Transforming Enterprise Cloud Operations through Autonomous Service Automation and Platform Engineering Practices

Abstract

Cloud-native architectures have increased enterprise operational complexity by distributing services across heterogeneous multi-cloud environments. Traditional DevOps pipelines support controlled deployments but are limited by fragmented toolchains, inconsistent governance, weak configuration-drift visibility, and delayed remediation. This paper presents an autonomous service automation framework integrated with platform engineering principles to provide closed-loop operational control. The framework combines telemetry normalization, Temporal Convolutional Network (TCN)-based temporal feature extraction, Graph Attention Network (GAT)-based configuration dependency modeling, cross-modal attention fusion, and a Deep Q-Network (DQN) remediation agent within an Internal Developer Platform. Three benchmark datasets were created from Kubernetes lifecycle events, multi-cloud configuration logs, and production incident records after anonymization, schema normalization, event labeling, and train-validation-test separation. Experiments on CloudBench-2024, PlatformOps-DS, and EnterpriseSRE-X show a 43.7% reduction in mean time to recovery (MTTR), a 38.2% increase in deployment frequency, and a 61.4% reduction in configuration-drift incidents compared with baseline DevOps pipelines. The framework also achieves a 97.8% platform reliability score across 10,000 simulated production events. These results demonstrate that autonomous remediation combined with disciplined platform engineering can improve enterprise cloud resilience, governance compliance, and developer productivity at scale.