Nissist: An Incident Mitigation Copilot based on Troubleshooting Guides

TL;DR

Nissist utilizes large language models to analyze troubleshooting guides and incident histories, providing proactive incident mitigation suggestions that reduce resolution time and improve operational efficiency in cloud services.

Contribution

This work introduces Nissist, a novel LLM-based system that automates and enhances incident mitigation by extracting insights from unstructured troubleshooting guides and histories.

Findings

Significantly reduces Time to Mitigate (TTM) in incident handling.

Alleviates operational burden on on-call engineers.

Improves overall service reliability.

Abstract

Effective incident management is pivotal for the smooth operation of enterprises-level cloud services. In order to expedite incident mitigation, service teams compile troubleshooting knowledge into Troubleshooting Guides (TSGs) accessible to on-call engineers (OCEs). While automated pipelines are enabled to resolve the most frequent and easy incidents, there still exist complex incidents that require OCEs' intervention. However, TSGs are often unstructured and incomplete, which requires manual interpretation by OCEs, leading to on-call fatigue and decreased productivity, especially among new-hire OCEs. In this work, we propose Nissist which leverages TSGs and incident mitigation histories to provide proactive suggestions, reducing human intervention. Leveraging Large Language Models (LLM), Nissist extracts insights from unstructured TSGs and historical incident mitigation discussions, forming a comprehensive knowledge base. Its multi-agent system design enhances proficiency in precisely discerning user queries, retrieving relevant information, and delivering systematic plans consecutively. Through our user case and experiment, we demonstrate that Nissist significant reduce Time to Mitigate (TTM) in incident mitigation, alleviating operational burdens on OCEs and improving service reliability. Our demo is available at https://aka.ms/nissist_demo.