CEO-Bench Evaluates AI Agents' Long-Term Strategic Capabilities

While language model agents excel at isolated, short-term tasks, their ability to manage complex, long-horizon challenges in dynamic, uncertain environments remains largely untested. Real-world scenarios, such as running a startup, demand a combination of sophisticated skills: navigating uncertainty, acquiring information from noisy data, adapting to change, and coordinating numerous interdependent decisions. To address this gap, the CEO-Bench benchmark has been introduced. It simulates the operation of a startup over 500 days, requiring an AI agent to manage various business aspects like pricing, marketing, and budgeting through a programmable Python interface. The environment mirrors the complexities and challenges faced by a human CEO. The benchmark reveals that even state-of-the-art models struggle with this level of strategic complexity. Only a few advanced models managed to stay above the initial capital, and none consistently achieved profitability. This highlights that while agents can execute tasks, mastering sustained, adaptive progress over extended periods in a noisy, interconnected business environment is still a significant hurdle for current AI.