RAFT: A Stateful Retrieval-Augmented Framework for Troubleshooting Agents
Teaching support bots to learn from similar problems at every step
When customer support systems look for similar past cases to solve a new problem, they typically treat old cases as complete documents—missing the fact that troubleshooting happens in stages. RAFT treats each historical case as a timeline of steps and matches cases based on where they are in the process, not just their final outcome. On real issues from Apache's bug tracker and a synthetic Windows Server benchmark, RAFT found relevant past cases more consistently than standard approaches, especially in the middle stages of troubleshooting.
Support agents waste time when they retrieve irrelevant historical cases or miss the ones that would actually help. By matching cases at intermediate steps rather than treating them as static snapshots, this approach could reduce the time spent searching for guidance and improve the accuracy of the solutions suggested to customers. The researchers released their benchmark and code, giving support teams a concrete tool to improve how their systems learn from history.