Token-Aware Workload Persona Clustering for Browser Shopping Agents: Lightweight Capacity Modeling from Web Interaction Trajectories

Abstract

Browser agents are usually evaluated by task success, reward, or human preference, yet the same trajectories also determine the serving load imposed on a language-model system. This paper studies browser shopping work as a capacity-planning problem. Each WebShop trajectory is transformed into a workload unit with instruction tokens, observation-token proxies, action counts, page types, candidate action counts, reward, and derived latency-risk indicators. The study uses 1,643 WebShop human trajectories, a 1,000-product WebShop catalog subset, 12,251 crowd instructions, and a deterministic MiniWoB++ validation sample of 1,467 demonstrations across 60 task directories. KMeans clustering over post-hoc trajectory features yields four operational personas: Direct buyers, Option comparators, Catalog scanners, and Recovery browsers. Direct buyers make up 73.5% of trajectories and require 637 prompt-token units on average, while the rare Catalog scanner persona uses 9,499 prompt-token units and 73 actions on average. Planning-time token prediction remains difficult when only instruction and product fields are available: Ridge regression achieves a held-out RMSE of 1,328.7 token units, only slightly ahead of the mean baseline, while logistic high-cost detection reaches AUC 0.688 and F1 0.503. A queue-based capacity simulation shows that under the observed persona mix, a four-worker service lane remains stable through 50 tasks per minute but crosses 38.1% wait-over-two-second risk at 60 tasks per minute. The findings show that browser-agent workloads have heavy-tailed serving costs, and that lightweight persona metadata can expose capacity pressure that is invisible in task-success metrics alone.

Keywords:

• browser agents,
• WebShop,
• MiniWoB++,
• workload clustering,
• token-aware capacity planning