Nissist: An Incident Mitigation Copilot based on Troubleshooting Guides
This addresses on-call fatigue and productivity issues for engineers in enterprises-level cloud services, though it appears incremental as it builds on existing troubleshooting knowledge with LLM enhancements.
The paper tackles the problem of complex incident management in cloud services by proposing Nissist, a copilot that uses LLMs and multi-agent systems to provide proactive suggestions from troubleshooting guides and histories, resulting in a significant reduction in Time to Mitigate (TTM) for incidents.
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.