OpenAI released a report itself, "How agents are transforming work." Over the past six months, Codex has been used deeper and deeper inside OpenAI. Look at the metric of merged output tokens from active internal users: the Research department in June 2026 was 56x higher than November 2025, Customer Support 32x higher, Engineering also climbing. Numbers from a company using its own people as the sample tell you most clearly what happened to agents this half-year.
1. From Novelty to Mainstay: What OpenAI's Own Report Says
Reading internal data means first admitting its value: OpenAI is genuinely rolling out Codex across its whole company, not just posting a demo video. That also carries a limit, since OpenAI's sample is "own company plus own product," and how far it generalizes to other companies needs a discount. Even so, a few sets of numbers are worth unpacking.
First set, depth of internal usage. By merged output tokens from active internal users, the Research category grew 56x in half a year, Customer Support 32x, Engineering also climbing. This isn't registration count or login count, it's the merged volume of tokens actually being produced, directly reflecting "how deep the use goes." The watershed for any new tool moving from novelty to production mainstay is the shift from "occasional click" to "daily output." OpenAI has crossed that line.
Second set, task complexity. Per OpenAI's paper and TechTimes reporting, in December 2025, 35.4% of users submitted at least one task that "would take a human ≥1 hour" to complete; by May 2026, that ratio rose to 70.2%. 25.6% of users delegated a task that "would take a human >8 hours." Axios reported the same caliber numbers on June 25, 2026: 70.2% of Codex users made at least one request that saved >1 hour of human labor, 25.6% over 8 hours.
This set of numbers carries more signal than "internal tokens up 56x." It says users are no longer using agents to tweak comments or write function names, they're starting to throw genuinely time-consuming work at them.
But the sample scope must be spelled out. Axios noted in its report: this is based on a 0.1% random sample of individual users, with thresholds estimated by models, not a full-volume statistic. Don't take "70.2%" as a universal industry verdict, it's a sampled ratio. Treat it as a trend signal that "roughly this order of magnitude of agent users is running long tasks," reasonable; cite it as a precise percentage and you're misusing it.
2. Non-Developers Rising: Who's Using Codex to Get Work Done
TechTimes's June 26 piece flagged a trend often missed: non-developers are becoming the main force of enterprise agent growth.
This runs counter to what most people picture. Mention Codex and the default assumption is a programmer tool. Yet OpenAI's report singles out Research and Customer Support as two departments, implicitly signaling that agents long since spilled outside Engineering. Customer service writing ticket summaries, researchers running literature reviews, operations doing data pivots, these tasks share a common shape: "long flow, many steps, but each step has a clear deliverable." This kind of work used to eat human hours; now it's naturally agent territory.
The significance is that agent's market ceiling is far higher than imagined. If it were only a programmer tool, it would cap out at a few million professional users; the moment it can steadily handle long-flow tasks for non-developers, the ceiling swaps out, and tens of millions of knowledge workers become potential users. OpenAI putting "non-developer growth" as a highlight in the report is the company itself confirming this path.
3. Can Long Tasks Go to Agents: It Depends on How Clear the Boundary Is
The data looks good, but landing on "which task should I hand to the agent right now" still depends on reliability boundaries.
Third-party test data is worth referencing. In his 2026 Codex review, zackproser offered an observation: for maintenance-class tasks with clear boundaries, Codex's success rate rose from about 40-60% to about 85-90%. This must be labeled clearly, it's third-party subjective impression, not official data, with sample and method not independently reproduced. It can only serve as corroborating evidence that "for boundary-clear tasks, agents do run stably."
That corroboration happens to nail the key variable: whether the boundary is clear. Maintenance tasks are naturally suited to agents, fixed codebase structure, locatable problems, test cases as objective verification, immediate feedback when something breaks. In this closed-loop feedback environment, agents hitting 85-90% is no surprise.
Flip it around: tasks with blurry boundaries, like "help me think of a growth strategy" or "write a viral piece," agents still can't run stably. Not because the model is dumb, but because these tasks have no objective verification loop; the agent can't judge "is it done" or "is it right," so it just keeps going until tokens run out. A simple way to tell if a task's boundary is clear: can you write down the standard for "done" in one sentence? If yes, the agent has an objective anchor; if no, the agent can only guess, and you may not even know whether it guessed well.
The dividing line is therefore clear: whether a task can be objectively verified and has a fast feedback loop decides whether it fits an agent. Maintenance, customer-service summaries, document cleanup, work where "right is right, wrong is wrong," agents can already carry the main load; strategy, creative, judgment, work where "good or not needs human rating," agents still have to drop back to assist.
4. Three Takeaways
First, don't get fooled by the old impression that "agents are still a novelty." Within half a year, OpenAI's internal Research usage grew 56x and 70.2% of sampled users ran ≥1-hour tasks; agents are crossing the "mainstay" line. Still using "it bugs out sometimes" as an excuse not to learn is like saying "phones are insecure, I won't use one" in 2023.
Second, non-developers are the real growth front for agents. TechTimes flagging this isn't accidental. If you do product, operations, or customer service, this wave of upside points at you, not programmers. To judge which task in your hand can go to an agent, one check is enough: does this task have objective right-or-wrong feedback.
Third, reading agent data must carry sample scope. 70.2% is 0.1% sampled, thresholds model-estimated; 85-90% is third-party subjective impression; 56x is OpenAI's own internal users. Behind every number stands a set of assumptions; carry the assumptions out with the number, or you'll mislead yourself and your team.
The value of OpenAI's report is that it's rare hard data from a vendor using its own whole company as the sample. But data, however hard, can't run your workflow for you. Pick the most time-consuming task in your hand, check whether it has an objective feedback loop, and if it does, hand it to the agent for a real try.
References
- OpenAI, "How agents are transforming work" (official report, Codex internal usage data): https://openai.com/index/how-agents-are-transforming-work
- TechTimes coverage (2026-06-26, non-developers driving enterprise agent growth): https://www.techtimes.com/articles/319120/20260626/openai-codex-data-shows-non-developers-now-driving-enterprise-ai-agent-surge.htm
- Axios coverage (2026-06-25, Codex user long-task ratio and 0.1% sample scope): https://www.axios.com/2026/06/25/codex-agents-growth-openai
- zackproser third-party Codex review (2026, maintenance-task success-rate impression): https://zackproser.com/blog/openai-codex-review-2026