OpenAI’s New Enterprise AI Agent Play: A Managed Service, Not a Self-Serve Product
On July 22, OpenAI announced Presence, a managed enterprise AI agent offering that breaks from the company’s usual playbook. You can’t just sign up online and start using it. Presence is delivered through a limited general availability programme, with deployments led by OpenAI’s own Forward Deployed Engineers and a handful of selected global systems integrators.
This is a notable shift for a company that’s built its business on API keys and seat licences. Presence is sold as a project, not a product. Each engagement starts with a single, focused job — resolving a billing dispute, handling an insurance claim, or clearing an employee IT service request.
The agent gets only the knowledge and system access that specific job requires. The customer writes the rules: what the agent can do, when it needs sign-off, and when a human takes over. After launch, Codex reads production sessions and escalations, then proposes changes that the customer’s team tests and approves before rollout.
The Labour Behind the AI Agent
OpenAI’s documentation is refreshingly candid about the work involved. Its help centre lays out a six-stage process: scoping business outcomes, security and privacy review, 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.
That honesty matters. Gartner has warned that more than 40% of agentic AI projects will be cancelled by the end of 2027. The failures, Gartner says, stem from governance, undefined business value, and weak operational discipline — not from 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. New versions go out through controlled rollout with rollback.
Enterprises have spent two years learning 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: Forward Deployed Engineers
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. The title Forward Deployed Engineer is borrowed from Palantir, where it describes staff embedded in customer operations for months at a time. 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. That’s a workable arrangement while volumes are small — and a more complicated one later.
It also raises a question 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. Its case for that language: 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 time in market, and it’s 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 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 — worth holding alongside the word proven.
What OpenAI Hasn’t Disclosed About Presence
Pricing is not published. Implementation scope and cost are set per customer and per deployment. That’s ordinary for enterprise services, but it 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 — 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’re 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. 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.
For a deeper look at how companies are putting agentic AI to work, see our coverage of HP accelerating enterprise workflows with OpenAI Frontier and the broader enterprise AI agent landscape.
