OpenAI has introduced Presence, an enterprise offering designed to help businesses move AI agents from prototypes into live customer service and internal workflows. The service is aimed at qualifying enterprise customers rather than the general market, and it adds hands-on support from OpenAI engineers for deployments that require more than configuration.

The launch addresses a practical weakness in enterprise AI adoption: building an agent is often easier than making it reliable, connected to existing systems, and safe enough for production use. The Decoder, which reported the offering, describes Presence as a step beyond OpenAI’s mainly internal-facing tools for creating customized assistants.

From internal assistants to production deployments

OpenAI’s existing Workspace Agents are positioned primarily around internal use cases. Presence is intended for deployments that interact with customers as well as for more complex internal operations, according to The Decoder.

That distinction matters because production agents face requirements that are less visible in a demonstration. They may need to retrieve information from business systems, follow company-specific rules, handle exceptions, preserve an audit trail, and transfer work to humans when they cannot complete a request. A system used by employees can often tolerate a narrower scope than one operating in a customer-facing channel.

The available evidence does not establish which industries, software integrations, or agent capabilities Presence supports. OpenAI has also not publicly detailed pricing, deployment timelines, service-level commitments, or the specific models available through the offering. Those omissions make it difficult to compare Presence directly with enterprise agent platforms from other vendors.

OpenAI adds engineers to the deployment process

A central part of Presence is the involvement of OpenAI’s Forward Deployed Engineers. For use cases that cannot be handled through the standard offering, these engineers work with customers to identify suitable workflows, connect existing systems, define operating guidelines, and test the agent before launch.

This approach makes Presence more than a self-service software product. It resembles a combination of an agent platform and a specialist implementation service, with OpenAI taking responsibility for helping a customer shape the deployment. That could be important for companies that have identified valuable workflows but lack the internal staff to integrate models with legacy applications, knowledge bases, ticketing systems, or other business tools.

It also creates a limit on scalability. A model in which OpenAI personnel participate deeply in each complex rollout may improve early reliability, but it can be harder to extend across thousands of customers than a product that organizations configure independently. The source material does not say how many engineering teams OpenAI has assigned to Presence or how much implementation work is included.

Evidence is limited, and key safeguards remain unclear

The Decoder is the substantive source for the product description. The available material does not include an OpenAI announcement, technical documentation, customer case study, independent test, or independently verified adoption data. As a result, Presence’s production-readiness positioning should be treated as a description of the offering, not proof that its agents outperform competing systems in live environments.

The Decoder reports that Presence is available to qualifying enterprise customers, but it does not identify those customers or provide deployment figures. There are also no published benchmark results in the supplied evidence covering accuracy, latency, cost, task completion, failure rates, or human escalation.

Compliance is another unresolved area. The Decoder notes that OpenAI refers to trust mechanisms but has not provided specific legal details about how Presence addresses requirements such as the EU AI Act. For enterprise buyers, that gap is significant. Customer-facing agents may process personal data, make recommendations, or take actions that require clear accountability and documented controls.

OpenAI’s implementation support could help customers establish guidelines and testing procedures, but the public information does not show whether those procedures include formal risk assessments, continuous monitoring, model-change controls, or sector-specific compliance reviews. Buyers will need those details before treating Presence as a complete governance solution.

What Presence means for enterprise AI teams

For product and operations leaders, Presence signals a shift in where vendors are competing. The question is no longer only which model can generate the best response. It is also who will connect the model to business systems, define its boundaries, test failure modes, and support the launch after the initial pilot.

The service could appeal to companies that have moved beyond experimentation but do not want to assemble an entire internal agent engineering function. Customer service is a particularly demanding target: an agent must access accurate account or policy information, respect permissions, recognize unusual cases, and hand off smoothly when automation is inappropriate. Internal workflows may offer a more controlled starting point, although they still require access management and dependable integrations.

The cost and operating model will be decisive. A managed engagement with Forward Deployed Engineers may reduce the burden on a customer’s team, but it could also make deployments more expensive or less flexible than self-service alternatives. Enterprises will want to know whether the resulting systems remain portable, how much they depend on OpenAI-specific infrastructure, and who owns the integration and workflow logic if the customer later changes providers.

For competing vendors, Presence reinforces the importance of implementation services around AI agents. Companies selling models, automation platforms, and enterprise software may increasingly bundle technical specialists with their products because reliability depends as much on workflow design and operational controls as on model quality.

What to watch next

The next meaningful signals will be public customer examples, technical documentation, and clearer eligibility requirements for Presence. Those details could show whether the offering is a repeatable product or primarily a bespoke engagement for a limited number of large organizations.

Buyers should also watch for information about supported integrations, data handling, monitoring, human handoff, audit logs, pricing, and service commitments. OpenAI’s position on EU AI Act compliance and other regulatory requirements will be especially important for customer-facing deployments.

Independent evidence will matter as well. Measurements of task completion, escalation rates, operational cost, and failure recovery would give enterprises a stronger basis for evaluating the service than vendor or media descriptions alone.

Creati.ai perspective

Presence is notable because it treats deployment work as part of the AI product rather than leaving every enterprise to solve integration, testing, and governance independently. OpenAI’s decision to involve Forward Deployed Engineers suggests the company recognizes that production agents require operational design, not just access to a capable model.

But the public evidence is too thin to establish how broadly Presence can scale or whether it delivers better reliability than alternatives. Until OpenAI publishes customer results, safeguards, and commercial terms, the offering is best understood as a managed path into enterprise agents—not yet a demonstrated standard for production readiness.