source: arxiv artificial intelligence: ceo-bench: can agents play the long game?
level: research
language model agents are getting good at short tasks like coding or customer service. but real-world problems need more: handling long time spans, dealing with messy information, adapting to change, and juggling many parts at once. ceo-bench is a new test that checks all these skills together. it makes an agent act as the ceo of a simulated startup for 500 days. the agent controls pricing, marketing, budgeting, and other business areas through a python interface. it faces the same challenges a human ceo would, using noisy business data to make decisions.
the benchmark is built to be tough and realistic. agents must dig through interconnected databases that are not clean or simple. they have to turn what they find into a real strategy and keep many operations running toward one goal. the environment changes over time, so agents cannot just follow a fixed plan. they need to learn and adjust as new situations come up. this setup pushes agents beyond single-step tasks and into the kind of long-term thinking that real leadership requires.
early results show that current agents struggle with this kind of work. they often fail to keep the company on track over hundreds of days. the benchmark reveals gaps in planning, information gathering, and adapting to shifts. it gives researchers a clear way to measure progress on these harder problems. by focusing on a common but complex job like running a business, ceo-bench aims to drive development of agents that can handle the messy, long-term tasks that matter outside of labs.
why it matters: it provides a realistic test for ai agents on long-term, multi-step business tasks, highlighting where current models fall short in practical decision-making.
source: arxiv artificial intelligence: ceo-bench: can agents play the long game?