{"ID":23507428,"CreatedAt":"2026-09-18T02:21:44.056544415Z","UpdatedAt":"2026-09-20T18:11:56.143995915Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2609.20754","arxiv_id":"2609.20754","title":"RAFT: A Stateful Retrieval-Augmented Framework for Troubleshooting Agents","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.","short_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-Augmente...","url_abs":"https://arxiv.org/abs/2609.20754","url_pdf":"https://arxiv.org/pdf/2609.20754v1","authors":"[\"Mingxuan Zhang\",\"Xiaowen Wang\",\"Anupma Sharan\",\"Zhengyi Chen\",\"Chenyu Diana Zhang\",\"Shanshan Yang\",\"Chittibabu Pacharu\"]","published":"2026-09-17T17:41:31Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"RAG\"]","has_code":false}