Introducing OpenAI Frontier

Introducing OpenAI Frontier

AI has let teams take on things they used to talk about but never execute. In fact, 75% of enterprise workers say AI helped them do tasks they couldn't do before. We're hearing this from every department, not just technical teams. The way work gets done has changed, and enterprises are starting to feel it in big ways.

We've seen this in action with over 1 million businesses over the past few years. At a major semiconductor manufacturer, agents reduced chip optimization work from six weeks to one day. A global investment company deployed agents end-to-end across the sales process to open up over 90% more time for salespeople to spend with customers. And, at a large energy producer, agents helped increase output by up to 5%, which adds over a billion in additional revenue.

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.

The AI opportunity gap

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.

As agents have gotten more capable, the opportunity gap between what models can do and what teams can actually deploy has grown. The gap isn't just driven by technology. Teams are still building the knowledge to move agents past early pilots and into real work as fast as AI is improving. At OpenAI alone, something new ships roughly every three days, and that pace is getting faster.¹Keeping up means balancing control and experimentation, and that's hard to get right.

Enterprises are feeling the pressure to figure this out now, because the gap between early leaders and everyone else is growing fast.

OpenAI Frontier

We've learned that teams don't just need better tools that solve pieces of the puzzle. They needed help getting agents into production with an end-to-end approach to build, deploy, and manage agents.

We started by looking at how enterprises already scale people. They create onboarding processes. They teach institutional knowledge and internal language. They allow learning through experience and improve performance through feedback. They grant access to the right systems and set boundaries. AI coworkers need the same things.

For AI coworkers to actually work, a few things matter:

• They need to understand how work actually gets done across systems.
• They need access to a computer and tools to plan, act, and solve real-world problems.
• They need to understand what good looks like, so quality improves as the work changes.
• And they need an identity, permissions, and boundaries teams can trust.

And all of this has to work across many systems, often spread across multiple clouds. Frontier works with the systems teams already have, without forcing them to replatform. You can bring your existing data and AI together where it lives - as well as integrate the applications you already use-using open standards. That means no new formats and no abandoning agents or applications you've already deployed.

The superpower of this approach is that AI coworkers are accessible and useful through any interface, not trapped behind a single UI or application. They can partner with people wherever work happens, whether that is interacting with ChatGPT, through workflows with Atlas, or inside existing business applications. This is true whether agents are developed in-house, acquired from OpenAI, or are integrated from other vendors you already use.

Understand the work

Every effective employee knows how the business works, where information lives, and what good decisions look like.

Frontier connects siloed data warehouses, CRM systems, ticketing tools, and internal applications to give AI coworkers that same shared business context. They understand how information flows, where decisions happen, and what outcomes matter. It becomes a semantic layer for the enterprise that all AI coworkers can reference to operate and communicate effectively.

Plan, act, and solve problems

With shared context in place, agents need to be able to actually do the work.

Teams across the organization, technical and non-technical, can use Frontier to hire AI coworkers who take on many of the tasks people already do on a computer. Frontier gives AI coworkers the ability to reason over data and complete complex tasks, like working with files, running code, and using tools, all in a dependable, openagent executionenvironment. As AI coworkers operate, they build memories, turning past interactions into useful context that improves performance over time.

Once deployed, AI coworkers can run across local environments, enterprise cloud infrastructure, and OpenAI-hosted runtimes without forcing teams to reinvent how work gets done. And for time-sensitive work, Frontier prioritizes low-latency access to OpenAI's models so responses stay quick and consistent.

Improve quality on real work

For agents to be useful over time, they need to learn from experience, just like people do.

Built-in ways to evaluate and optimizeperformance make it clear to human managers and AI coworkers what's working and what isn't, so good behaviors improve over time. Over time, AI coworkers learn what good looks like and get better at the work that matters most.

This is how agents move from impressive demos to dependable teammates.

Identity, permissions, and boundaries

Frontier makes sure AI coworkers operate within clear boundaries. Each AI coworker has its own identity, with explicit permissions and guardrails. That makes it possible to use them confidently in sensitive and regulated environments. Enterprise security and governance⁠ are built in, so teams can scale without losing control.

Combining technology with know-how

Closing the opportunity gap isn't just a technology problem.

We've worked closely with large enterprises on complex AI deployments for years, so we've seen what works and what doesn't. Now we're helping teams apply those lessons to their toughest problems.

We pair OpenAI Forward Deployed Engineers (FDEs) with your teams, working side by side to help you develop the best practices to build and run agents in production.

The FDEs also give teams a direct connection to OpenAI Research. As you deploy agents, we learn not just how to improve your systems around the model. We also learn how the models themselves need to evolve to be more useful for your work. That feedback loop, from your business problem to deployment to research and back, helps both sides move faster.

Opening the AI ecosystem

AI works best in the enterprise when the platform and the applications work together. Because Frontier is built on open standards, software teams can plug in and build agents that benefit from the same shared context.

This matters because many agent apps fail for a simple reason: they don't have the context they need. Data is scattered across systems, permissions are complex, and each integration becomes a one-off project. Frontier makes it easier for applications to access the business context they need (with the right controls), so they can work inside real workflows from day one. For enterprises, that means faster rollouts without a long integration cycle every time.

We're also working with a small group of Frontier Partners-AI-native builders like Abridge⁠ (opens in a new window), Clay⁠ (opens in a new window), Ambience⁠ (opens in a new window), Decagon⁠ (opens in a new window), Harvey⁠ (opens in a new window), and Sierra⁠ (opens in a new window)-who are committing to go deep with Frontier. They'll work closely with OpenAI to learn what customers need, design solutions, and support deployment. Over time, we'll expand the program and welcome more builders focused on enterprise AI.

Let's build

The question now isn't whether AI will change how work gets done, but how quickly your organization can turn agents into a real advantage.

Frontier is available today to a limited set of customers, with broader availability coming over the next few months. If you want to explore working with us, reach out to your OpenAI team.

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