  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.
