Intelligent Operation and Maintenance Intelligent Agent

Traditional monitoring mainly relies on threshold alerts, requiring manual configuration of many rules. AIOps automatically establishes normal behavior baselines through machine learning, can discover new types of anomalies that deviate from normal patterns, and consolidates massive alerts caused by the same fault into actionable events.

AIOps effects are divided into short-term and long-term: short-term (1-3 months) can achieve alert consolidation and root cause localization efficiency improvement; long-term (6+ months) can accumulate sufficient historical data for fault prediction and capacity planning.

CMDB records IT asset configuration information and relationships, and AIOps needs CMDB to provide topology relationships to assist root cause analysis. The combination can more accurately determine fault impact scope and propagation paths.

AIOps effectiveness depends on high-quality historical monitoring data. It is recommended to accumulate at least 3-6 months of monitoring metrics and alert data before enabling AI analysis capabilities, which can establish more accurate baseline models.