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.

翻译：现代DevOps实践通过自动化、CI/CD流水线和可观测性工具加速了软件交付，但这些方法难以跟上云原生系统的规模和动态性。随着遥测数据量的增长和配置漂移的增加，传统的规则驱动自动化通常导致被动运维、修复延迟以及对人工专业知识的依赖。本文提出了认知平台工程，这是一种将感知、推理和自主行动直接集成到平台生命周期的下一代范式。我们提出了一个四平面参考架构，在持续反馈循环中统一了数据收集、智能推理、策略驱动编排和人工体验层。基于Kubernetes、Terraform、Open Policy Agent和基于机器学习的异常检测构建的原型实现，展示了在平均解决时间、资源效率和合规性方面的改进。结果表明，将智能嵌入平台运维能够实现弹性、自调整和意图对齐的云环境。本文最后探讨了强化学习、可解释治理和可持续自管理云生态系统等领域的研究机遇。