Discover why overly agreeable AI agents pose critical risks when executing real-world actions, and how to solve the growing problem of AI sycophancy

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AI agents inherit a tendency toward agreeableness from reinforcement learning and human feedback processes. While this behavior is mostly harmless in chatbots, it becomes far more consequential when agents can take real-world actions. Drawing on decades of research from aviation, healthcare, and human-factors engineering, this article argues that the next generation of agents should be designed around calibrated disagreement: knowing when to proceed, when to warn, and when to stop.