As cloud service systems grow in scale and complexity, incidents that indicate unplanned interruptions and outages become unavoidable. Rapid and accurate triage of these incidents to the appropriate responsible teams is crucial to maintain service reliability and prevent significant financial losses. However, existing incident triage methods relying on manual operations and predefined rules often struggle with efficiency and accuracy due to the heterogeneity of incident data and the dynamic nature of domain knowledge across multiple teams.<\/p>\n
To solve these issues, we propose Triangle, an end-to-end incident triage system based on a Multi-Agent framework. Triangle leverages a semantic distillation mechanism to tackle the issue of semantic heterogeneity in incident data, enhancing the accuracy of incident triage. Additionally, we introduce multi-role agents and a negotiation mechanism to emulate human engineers’ workflows, effectively handling decentralized and dynamic domain knowledge from multiple teams. Furthermore, our system incorporates an automated troubleshooting information collection and mitigation mechanism, reducing the reliance on human labor and enabling fully automated end-to-end incident triage. Extensive experiments conducted on a real-world cloud production environment demonstrate that Triangle significantly improved incident triage accuracy (up to 97%) and reduced Time to Engage (TTE) by as much as 91%, demonstrating substantial operational impact across diverse cloud services.<\/p>\n","protected":false},"excerpt":{"rendered":"
As cloud service systems grow in scale and complexity, incidents that indicate unplanned interruptions and outages become unavoidable. Rapid and accurate triage of these incidents to the appropriate responsible teams is crucial to maintain service reliability and prevent significant financial losses. However, existing incident triage methods relying on manual operations and predefined rules often struggle 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