Ask an AI agent to draft an email recapping your latest internal sync, and it won’t break a sweat. Ask it to find you the cheapest flight to Denver, and you’ve got a shortlist in seconds. But ask it to carry a product request through to release, figuring out whether last week’s Slack approval still applies to this week’s changes, and it will likely stall.

The models behind today's agents have absorbed most of what the public internet knows about tasks like drafting an email or booking a flight. The prevailing assumption is that enterprise automation is now a waiting game: the models keep improving, and agents will eventually work their way into every company.

That assumption is flawed: no model, no matter how capable, knows the ins and outs of how a company operates. It does not know that a claims analyst at one insurer checks an internal financial dashboard, reads the related email thread, and then updates a record in the CRM, in that exact order, with exceptions that live in nobody's documentation. That knowledge is spread across dozens of private tools and the daily habits of the people who use them. Until it is captured somewhere an agent can use, enterprise agents will keep stalling a few steps in.

Akshat Kannan and William Zhang are building Zeroset, which recreates workflows and states of how enterprises run so agents can operate inside it. Today, Zeroset announced a $5.2 million pre-seed round co-led by Gradient and 2048 Ventures, with participation from Leblon Capital.