Knowledge workers typically spend mornings managing a client report, a budget spreadsheet, a slide deck, and an email backlog, with all of these tasks interdependent and competing for attention. For artificial intelligence agents to provide real value in that setting, they must be able to operate across multiple tasks and contexts in a similarly fluid way rather than handle isolated prompts in sequence. The Corpgen effort is framed around this gap between how people actually work and how current artificial intelligence models are evaluated.
The core challenge identified is that today’s strongest models are assessed one query at a time, in controlled scenarios that do not reflect the overlapping workflows of day-to-day corporate work. In contrast, the envisioned artificial intelligence agents must track dependencies between documents, data, and communications, and respond appropriately as priorities shift and new information arrives. That means supporting a continuous flow of actions instead of single-turn question answering or narrowly scoped tasks.
By centering on real work environments where documents, spreadsheets, presentations, and messages are tightly linked, Corpgen aims to push artificial intelligence agents toward more practical utility. The focus is on enabling systems that can manage interwoven responsibilities much like a human knowledge worker, including switching between tasks without losing context. This direction highlights a move from benchmark-centric evaluation to performance in authentic, multifaceted workflows as the standard for future artificial intelligence agents in the workplace.
