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arXiv · 2603.14248

Why Do LLM-based Web Agents Fail? A Hierarchical Planning Perspective

Mohamed Aghzal·Gregory J. Stein·Ziyu Yao
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Abstract

Large language model (LLM) web agents are increasingly used for web navigation but remain far from human reliability on realistic, long-horizon tasks. Existing evaluations focus primarily on end-to-end success, offering limited insight into where failures arise. We propose a hierarchical planning framework to analyze web agents across three layers (i.e., high-level planning, low-level execution, and replanning), enabling process-based evaluation of reasoning, grounding, and recovery. Our experiments show that structured Planning Domain Definition Language (PDDL) plans produce more concise and goal-directed strategies than natural language (NL) plans, but low-level execution remains the dominant bottleneck. These results indicate that improving perceptual grounding and adaptive control, not only high-level reasoning, is critical for achieving human-level reliability. This hierarchical perspective provides a principled foundation for diagnosing and advancing LLM web agents.

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Mohamed Aghzal, Gregory J. Stein, Ziyu Yao. 2026-03-15. Why Do LLM-based Web Agents Fail? A Hierarchical Planning Perspective. https://arxiv.org/abs/2603.14248

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