Recent advances in large language models (LLMs) have enabled AI agents to perform increasingly complex tasks in web navigation. Despite this progress, effective use of such agents continues to rely on human involvement to correct misinterpretations or adjust outputs that diverge from their preferences. However, current agentic systems lack an understanding of when and why humans intervene. As a result, they might overlook user needs and proceed incorrectly, or interrupt users too frequently with unnecessary confirmation requests. This blogpost is…
