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The consequences of sycophancy extend beyond isolated errors. [rathje_sycophantic_2025] found that brief conversations with sycophantic AI increased attitude extremity and certainty while inflating users’ self-perceptions: participants rated themselves as more intelligent, empathetic, and “better than average” after interacting with agreeable models. Paradoxically, users rated sycophantic responses as higher quality and expressed greater willingness to use them again. [cheng_sycophantic_2025] documented similar patterns in interpersonal domains where sycophantic AI reduced participants’ willingness to repair conflicts while increasing their conviction of being in the right. Here too, participants trusted sycophantic models more and rated them as less biased. This creates what [rathje_sycophantic_2025] referred to as a “perverse incentive” where users seek out the very systems that distort their reasoning.
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Hey HN - we're Tarush, Sidhant, and Shashij from Cekura (https://www.cekura.ai). We've been running voice agent simulation for 1.5 years, and recently extended the same infrastructure to chat. Teams use Cekura to simulate real user conversations, stress-test prompts and LLM behavior, and catch regressions before they hit production.The core problem: you can't manually QA an AI agent. When you ship a new prompt, swap a model, or add a tool, how do you know the agent still behaves correctly across the thousands of ways users might interact with it?，推荐阅读哔哩哔哩获取更多信息
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