OpenAI Launches Frontier to Turn AI Agents Into Enterprise Coworkers

Despite the clear advantages AI brings to enterprises, many organizations still struggle with how agents are built, deployed, and governed.

AI fragmentation across clouds, data platforms, and applications has made complexity more visible, with agents often deployed in isolation and lacking shared context.

This fragmentation has added operational overhead and created what OpenAI describes as an “AI opportunity gap” between what modern models can do and what organizations can realistically deploy at scale.

From isolated pilots to AI coworkers

To address this gap, OpenAI has introduced Frontier, a new platform designed to help enterprises build, deploy, and manage AI agents that do real work. Frontier is positioned as a step beyond isolated pilots, enabling AI coworkers that can operate across an entire business rather than within narrow, disconnected workflows.

Early Frontier adopters include HP, Intuit, Oracle, State Farm, Thermo Fisher, and Uber. Existing customers such as BBVA, Cisco, and T-Mobile have already piloted the platform.

How Frontier is built for enterprise scale

Frontier is built around how enterprises already scale people, applying those same principles to AI coworkers. OpenAI says these agents gain shared organizational context, onboarding and institutional knowledge, the ability to learn through experience and feedback, and clear identities with defined permissions and boundaries.

The platform focuses on understanding how work happens across systems, enabling agents to plan, act, and solve real-world problems using tools and computers. Continuous evaluation and feedback help improve quality over time, while identity, permissions, and governance aim to ensure trust in production environments.

Connecting siloed systems into a shared context

A core part of Frontier is its ability to connect siloed systems such as data warehouses, CRMs, ticketing tools, and internal applications into a shared intelligence layer. This unified context gives AI coworkers a consistent understanding of workflows and business outcomes, reducing the friction that often limits enterprise AI deployments.

Agents built on Frontier can work with files and code, use tools and applications, and build memory from past interactions to improve performance over time. They can run across local environments, enterprise clouds, or OpenAI-hosted runtimes, with Frontier prioritizing low-latency access to OpenAI models for time-sensitive tasks.

Security, governance, and real-world deployment

Security and governance play a central role in Frontier’s design. Each AI coworker has a unique identity, with explicit permissions and enforced guardrails, making the platform suitable for sensitive and regulated environments.

OpenAI also pairs customers with Forward Deployed Engineers to help design, deploy, and scale agents in production. These engineers provide a direct feedback loop between enterprise deployments and OpenAI research, feeding real-world insights back into both the platform and underlying models.

Open standards and partner ecosystem

Frontier is built on open standards, allowing enterprises to integrate existing data sources, applications, and even third-party agents. OpenAI is also working with Frontier partners, including Abridge, Clay, Ambience, Decagon, Harvey, and Sierra.

Frontier is available today to a limited set of customers, with broader availability planned over the coming months. OpenAI frames the challenge not as whether AI will change work, but how quickly organizations can turn agents into a lasting competitive advantage.

Alongside Frontier, OpenAI is retiring older ChatGPT models and has recently partnered with Snowflake to bring AI more deeply into enterprise data workflows. The company has also launched the Codex app for macOS, expanding its tooling for developers and enterprise users alike.