Cognitive Platform Engineering for Autonomous Cloud Operations

TL;DR

This paper presents Cognitive Platform Engineering, a new paradigm integrating sensing, reasoning, and autonomous actions into cloud platform management to improve efficiency, resilience, and compliance in dynamic cloud-native environments.

Contribution

It introduces a four-plane reference architecture unifying data collection, inference, policy orchestration, and human interaction, with a prototype demonstrating tangible operational improvements.

Findings

Reduced mean time to resolution

Enhanced resource efficiency

Improved compliance and resilience

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.