What Are OpenAI Dots And Do You Need Them?

OpenAI recently introduced Dots, a new class of persistent AI agents designed to keep working after a user leaves a chat session. Powered by GPT-6 Astra, each Dot gets its own cloud computer and browser, can connect to more than 4,000 apps through OpenAI’s plugin ecosystem, and can continue pursuing assigned goals between conversations. Users can reach the same Dot through ChatGPT, Slack, Microsoft Teams and voice, with context carrying from one interaction to the next.

For business users, the interesting part is that OpenAI is trying to turn AI from something you consult into something you assign. A Dot can watch a project, check for changes, run background work, use connected software and return when a decision needs human input. OpenAI’s own guidance tells users to begin with work they want a Dot to “keep track of,” rather than treating each conversation as a fresh request.

But persistent AI is not a novel idea. Anthropic’s Claude Cowork is pushing agents deeper into desktop and knowledge work, while Meta’s Muse is built around completing multi-step tasks from its own cloud environment. Other agentic systems are moving in the same direction, from research and coding to browser use and workflow automation.

The competitive landscape is shifting from which model can generate the best answer to which company can build an agent people will trust with an ongoing job. For OpenAI, Dots are an attempt to turn the company’s enormous user base into a persistent work layer that stays involved after the conversation ends.

What Makes A Dot Different?

The simplest way to understand Dots is to stop thinking about a typical chat-based operation or even a longer-form project or coding task. A normal LLM interaction has a natural stopping point. Ask for a market analysis, receive the analysis, perhaps refine it, then move on. Instead, consider agentic Dots to be given an ongoing responsibility and operate in a mostly-autonomous mode.