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Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →OpenAI is betting that businesses will move beyond using AI to draft and answer questions and let agents carry out multi-step work in company systems. Its products now span an enterprise agent platform, voice and chat deployments, and a developer API. But OpenAI’s usage figures show growing adoption of its tools—not that agents have reliably completed high-stakes work or delivered measurable productivity gains.
What can AI agents actually do at work?
In OpenAI’s framing, an agent uses context and tools to find information, edit files, and carry out a sequence of tasks. It may work autonomously within defined limits or pause for a person to review or approve an action. That is a shift from asking an AI assistant for an answer to assigning it a job with a desired outcome.
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Examples OpenAI gives include customer-support agents that answer questions and resolve issues, sales agents that work through voice or chat, and agents that use internal systems to complete approved actions. In software development, agents can work with code and files, run code, and use testable outputs to check their work. The underlying pattern can apply to other fields, but the work must still be specified well enough for the agent to know what to do and for the organization to judge whether it did it correctly.
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OpenAI’s Enterprise Signals analysis, published in 2026 and covering data through June, reports substantial growth in agent-related use inside its enterprise customer base. OpenAI says Codex accounted for 64% of combined Codex and ChatGPT output tokens among enterprise customers in June. For this measure, OpenAI characterizes Codex tokens as agentic use. Tokens indicate activity in its products; they do not show how many tasks were completed successfully or what business value resulted.
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The same report compares customers it calls “frontier firms”—the top 10% by monthly AI usage—with “typical firms,” those between the 45th and 55th percentiles. OpenAI says frontier firms generated 8.3 times as many output tokens per active user as typical firms in June 2026, up from 2.6 times in January. This describes a gap in product usage, not a measured gap in productivity.
OpenAI also reports that weekly active enterprise Codex users grew between February and its 2026 reporting period by 108 times in legal, 41 times in sales, 41 times in recruiting, 26 times in marketing, and 5 times in engineering. These are relative increases in active users, not rates of task completion or return on investment.
Other figures in the report provide context for what employees do with AI. Across a sample of more than 10 million messages, OpenAI says writing was the most common ChatGPT use, while coding and system or agent operations together made up nearly 75% of agentic messages. It also reports that 21% of active users at frontier firms used Plugins weekly, compared with 9% at typical firms. Neither measure establishes that the work was correct or produced a business outcome.
OpenAI’s downloadable A business leader’s guide to working with agents also reports survey figures attributed to other organizations: 79% of senior executives said agents were already in use in their companies, and two-thirds believed agents would reshape the workplace more than the internet did, both attributed to PwC’s 2025 AI Agent Survey. The guide says 86% expect to be operational by 2027, attributing that figure to PagerDuty; the excerpt does not identify a survey year for that statistic. These are figures as presented in OpenAI’s guide, not direct measures of deployment quality or outcomes.
What OpenAI is offering enterprises
OpenAI’s product strategy covers three different ways to build or deploy agents. The offerings differ in audience and role, and their descriptions are OpenAI’s own; they are not independent evaluations of how well the products perform.
| Offering | Role | What OpenAI describes | Availability context |
|---|---|---|---|
| Frontier | Enterprise platform for building, deploying, and managing agents | Shared business context across systems such as data warehouses, CRM, ticketing, and internal applications; an execution environment for files, code, and tools; performance evaluation; and agent identities with permissions and guardrails. | Announced in February 2026. OpenAI named HP, Intuit, Oracle, State Farm, Thermo Fisher, and Uber as early adopters, and said BBVA, Cisco, and T-Mobile had piloted its approach. |
| Presence | Real-time voice and chat workflows | Agents that answer questions, resolve issues, use company systems, take approved actions, and escalate to people; OpenAI describes use cases including customer support, outbound sales, and high-risk internal workflows. | Introduced in July 2026. OpenAI describes deployments as starting with a specific job and configured policies, access, approvals, and escalation rules. |
| Agents API | Developer infrastructure for building agents | A harness for context management, tool use, and subagent coordination, plus support for long-running agents that work with files, run code, and save intermediate results. | Introduced in public beta in September 2026. Beta status and access may change. |
OpenAI’s Enterprise Signals article also says ChatGPT Work extends agentic capabilities beyond developers. Product access and administrator controls are subject to change: OpenAI’s Help Center release notes describe evolving plan eligibility and workspace settings, as well as event-triggered workflows for eligible users with approved app access and admin controls. Organizations should check current plan eligibility and configuration before designing a deployment around a specific feature.
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Can you trust an agent to take actions in company systems?
Trust should be earned for a defined workflow, not inferred from a convincing demo or a general claim of autonomy. An agent that can read a knowledge base is different from one that can change customer records, contact customers, or trigger a business process. Each additional permission increases the consequences of a misunderstanding or error.
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OpenAI’s July 22, 2026 Presence announcement put the production challenge this way: “The challenge for enterprises is no longer proving that AI agents can work, it’s making them reliable enough to do high-value work in production.” That is OpenAI’s characterization of the market, not independent evidence that its agents—or agents generally—are dependable for every business task.
OpenAI says software agents advanced earlier because software work tends to have clearer context and outputs that can be tested. General knowledge work can be harder to specify, may lack complete context, and may not have an objective way to verify a result. For a task with ambiguous instructions or a consequential decision, an agent needs more supervision and stronger checks than a task whose output can be automatically tested.
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A practical deployment begins by turning a broad ambition—such as “handle customer issues”—into a bounded job with known inputs, permitted actions, and an acceptable result. OpenAI’s Frontier description emphasizes shared context, evaluation, and explicit permissions; its Presence description adds approved actions, policies, and escalation. These are sensible control areas to assess, but the products do not make governance automatic.
- Choose one workflow and define success. Specify what starts the task, what information is relevant, what a correct completion looks like, and what conditions should stop or redirect the agent.
- Connect only the necessary context. Identify the systems, files, and knowledge sources the agent needs, and determine how that information is kept current. Missing or stale context can undermine otherwise capable behavior.
- Scope permissions to the job. Decide whether the agent may only recommend an action, prepare a change for approval, or make a limited change itself. Separate low-risk steps from actions that affect customers, money, access, or official records.
- Set approval and escalation rules. Define when a person must approve an action, when the agent should stop, and which cases must go to a human. Staff need a usable way to inspect the work and take over the workflow.
- Test before production and monitor afterward. Evaluate representative cases, including incomplete information and likely failure cases. Track whether tasks meet the defined success criteria, review logs and exceptions, and revise access or instructions when results fall short.
- Budget for operations, not just the model. Long-running tasks can require more computation. Compare total workflow cost and response time, along with the work needed to integrate, evaluate, monitor, and support the agent.
When comparing agent platforms or implementations, examine context and integrations, action permissions, verification and monitoring, human takeover, deployment environment and identity controls, and total cost and latency. Keep evidence types separate: vendor-reported usage, customer adoption statements, controlled task evaluations, and independently measured business outcomes answer different questions.
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What OpenAI’s enterprise bet does—and does not—show
OpenAI is positioning agents as a way to delegate work, and its product releases and enterprise usage analysis show that it is investing in that direction and reporting wider use among its customers. The usage figures do not establish that enterprises are ready to delegate open-ended or high-stakes decisions without oversight, or that higher token use translates into equivalent productivity gains. For a buyer, the meaningful test is whether a narrowly defined workflow can use the right context, act within justified permissions, produce verifiable results, and hand control to a person when needed.
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