The serving problem
Long-running agents collect instructions, observations, tool results, and intermediate reasoning. Sending all of it through every serving step increases latency and cost.
Our gisting model converts the useful state into a smaller learned representation. Evaluation focuses on task completion, not only token reconstruction.