This is happening for AI leaders across every industry, and the pressure to catch up is increasing. What’s slowing them down isn’t model intelligence, it’s how agents are built and run in their organizations.

Today, we’re introducing Frontier, a new platform that helps enterprises build, deploy, and manage AI agents that can do real work. Frontier gives agents the same skills people need to succeed at work: shared context, onboarding, hands-on learning with feedback, and clear permissions and boundaries. That’s how teams move beyond isolated use cases to AI coworkers that work across the business.

HP⁠(opens in a new window), Intuit⁠(opens in a new window), Oracle⁠(opens in a new window), State Farm⁠(opens in a new window), Thermo Fisher⁠(opens in a new window), and Uber⁠(opens in a new window) are among the first to adopt Frontier, and dozens of existing customers–including BBVA⁠(opens in a new window), Cisco⁠(opens in a new window), and T-Mobile⁠(opens in a new window)–have already piloted Frontier’s approach to power some of their most complex and valuable AI work.

Companies are already overwhelmed with the disconnected systems and governance spread across clouds, data platforms, and applications. AI made that fragmentation more visible, and in many cases, more acute. Agents are now getting deployed everywhere, and each one is isolated in what it can see and do. Every new agent can end up adding complexity instead of helping, because it doesn’t have enough context to do the job well.