A Multi-Agent Generative AI and Machine Learning Framework for Autonomous Incident Intelligence, Predictive Service Assurance, and Customer Experience Optimization in Cloud-Native Digital Service Platforms

Abstract

The growing complexity of the online service platforms based on the cloud has posed new challenges to the traditional IT operations unprecedentedly, and reactive incident management is no longer adequate in ensuring the reliability of service and customer satisfaction. The authors present a Multi-Agent Generative AI and Machine Learning (MAGML) system for autonomous incident intelligence, as well as predictive service assurance and customer experience optimization in this article. The domain-specific ecosystem of intelligent agents to identify root causes, investigate incidents, assess the impact, draw up remediation plans and learn about incidents is all incorporated under the framework incorporation. With a Generative AI-based engine, heterogeneous operational data, such as telemetry, logs, distributed traces, dashboards, and past incidents, are converted into contextual operational intelligence, automated incident summaries, diagnostics, and remediation suggestions. Infrastructure performance is combined with application observability as well as customer journey analytics and business key performance indicators to develop machine learning models that are capable of predicting the occurrence of a service failure prior to the disruption of the service affecting customers. Instead, reinforcement learning enables learning to remediate itself based on outcomes of the learning, and an Operational Knowledge Graph can model the connection between services to increase correlations between events, decrease unnecessary alerts about events, and accelerate the process of root cause detection. Another Customer Experience Intelligence overlay that measures the business impact, the severity of the incident, and SLA/SLO compliance or an AI-based operational copilot that supports the engineer with runbooks, intelligent predictions, and diagnostics. With heterogeneous observability data packets that comprised the HDFS Log Dataset, the BlueGene/L (BGL) Log Dataset, the OpenTelemetry distributed traces, Kubernetes operational metrics and the Application Performance Monitoring (APM) data, the given framework has been experimentally tested and compared with six current approaches, including the threshold-based monitoring, the Random Forest, the LSTM, the Knowledge Graph-based RCA, the RAG-enabled LLM incident intelligence, and the traditional enterprise AIOps platform. Laboratory results demonstrate that the proposed MAGML model achieves an accuracy, precision, recall and F1-score of 98.4 per cent, 98.1 per cent, 98.3 per cent, and 98.2 per cent, respectively, and reduces MTTD to 3.1 minutes and MTTR to 14.6 minutes. It also boasts of 98.6% accuracy RCA, 98.3% accuracy alert-correlation, 99.1% SLA compliance and 99.0% recoverability success, which confirms that it is solid in its predictability incident management model, autonomous remediation model, service assurance model, and customer-focused operation insight of cloud-native digital service.