# Agentic Image Editing

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.

EditAgent widens an image-editing agent's action space beyond a general editor to specialized methods, and attaches to each candidate a verification record of change maps, close-ups and measured color and geometry, so an off-the-shelf VLM judge compares candidates in one listwise call. On a 600-set, 30-edit-type expert-annotated diagnostic benchmark, the general editor yields no usable result in 42.7% of sets and specialized methods recover 175 of them; in a blind study on 144 Complex-Edit samples, experts preferred parallel search over sequential refinement in 78.5% of decided comparisons, at no more than half the judge calls.

EditAgent widens an image-editing agent's action space beyond a general editor to specialized methods, and attaches to each candidate a verification record of change maps, close-ups and measured color and geometry, so an off-the-shelf VLM judge compares candidates in one listwise call. On a 600-set, 30-edit-type expert-annotated diagnostic benchmark, the general editor yields no usable result in 42.7% of sets and specialized methods recover 175 of them; in a blind study on 144 Complex-Edit samples, experts preferred parallel search over sequential refinement in 78.5% of decided comparisons, at no more than half the judge calls.