RAFT: A Stateful Retrieval-Augmented Framework for Troubleshooting Agents
[Sztuczna inteligencja] Autorzy: Mingxuan Zhang, Xiaowen Wang, Anupma Sharan i 4 więcej
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 …
- Kategoria arxiv
- cs.AI
- Kategoria
- Sztuczna inteligencja