一种用于故障排除代理的状态化检索增强框架 RAFT
RAFT: A Stateful Retrieval-Augmented Framework for Troubleshooting Agents
这是来自微软研究团队的新方法，能更智能地处理故障排除，比普通 RAG 更有效。
我们提出了一种名为 RAFT 的状态化检索增强框架，它将历史案例抽象为时间线条目链，在条目级别进行检索，从而在故障排除代理中实现更有效的指导。该框架通过在案例进展的每个阶段提高案例命中率，显著优于标准 RAG 和 GraphRAG 基准，并展示了其在真实案例历史中的优势。
RAFT: A Stateful Retrieval-Augmented Framework for Troubleshooting Agents
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.
