Large Language Model (LLM)-based agents have achieved promising performance in web task planning. However, they primarily focus on task completion rather than preference modeling, leading to imprecise personalization. This can be attributed to two facts: (1) Contextual scarcity. Vague instructions and insufficient background of users’ inputs make it difficult to accurately identify users’ intents. (2) Preference unpredictability. It’s hard for a single response path to handle users’ sudden intent changes. To address these issues, we propose SAPA, a Situation-Augmented Personalized web Agent. Specifically, a Situational Intent Grounding module is proposed to enrich the contexts of users’ inputs by incorporating historical records and candidate constraints, effectively alleviating the contextual scarcity. Then, we propose a Task-Driven Semantic Expansion module to explore diverse response paths and reduce preference unpredictability in turn, which is achieved by employing a test-time expansion strategy. We construct SAPA with representative LLMs and conduct extensive experiments on PersonalWAB and Amazon Reviews datasets. On PersonalWAB, SAPA achieves new state-of-the-art performance with improvements of +14.0%, +8.8%, and +1.3% in Overall score on personalized recommendation, search, and question answering, respectively. We further evaluate on three single-category scenarios from Amazon Reviews (Office Products, Beauty and Personal Care, Electronics), where SAPA consistently improves Overall scores by 16%–18% with gains across all task types. Ablation studies and case analyses further confirm that the gains stem from jointly addressing contextual scarcity and preference unpredictability.