WebSAPE: Structurally Aware and Progressively Experienced Web Agent

Current web agents lack mechanisms to autonomously acquire structural knowledge of website architecture or to accumulate operational experience over time, and consequently regress to blind exploration on complex tasks. To address this, we present WebSAPE, a Structurally Aware and Progressively Experienced web-agent framework. WebSAPE maintains a centralized knowledge store consisting of (i) per-site textual topology guides synthesized from website exploration, summarizing page functions, reusable operations, and expected destinations, and (ii) an experience repository of operational lessons distilled from historical trajectories. A progressive learning pipeline is built to update the knowledge one-shot and incrementally. At runtime, both artifacts are jointly retrieved and injected into the agent's context to facilitate its execution. Additionally, we introduce BizArena, a high-fidelity benchmark for web agents in enterprise productivity environments, with 100 curated tasks spanning email, calendar, chat, project management, and cross-app workflows within self-hosted environments under deterministic, instrumented evaluation. Experiments indicate that our WebSAPE surpasses the state-of-the-art academic and industrial methods on the WebArena (+13.6 pp), WebArena-Lite (+10.3 pp), WebVoyager (+2.4 pp), and BizArena (+4.0 pp) benchmarks, demonstrating that integrating structural priors with progressively distilled experience yields markedly more capable web agents. Code and benchmark are publicly released at https://github.com/papersubmissionstore/WebSAPE.