OpenAI reveals a simple way to let big companies make AI agents of their own

by: Marty Spargo

Last updated: February 10, 2026

5 min read

Table of Contents

OpenAI is expanding its enterprise offerings with a platform that lets companies build, deploy, and manage their own AI agents. These agents can function like digital coworkers, integrating directly into corporate workflows, software, and data systems.

The new platform, called Frontier, aims to turn AI from a series of experimental pilots into a managed workforce. Large organizations can now connect advanced models to real business processes without compromising compliance or security.

Turning AI into managed coworkers

Frontier is designed for enterprises to orchestrate multiple AI agents rather than rely on a single assistant. Companies can create agents that handle tasks like drafting contracts, triaging support tickets, or coordinating logistics, all while remaining centrally governed.

Each agent can generate content, execute workflows, and make constrained decisions inside business applications. Essentially, Frontier adds a layer of operational infrastructure that sits alongside existing software.

Moving from experimentation to production

OpenAI positions Frontier as a bridge from AI prototypes to production-scale automation. The platform is designed for companies that want agents to operate across departments such as finance, HR, and customer service.

Rather than a single product, Frontier acts as a foundation for an AI agent ecosystem. Analysts describe it as a potential rival to traditional enterprise software suites, providing a more intelligent layer for operations.

From APIs to an orchestration layer

Frontier builds on OpenAI’s Responses API, which replaced the older Assistants API earlier this year. This API is designed for teams that want to create agentic applications rather than simple prompt-and-response bots.

AI agents AI assistants support human intelligence — *Source: Depositphotos*

The orchestration layer connects agents to corporate systems, eliminating the need for custom middleware between SaaS tools. Companies can define workflows and policies in Frontier, letting agents manage routine coordination automatically.

How enterprises will actually use agents

Companies can configure agents with roles, permissions, and performance metrics. Managers can monitor activity across departments, tracking which tasks require human intervention and which can be fully automated.

For example, a contract review agent could flag unusual clauses for human review while routine agreements are processed automatically. A support agent could summarize emails and propose responses, streamlining customer service without replacing human judgment.

HR-style control and governance

Frontier treats each agent as a managed entity with defined access rights. This HR-like structure allows organizations to see which agents are active, what they are doing, and how often they require supervision.

The platform embeds agents into everyday tools like email, CRM systems, and internal dashboards. This approach helps integrate AI quietly into critical business processes such as invoice processing, compliance, and approvals.

Early adoption and competitive stakes

OpenAI is moving aggressively into the corporate AI market, responding to rivals like Anthropic. Early adopters include financial institutions and industrial firms that want agents to handle complex, regulated workflows.

The platform integrates into existing enterprise systems rather than replacing them. Companies like Uber and Thermo Fisher are testing Frontier agents to coordinate tasks across multiple tools, signaling a high-stakes bet on standardizing AI operations.

Data, governance, and Snowflake partnership

Trusting agents with sensitive data is a key concern for enterprises. OpenAI and Snowflake announced a roughly $200 million multi-year partnership to enable OpenAI models within Snowflake’s AI Data Cloud, which Snowflake says will let customers run model workloads near their data.

OpenAI and coverage in industry press say companies such as Uber and Thermo Fisher are piloting Frontier agents to coordinate cross-tool workflows, a sign of the platform’s ambition to standardize AI operations.

Customizing and training your AI agents

Frontier lets companies tailor agents to their unique workflows and policies. Businesses can define how agents reason, which tools they access, and what outputs are acceptable for different tasks.

Training agents on internal data allows them to understand company-specific language, procedures, and priorities. This ensures the AI behaves consistently and produces actionable results without constant supervision.

Source: aileenchik/Depositphotos

Custom agents can also evolve over time, learning from feedback and improving their accuracy. Enterprises can monitor performance metrics, adjust parameters, and refine workflows to maximize efficiency and reliability.

Ideas for what companies might try first

Enterprises could start with a contract review agent to streamline legal workflows. They might also create a customer support agent to draft responses and flag complex issues for humans.

Other early experiments could include logistics coordination agents or internal research assistants. Teams can explore agent workflows gradually, learning how AI can complement rather than replace employees.

Experimenting with cross-department agents could also uncover unexpected efficiencies. For instance, finance, HR, and operations could each have specialized agents sharing insights through Frontier’s orchestration layer.

The enterprise AI era is here.

OpenAI’s Frontier platform signals a shift in how businesses adopt AI. Instead of isolated tools, companies now have an operating layer in which AI agents act as trusted coworkers.

This approach combines automation, governance, and integration into existing workflows. Organizations can experiment safely, scale efficiently, and unlock productivity gains across departments.

Frontier also sets the stage for future innovations. Analysts say that as companies deploy more agents, collaboration models may shift, and organizations could see more integrated human-machine workflows that change how tasks are assigned and supervised.

This article was made with AI assistance and human editing.