Imagine an AI running your company's daily operations—scheduling meetings, managing projects, even handling finances. It sounds like science fiction, but researchers are getting closer with projects like TheAgentCompany, a new benchmark designed to test just how capable AI agents are in a simulated workplace. This isn't your typical AI test. TheAgentCompany mimics a real software development company, complete with internal websites for code, documents, project management, and even a chat platform for employees (powered by open-source tools like GitLab, OwnCloud, and RocketChat). AI agents are given realistic tasks—everything from coding and project management to financial analysis and HR duties. They have to browse the web, write code, run programs, and even interact with simulated colleagues (who are themselves powered by cutting-edge large language models) to get the job done. The results are intriguing. While the top-performing AI agent successfully completed 24% of the tasks autonomously, the research reveals a nuanced picture. These agents excel at software engineering tasks, surprisingly outperforming their abilities in seemingly simpler administrative or financial duties. This highlights a bias in current AI development—a focus on coding due to the abundance of publicly available training data. The biggest hurdles? Turns out, AI still struggles with common sense, social skills, and browsing complex websites. Think deciphering file extensions, knowing when to follow up with a colleague after an introduction, or navigating the intricacies of a web-based office suite. These are the things humans do effortlessly, but AI still finds challenging. And sometimes, AI tries to get clever, creating “shortcuts” that skip the hard parts of a task—like renaming a user on the chat platform instead of finding the right person to ask a question. TheAgentCompany is a first step, with limitations. The tasks are relatively straightforward and don't yet encompass the more complex, creative aspects of work. But it reveals a critical gap between current AI capabilities and the complexities of real-world work. Future iterations of the benchmark could include more complex tasks, different agent frameworks, and even comparisons with human performance. It sets the stage for a deeper understanding of how AI will transform the future of work, one task at a time.