OpenAI employees are moving from chatbots to agents as their primary AI tool

OpenAI employees are increasingly moving away from using ChatGPT for one-off questions — instead, they're delegating Codex agents to handle complex multi-step tasks that consume considerable time. According to the company, a similar trend is being observed across other organizations and among individual users, though it is currently less pronounced.

From Question to Task

The fundamental difference between a chatbot and an agent lies not in model power, but in interaction architecture. A chatbot operates in a "question-answer" mode: a person formulates a query, receives a response, then formulates the next one. This is productive for exploring ideas or quick lookups — but inefficient for long tasks with a clear endpoint.

An agent works differently. It receives a task with a description of the desired outcome and independently breaks it into steps: requesting necessary data, writing and running code, checking intermediate results, and fixing errors when needed. Meanwhile, the person can focus on other work — and returns to a finished result rather than another question. This is precisely why OpenAI employees are switching from ChatGPT to Codex: the agent takes on labor-intensive tasks that would require dozens of iterations in dialogue mode.

Beyond Developers

Notably, the shift is visible not only among software engineers. Non-technical staff — managers, analysts, operations specialists — are also beginning to delegate tasks to agents. This signals a lowered barrier to entry: working with agents no longer requires understanding their internal mechanics.

Among tasks increasingly delegated to agents:

• preparing analytical reports on large datasets
• researching and aggregating information from multiple sources
• automating repetitive workflows
• analyzing large documents with structured data extraction
• writing, testing, and refactoring code across entire projects

Agent mode is also convenient for asynchronicity: an employee launches a task, switches to other work, and returns to a finished result — rather than maintaining a continuous dialogue.

The Trend Extends Beyond One Company

According to OpenAI's observations, a similar shift is occurring in other organizations, though currently in a less pronounced form. Many teams are still becoming acquainted with agent tools, and a full transition lies ahead — but the direction is set. Agent format is especially valuable for tasks lasting from an hour to several days: too long for chatbot dialogue, but not requiring a full automated pipeline.

This fits into broader industry dynamics. Microsoft is advancing Copilot Agents for enterprise automation, Google is developing Project Astra and Gemini agents, and Anthropic is expanding computer use capabilities. The market is shifting from "talk to AI" to "delegate to AI" — from conversational assistant to task executor. The difference is roughly like that between a consultant who gives advice and an employee who does the work.

What This Means

If the trend continues, the AI interaction model will change fundamentally. Instead of "ask and get an answer," the primary format will become "set a task and get a result." This requires different skills: agents need goal formulation, context setting, and readiness criteria — not step-by-step dialogue. The fact that this transition is already happening inside OpenAI — one of the world's most technologically advanced teams — suggests this is not a hypothetical possibility but an emerging practice.