operations enables resilient, self-adjusting, and intent-aligned cloud environments. The paper concludes with research opportunities in reinforcement learning, explainable governance, and sustainable self-managing cloud ecosystems.
Cite
@article{arxiv.2601.17542,
title = {Cognitive Platform Engineering for Autonomous Cloud Operations},
author = {Vinoth Punniyamoorthy and Nitin Saksena and Srivenkateswara Reddy Sankiti and Nachiappan Chockalingam and Aswathnarayan Muthukrishnan Kirubakaran and Shiva Kumar Reddy Carimireddy and Durgaraman Maruthavanan},
journal= {arXiv preprint arXiv:2601.17542},
year = {2026}
}