RAFT: A Stateful Retrieval-Augmented Framework for Troubleshooting Agents

Problem

This paper addresses the limitations of existing retrieval-augmented generation (RAG) systems, particularly their inability to effectively manage the multi-stage and stateful nature of support cases. The authors highlight that current systems struggle with the complexities involved in troubleshooting processes, which often require contextual understanding and historical case retrieval. Additionally, the work is presented as a preprint, indicating that it has not yet undergone peer review.

Method

The proposed framework, RAFT (Retrieval-Augmented Framework for Troubleshooting Agents), utilizes a novel retrieval mechanism that abstracts closed historical cases into a directed chain of timeline entries, allowing for retrieval at the entry level. The data sources include a synthetic benchmark derived from Microsoft Learn Windows Server documentation and real Apache Jira issues, which are annotated with human-created duplicate labels. RAFT also incorporates an optional graph representation that links cases through a configurable similarity representation, enhancing the contextual retrieval process.

Results

RAFT demonstrates statistically significant improvements in case hit rates over both vanilla RAG and GraphRAG baselines at every stage of case progress, indicating its effectiveness in retrieving relevant historical cases. While the paper provides evidence of performance gains, it notes that there is directional evidence suggesting that these advantages may transfer to real case histories, although no quantitative results are reported for the Jira dataset.

Limitations

The authors acknowledge a significant limitation in the availability of public multi-stage troubleshooting data, which is extremely rare. This scarcity may hinder the generalizability of the framework and its applicability to a broader range of real-world scenarios. Additionally, the reliance on synthetic data for benchmarking may not fully capture the complexities of actual troubleshooting cases.

Why it matters

The implications of this work are substantial for the development of intelligent troubleshooting agents. By effectively managing the stateful nature of support cases, RAFT could enhance the efficiency and accuracy of automated support systems, leading to improved user experiences and reduced resolution times. This framework sets a foundation for future research in stateful retrieval mechanisms and their applications in various domains requiring complex problem-solving capabilities.