# RetryRoute

Web agents edit images by calling open editing models in a critique-and-retry loop, yet a retry after a rejection succeeds only 17–53% as often as a first try, while existing routers treat each retry as an independent draw. RetryRoute estimates each model's next-attempt success probability from the request's failure history, plans retries, escalations and abandonment over failure states, and charges live queue waits to the cost. At the main budget it exceeds the strongest of eleven prior baselines by 2.8 points on average over eight groups, and on a three-GPU serving testbed one setting raises the share of edits meeting the service objective by 5.0–6.3 points over the best prior baseline at every load.

Web agents edit images by calling open editing models in a critique-and-retry loop, yet a retry after a rejection succeeds only 17–53% as often as a first try, while existing routers treat each retry as an independent draw. RetryRoute estimates each model's next-attempt success probability from the request's failure history, plans retries, escalations and abandonment over failure states, and charges live queue waits to the cost. At the main budget it exceeds the strongest of eleven prior baselines by 2.8 points on average over eight groups, and on a three-GPU serving testbed one setting raises the share of edits meeting the service objective by 5.0–6.3 points over the best prior baseline at every load.