Large language models are increasingly integrated into everyday and professional decision making, yet often exhibit sycophantic behavior by aligning with users’ views or preferences. While sycophancy can enhance interaction, its influence on users’ decisions remain unclear given different styles and task risks. We examine three forms of sycophancy—opinion agreement, direct praise, and self-deprecation—in two contrasting contexts: a low-risk speed-dating prediction task and a high-risk ETF investment task. In a 4×2 mixed-design online study (N = 106), we compare non-sycophantic AI with sycophantic variants on decision outcomes and confidence changes. Results show that sycophancy influences decision patterns in type-dependent ways. Specifically, opinion agreement reinforces initial decisions and self-deprecation boosts confidence. Interviews further indicate that users value supportive AI but question its objectivity when praise becomes excessive. These findings reveal the multifaceted effects of AI sycophancy and offer design implications for balancing support and credibility in human–AI interaction.