RAFT: A Stateful Retrieval-Augmented Framework for Troubleshooting Agents
Researchers introduce RAFT, a stateful retrieval-augmented framework that retrieves timeline entries from historical cases to improve enterprise troubleshooting agents.
RAFT abstracts closed support cases into directed chains of timeline entries and retrieves at the entry level, returning parent-case trajectories anchored at matched states, with an optional case-level similarity graph. It beat vanilla RAG and GraphRAG baselines on Case Hit at every stage of case progress, using a synthetic benchmark built from Microsoft Learn Windows Server documentation and real Apache Jira issues with human-created duplicate labels. The benchmark, implementation, and Jira evaluation set are publicly released.
- Retrieves at timeline-entry level instead of whole documents
- Case-level graph links cases via configurable similarity
- Beats vanilla RAG and GraphRAG on Case Hit at every stage
- Evaluated on Windows Server docs benchmark and Apache Jira issues
- Benchmark, implementation, and Jira set released
From the original article
1 min read at arxiv.orgEffective 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.
By Mingxuan Zhang · arxiv.org
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