AI web agents can perform complex tasks, such as searching for products and making purchases on users' behalf. However, verifying an agent's output remains difficult, because existing transparency mechanisms (e.g., full trajectory logs) require users to review logs and explanations, keeping verification a passive reading task. In this work, we view agent verification as an interactive activity and present HANSEL. Given an agent trajectory, HANSEL extracts evidence pages and snippets and presents them as navigable, live views that preserve relevant page state (e.g., applied filters), allowing users to verify how the agent arrived at its answer through interaction. In a controlled user study with 14 participants, we show that HANSEL significantly reduces task completion time and perceived effort, demonstrating that reframing verification as an interactive activity enables more efficient post-hoc verification of AI agents.

翻译：暂无翻译