RAFT: A Stateful Retrieval-Augmented Framework for Troubleshooting Agents

2026-09-17

AI Summary

RAFT通过将历史案例抽象为时间线条目链并在此级别检索，解决了现有系统忽视案例多阶段状态性的问题，改进了故障排查代理的效果。

Abstract

Effective troubleshooting agents in enterprise customer support depend on retrieving actionable guidance from similar historical cases, yet existing retrieval-augmented generation (RAG) systems treat support cases as static documents and overlook their multi-stage, stateful nature. We introduce RAFT (Retrieval-Augmented Framework for Troubleshooting Agents), a stateful RAG framework that abstracts each closed historical case into a directed chain of timeline entries and retrieves at the entry level, surfacing cases whose intermediate states match the active case and returning the parent-case trajectory anchored at the matched state; an optional case-level graph links cases through a configurable similarity representation. We evaluate this retrieval layer directly, which, unlike evaluating a full agent system, requires no production deployment. Because public multi-stage troubleshooting data is extremely rare, we pair a synthetic benchmark built from Microsoft Learn Windows Server documentation with real Apache Jira issues carrying human-created duplicate labels. RAFT improves Case Hit over vanilla RAG and GraphRAG baselines at every stage of case progress, with statistically significant gains over the strongest baseline; the Jira results provide directional evidence that the advantage transfers to real case histories. We release our benchmark, implementation, and the Apache Jira evaluation set.

Problem

• Retrieval-Augmented Generation
• Stateful Nature
• Troubleshooting Agents
• Historical Cases
• Customer Support

Innovation

• stateful RAG
• timeline entries
• multi-stage troubleshooting
• case-level graph
• retrieval-augmented framework