🤖 AI Summary
Traditional rule-based operations struggle to manage the complexity of cloud-native systems amid their scale, dynamism, and telemetry data explosion, often resulting in delayed responses and heavy reliance on manual intervention. This work proposes a novel paradigm—cognitive platform engineering—introducing a four-plane reference architecture encompassing perception, reasoning, policy orchestration, and human-in-the-loop collaboration. By embedding intelligence throughout the platform lifecycle and establishing a continuous feedback loop, the approach enables adaptive, intent-aligned autonomous operations. A prototype system built on Kubernetes, Terraform, Open Policy Agent, and machine learning–based anomaly detection demonstrates significant reductions in mean time to repair, alongside improved resource efficiency and compliance. These results validate the paradigm’s capacity to support highly resilient, self-tuning cloud environments.
📝 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.