The newest way to buy enterprise AI agents from OpenAI does not involve buying anything online. OpenAI Presence, announced on July 22, is a managed product delivered through a limited general availability programme, and the company states plainly that it is not yet available as a self-serve product. Deployments are led by OpenAI’s own Forward Deployed Engineers and a set of selected global systems integrators.
That is a departure for a business that has largely run on API keys and seat licences. Presence is sold as a project rather than a product. Each engagement starts with a single job, such as resolving a billing dispute, handling an insurance claim, or clearing an employee IT service request.
The agent is given only the knowledge and system access that the job requires, and the customer writes the rules governing what it can do, when it needs sign-off, and when a person takes over. After launch, Codex reads production sessions and escalations, then proposes changes the customer’s team tests and approves before rollout.
OpenAI’s documentation is unusually candid about the labour involved. Its help centre sets out a six-stage process running from scoping business outcomes, through security, privacy and legal review, simulation and acceptance testing, staged rollout, and post-launch iteration. A Presence agent, it says, does not become production-ready simply by ingesting documents.
The problem this is built to solve is real
The managed model is easy to read cynically, and harder to dismiss on the evidence. Gartner has warned that more than 40% of agentic AI projects will be cancelled by the end of 2027, attributing the failures to governance, undefined business value and weak operational discipline rather than to model capability.
Almost everything Presence bundles is aimed squarely at that diagnosis. Simulations and graders test whether an agent reached the right outcome, followed policy, used its tools correctly and escalated when it should, before anyone outside the company speaks to it. Guardrails intervene when an interaction moves past defined boundaries. Session records and action histories give reviewers something to audit. Escalation paths hand a person structured context rather than a cold transcript, and new versions go out through controlled rollout with rollback.
Enterprises have spent two years discovering that the hard part of a production agent sits in integration, permissions and change management. A vendor that sends engineers to do that work is responding to what buyers have actually been failing at, rather than shipping another dashboard and calling the gap a customer problem.
Where the constraint sits
The trade-off shows up in the eligibility criteria. Access, OpenAI says, depends on workflow fit, implementation readiness and available delivery capacity.
Delivery capacity is a consulting constraint. Software scales; engineers cleared into a bank’s core systems do not. Forward Deployed Engineer is a title borrowed from Palantir, where it describes staff embedded in customer operations for months at a time, and the economics attached to it look nothing like the economics of metered inference. By putting its own FDEs and named partners at the front of every deployment, OpenAI has stepped into the layer of the market occupied by the integrators it will also rely on to scale, which is a workable arrangement while volumes are small and a more complicated one later.
It also puts a question on the table for anyone scoping a contract. When the model vendor is also the implementation partner, the lines of accountability for a policy misapplied in production need to be written down rather than assumed.
The enterprise AI agents on display are still early
OpenAI describes Presence as battle-tested, and its case for that language is that the product was assembled from years of deploying agents with enterprise customers before it was packaged and named. The claim is about accumulated practice rather than about the product’s time in market, and it is a reasonable one to make.
The strongest single proof point is OpenAI’s own English-language phone support line, 1-888-GPT-0090. The company says the agent met or exceeded its internal benchmarks for frontline human support within weeks, now resolves 75% of inbound issues without human assistance, and cut human handoffs by 15 percentage points in ten days through the Codex improvement loop. Those are OpenAI’s figures, measured against OpenAI’s own grading criteria, on OpenAI’s own channel. The transparency is welcome, but the numbers are not independently verified.
The three named customers sit earlier in the cycle than the launch framing implies. BBVA is exploring voice support for everyday banking in Mexico. SoftBank is testing Japanese-language conversations. IAG is exploring support during high-demand events such as severe weather. Daniel Ordaz, head of AI transformation at BBVA Mexico, describes the bank as a design partner helping shape and refine voice experiences for financial customer service. Design partners are normal and useful at limited GA. None of the three, though, is presented as running Presence at scale, which is worth holding alongside the word proven.
What has not been disclosed
Pricing is not published. Implementation scope and cost are set per customer and per deployment, which is ordinary for enterprise services and still leaves buyers without a public reference point for cost per resolved contact against an incumbent contact-centre vendor.
The model is not named. Presence uses OpenAI models, the documentation says, with configuration selected for the workflow and subject to change as that workflow evolves. That flexibility is defensible engineering, because pinning a production agent to a frozen model version ages badly. Teams that have spent the past year building evaluation suites against specific versions will nonetheless want the contract to say what they are being held to when the configuration moves.
Channel support during limited GA covers voice or chat, with contact-centre integration, routing, authentication and handoff design confirmed deployment by deployment. Data handling follows the same pattern, with the signed architecture and contract treated as the governing record rather than any published policy.
Presence sits apart from ChatGPT Workspace Agents, which remain the self-serve path for teams building inside ChatGPT and Slack, while voice customers keep API access to OpenAI’s frontier models. The company now offers broadly the same capability three ways, separated less by what the technology can do than by who does the work.
That leaves buyers choosing on delivery capacity as much as on model capability, and on OpenAI’s own account, delivery capacity is the part being rationed.
See also: HP accelerates enterprise workflows with OpenAI Frontier
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