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Our vision for the autonomous enterprise

The first wave of generative AI changed what machines know: they can write, reason, and hold a conversation. In the enterprise, though, knowledge is cheap. What's missing is agency, the ability to act.

Most companies have a gap between having an insight and acting on it. The work is spread across legacy software and disconnected web apps, and getting anything done means moving between them by hand. Most AI sits on the sidelines as a co-pilot: it watches the work and comments on it instead of doing it.

Our research is focused on closing that gap. We think the next step isn't better conversation, it's software that uses other software on its own. We are building an operating system for AI agents: a layer that navigates, reasons, and acts inside the systems a company already runs.

It rests on three technical foundations:

1. Computer use, not just APIs

Relying on APIs is a bottleneck. They are brittle, incomplete, and often missing entirely for the legacy systems that still run most of the economy. Waiting for someone to build "connectors" is a losing strategy.

Our agents work the screen directly. They read the pixels and the UI the way a person does, which lets them operate any system end to end, whether desktop, mobile, or web. If a task can be done on a screen, our agents can learn to do it. That is more than automation: it is a way to drive software that was never built to be driven by a machine.

2. Deployment you control

High-stakes work can't be handed to a black box. As AI takes on more of how a company runs, the data, the models, and the execution all need to stay under the customer's control.

So we built the whole stack ourselves: the orchestration, the environment management, and the safety guardrails a real deployment needs. Unlike the big centralized providers, we let you choose where it runs, from multi-tenant on our managed cloud to a regional or on-premise VPC. Sensitive workflows stay yours and never leave your security boundary.

3. A learning flywheel

Fixed datasets only get you so far. The systems that improve are the ones that learn from actually doing the work.

We do that with a Synthetic Environment Factory. We start from strong open-weight models as a reasoning base, then post-train them hard inside millions of simulated enterprise scenarios. That automated curriculum puts our agents through the edge cases of corporate bureaucracy at a scale no human-built training set could cover.

Just as AlphaGo improved by playing itself, our agents get better through agentic reinforcement learning. Every task they run feeds back into the models, so the whole system gets faster and cheaper the more it is used.

Research Meets Production

Our research and product teams work in a tight loop, so a research result turns into something customers can use quickly.

We back open research, but general-purpose benchmarks rarely match the mess of real enterprise systems. So we test our models and agents against our own benchmarks: real cross-system workflows. Because we own the whole chain, from our inference cluster to the execution engine, we can roll out improvements right away and keep our agents at the edge of what works.

The Path Forward

H isn't built to replace people. It's built to take the rote, manual work off their plates. By building a system that can see, reason, and act inside the software a company already uses, we are making the autonomous enterprise real.

A company doesn't run on its software alone anymore. It runs on the systems that act on top of it. Models have learned to think. The next thing is systems that learn to act.