ChatGPT's Dot: one more personal AI agent

On September 29, OpenAI released a new feature at DevDay: Dot.

Three weeks earlier, Meta had just launched Muse. Both products belong to a kind of AI agent that’s been very hot lately:

they have their own runtime environment, can connect to other apps, remember long-term context, and can keep working when you’re not watching the chat box.

I set up a job-search Dot myself, and what I most wanted to figure out first was:

how exactly is this different from the Chat, Work, Scheduled Tasks and Codex that already exist?

01 Understanding Dot through one example

Suppose I’m looking for an AI PM job.

In ordinary Chat, I might ask:

“Are there any new AI PM roles today that suit me?”

It searches, analyzes, and answers me.

In Work, I can hand it a more complete piece of work:

“Research AI PM roles in the UAE from the past week, filter and deduplicate them based on my background, and put together a list I can apply to.”

It can break down the steps itself, work for a while, and deliver the result at the end.

A Scheduled Task handles fixed triggers:

“Search for new AI PM roles once every day at 9 a.m.”

Codex is mainly responsible for software engineering.

If the task involves code, tests or a repo, it can go and do it directly.

Dot adds a layer of persistent state.

I can hand “AI PM job search” to it as a long-running thing.

It knows what I’ve already looked at, what I’ve applied to, which companies I’m interviewing with, and which recruiters are waiting for replies, and it can keep using Gmail, Calendar or other connected information.

When new roles come up the next day, I don’t need to explain the background all over again.

When a recruiter emails, it also knows which earlier application the email relates to.

OpenAI calls this capability ongoing responsibility.

02 What does Dot itself include?

Dot is powered by GPT-6 Astra and has its own cloud computer and browser.

It can retain context and memory long term, connect to authorized apps, complete tasks in the background, run recurring tasks, proactively look through connected information, and use Work or create Codex tasks as needed.

You can think of it as a long-lived agent identity.

Chat, mobile, Slack, Call and so on are more like different doors through which you talk to it.

03 Always-on doesn’t mean “scanning everything in real time”

This is something I only noticed after trying it myself.

When I created the task for my job-search Dot, in the end I still had to tell it:

“Check for new roles once a day.”

The reason is simple: AI needs a channel to know something has changed in the outside world.

For example:

• the time comes → search today’s new roles
• a new email arrives in Gmail → check whether it’s a recruiter reply
• a certain kind of message appears in Slack → start the corresponding work
• a GitHub CI failure → investigate the cause

Dot also has proactive research: it can look through authorized information on its own, looking for changes worth attention.

OpenAI hasn’t yet published how often proactive research checks.

So always-on is closer to “persists and keeps its state”; not everything has a real-time event stream.

That also made me start noticing a very basic question:

how proactive an agent can be depends largely on what signals it can receive.

If a system has no logs, tests, notifications, events or queryable data sources, even the smartest agent won’t know what just happened in the real world.

04 Dot can also hand work off to Work and Codex

Dot doesn’t need to do all the execution itself.

Say a software product hits a CI failure:

CI failure → Dot sees the signal → understands what happened recently → creates a Codex task → Codex investigates, changes code, runs tests → Dot organizes the results → comes back to a person when judgment is needed

To use Codex Cloud, you first need to set up a Codex cloud environment for the relevant repo.

Not setting one up doesn’t affect normal use of Dot; it just can’t create coding tasks directly in that cloud environment.

If you turn on local computer access, Dot can also create local Work / Codex tasks and access authorized local files and the browser.

05 What use cases has OpenAI given?

The examples OpenAI currently gives are actually very broad.

• The simplest is checking your Calendar every morning and telling you what’s on that day.
• It can also push a project forward over time, continuously tracking the plan and deadlines.
• In sales, once a customer’s needs change, Dot can update the proposal, keep preparing analysis and materials, and hand the results to a person for review when needed.
• In the developer example, it can keep looking through customer feedback, spot recurring feature requests or bugs, then investigate and handle them further.
• If Codex or Work is connected behind it, the parts that need executing can be delegated further.

These scenarios have one thing in common:

the matter lasts for a while, its state changes, and each change may produce a new next action.

Job hunting, customer follow-up, long-running projects, research and product maintenance are all fairly well suited.

06 Call and Slack put Dot in different places

You can now Call Dot directly.

Call here means you phone Dot yourself for a real-time voice conversation. For now Dot can’t call you on its own.

It can also be connected to Slack.

In company settings, Dot can appear in Slack channels with its own identity, so the work doesn’t all have to happen on the ChatGPT website.

What these features solve is:

where you go to find this agent, and where it keeps working with you.

The same Dot can carry over from ChatGPT on your computer to your phone, Call, Slack and other messaging channels.

Seen this way, the difference from an ordinary chat window becomes more intuitive too.

07 How far can permissions go?

Dot can use the Plugins you’ve already authorized for ChatGPT, such as Gmail, Drive and GitHub.

There are also Custom Rules, which let you specify that a certain kind of action:

• can be done on its own;
• can be done if I’ve explicitly asked for it;
• needs my approval every time;
• is handed straight back to me to do.

Take job hunting:

• reading recruiting emails: fine.
• updating the tracking: fine.
• sending recruiter emails: ask me every time.
• formally submitting a job application: I’ll do it myself.

Dot’s proactive research itself also has boundaries: it can read authorized information and leave private notes, but can’t send messages, modify plugin content or control the computer directly through this background research process.

When actually carrying out actions, it still has to go through the corresponding permissions and safety checks.

08 Who can use it now?

Dots began rolling out gradually on September 29.

It currently supports Pro, Business Premium, and an Enterprise beta turned on by admins.

The Pro launch doesn’t yet include the EEA, Switzerland or the UK, and you need to be 18 or older.

Dots can be created on desktop web or in the ChatGPT desktop app. Once created, you can keep talking to it on supported mobile clients too.

It’s still in gradual rollout, so different accounts may see the feature at different times.

09 Why is everyone suddenly building this kind of agent lately?

• Meta calls Muse a personal AI agent.
• OpenAI calls Dot an always-on agent.
• The bigger category is still AI agents / agentic AI.

If you want an easy-to-grasp description for this kind of product, persistent personal agent fits fairly well, though it isn’t an official name the industry has agreed on:

• it has a persistent identity;
• it has memory;
• it has its own computer;
• it can connect to external tools;
• it can receive signals;
• it can keep working in the background;
• it comes back to a person when needed.

Muse launched on September 8, and Dots was released on September 29.

It can’t be that OpenAI only started building Dots within three weeks of Muse taking off. Their appearing at the same time is more like different companies arriving at a similar product direction.

Over the past year or two, models’ ability to handle long-horizon tasks, call tools and operate computers has kept improving.

Surrounding capabilities like cloud computers, memory, app integrations, permission controls and background execution have gradually matured too.

Now they’re starting to be pieced together into products ordinary users can use directly.

Muse’s recent rapid rise has also made more people start noticing this product form.

10 Having started using Dot, what will I pay particular attention to?

Using ChatGPT before, a lot of effort went into how to write prompts, what to ask next, how to carry context over, and remembering when to come back and continue.

Long-running agents reduce some of this management work.

What I care more about now are a few questions:

• what do I want it to be responsible for long term?
• through what signals does it know things have changed?
• which data and tools can I give it?
• which actions is it allowed to do itself?
• which results must come back for me to check?

At least for now, I won’t open up Gmail, Drive, GitHub and my computer all at once from the start and then tell it “use your judgment.”

I’d rather first pick something with clear boundaries that’s easy to verify and run it for a while.

What I’ve chosen for now is job hunting.

To see whether it can actually reduce the cost of my having to remember “what to do next” every day.

Finally, there’s a rather lovely coincidence.

OpenAI still hasn’t explained why it’s called Dot.

But seeing the name, the first thing I thought of was the line from Steve Jobs’s Stanford speech:

“You can’t connect the dots looking forward; you can only connect them looking backwards.”

I don’t know whether the naming has anything to do with it; OpenAI hasn’t said so.

But a long-running agent really does face lots of scattered dots every day:

a conversation, an email, a Calendar event, a Slack message, a GitHub issue.

Whether it can keep connecting these dots may be what really decides whether this kind of product is any good.

Sources: OpenAI Dots Help Center, Dots Privacy / Security / Safety FAQ, GPT-6 Astra System Card; Meta’s official Muse announcement; Reuters coverage of Dots and Muse.