ChatGPT now comes with an AI team! The GPT-6 Astra Pro upgrade enables the AI squad members to split up to handle respective tasks and track the work progress in real time.
ChatGPT has learned to “call in reinforcements” to form a team for work!
On October 16, OpenAI announced the upgrade of GPT-6 Astra Pro in ChatGPT:
It delivers faster responses, enhanced plugin and tool usage capabilities, and allows multiple agents to split up and process the same task.
When you assign a complex task, several AIs can each take charge of a part and advance the work simultaneously.
You can also track their respective progress in the conversation, click on completed tasks separately to check the deliverables.
Working separately, tracking progress, and reviewing results, these processes have been integrated into the same chat entry point.
At present, this update is being rolled out gradually to eligible Pro users. Whether corporate or team accounts can access it depends on administrator settings and whether they have received the push notification.
As more AIs work for you, there are also more tasks for you to monitor.
Netizen @Sebasti54919704 complained that while parallel agents are great, tracking progress one by one is the part that drives him crazy.
He simply connected voice support to his Claude Code and Codex sessions, so he can ask for updates and switch between sessions with voice commands.
Sometimes these agents even talk back to him.
When multiple AIs work at the same time, the user might be busy tracking progress and urging tasks, and occasionally even have to deal with the AI’s “talking back”.
Whether an AI team works well depends on whether it can save users time on chasing updates, passing information, and coordinating tasks.
How this AI team gets to work
The entry is on the Chat tab of ChatGPT.
According to the official help documentation, select GPT-6 from the model menu, then drag the Thinking slider to the Pro gear, and you will be using GPT-6 Pro powered by GPT-6 Astra.
The Instant, Medium, High, and Extra High gears are powered by GPT-6 Sol.
The regular gears of GPT-6 are supported by Sol, and the Pro gear is supported by Astra.
This upgrade mainly makes three major changes.
First, speed improvement.
OpenAI says responses are now faster. As for how much faster exactly, and how much time can be saved to complete a task, the official has not released specific figures for the time being.
Second, tool usage optimization.
Whether the AI can finish the work well also depends on how smoothly it uses tools.
Netizen @Rossisonx complained that plugins sometimes get stuck in loops, stay in place, and seem to still consume quota. No progress is made on the task, but the quota may keep decreasing, which is inevitably worrying.
This time OpenAI announced improvements to plugin and tool usage capabilities, and the actual effect remains to be seen.
If the tool needs to access external services, users still need to connect their accounts and complete authorization first, and the AI can only operate within the permitted scope of permissions.
The most core change is multiple agents working in parallel.
For example, if you want to pick one from three AI tools, you need to compare their features, prices, and usage restrictions.
The AIs can separately collect information about each product, then align the terms of monthly payment, annual payment, free version, and paid version.
User @rubenssoto_a also shared his own attempt:
I asked it to do research, then output the product requirement document and task work order, and the result was great.
From looking up information to writing requirements and listing tasks, the research results can be directly converted into the execution plan for the next step.
However, the division of labor for AIs is the same as that for human teams: tasks that can be done at the same time are done separately, and those with sequential dependencies are carried out in order.
For example, reading materials first, then making plans, and finally modifying files, the latter step needs to use the result of the previous step, which cannot be skipped even if multiple AIs are assigned.
Only by splitting out the parts that can truly be advanced simultaneously can the waiting time be shortened.
After the tasks are assigned, the progress also needs to be visible.
Users can track each agent in the main conversation, and click to check the results after the tasks are completed.
Netizen @wushanyunyu1 mentioned this change and said, “This has turned chatting into task assignment.”
In his opinion, the next point of competition will be who can better manage the work of multiple agents, reduce errors and rework.
He is more eager to see how much rework is needed after Pro users use it to complete a batch of real tasks.
When several AIs submit their work at the same time, users may not be able to review all of them.
Netizen @leolaoshuai reminded: “The bottleneck has become your review speed.”
If you have to check every deliverable from scratch, the saved waiting time may be spent on the review process.
His suggestion is to clearly define “what counts as completion” before assigning tasks, and attach directly verifiable references when delivering results.
The deliverables submitted by agents should not only be viewable after clicking, but also better clearly list what has been done and where the references are, so as to save users time on searching and verification.
As for the maximum number of agents that can run at the same time, whether the division of labor is triggered by default, and how the results are specifically merged, this announcement has not been expanded to explain.
Claude has already formed its agent legion
Netizen @FinallySomeRest said in the comment section: “Claude has had this feature for a long time.”
When it comes to letting AIs form teams, Claude actually has relevant practices earlier.
Claude Research, which was made public in June 2025, can be led by a main agent, allowing multiple sub-agents to conduct separate research, and then summarize the findings back.
The main agent assigns research tasks, multiple sub-agents search in parallel, and then summarize the findings
On September 17, 2026, the new version of Claude Code Projects started public beta, extending collaboration to ongoing projects.
You state the goal in the project conversation, and Claude is responsible for splitting tasks, coordinating multiple threads, and accumulating project memory. Cloud threads can continue to work even after you close your laptop.
On October 10, the official announced that Pro and Max users on the Projects waiting list were granted access.
In the same period, the Managed Agents dynamic workflow started public beta, and a set of Bug detection tests was announced:
70 Bugs were pre-buried in 116,000 lines of code. When a single agent ran three times, it found 14, 15 and 27 Bugs respectively; when the dynamic workflow was used, 66 Bugs were found in all three runs.
From looking up information separately to coordinating multiple task lines, both Claude and ChatGPT are taking on more division of labor and collaboration work.
As for “who does the work, what to do first, and how to connect the results”, the implementation methods and public details of different manufacturers are still different.
Only when AIs can split tasks and connect results by themselves, can users stop being messengers as much as possible.
Separate work results need to be consistent in the end
However, splitting tasks is just the beginning.
If several AIs are all looking through the same document, they seem to be very busy, but in fact they are doing redundant work.
If one branch uses the latest data, while the other still refers to the old version of conditions, the final assembled report may have conflicting content.
When Anthropic introduced the multi-agent research system, it shared a lesson:
The main agent did not give specific enough instructions, two sub-agents repeatedly investigated the same direction, while the other looked into problems of different years.
Therefore, when dividing labor, it is necessary to clarify: what each agent checks, to what extent, and in what format to submit the results. The more ambiguous the task boundary is, the more likely redundant searches or missing key content will occur.
After separate searches, the results need to be aligned next.
Netizen @878967502Teamo said bluntly: “Parallelism itself is not difficult, the hard part is merging the results in the end.”
He hopes that in addition to viewing progress, he can also see at a glance where the results of several branches have conflicts.
When writing reports, the data time, price conditions and comparison standards must be consistent; when writing code, the interfaces and naming conventions must be compatible.
Otherwise, after the AIs finish the work separately, users still need to check for errors, fill in gaps, and reassemble everything.
Anthropic also reminded in the official description of Claude Code that steps with close sequential dependencies are not suitable for forced splitting, and multiple agents modifying the same file at the same time are prone to conflicts.
If the task is too small, the time spent on division of labor and handover may offset the time saved by parallelism.
The feature is very cool
But you also need to pay attention to the quota
Whether you can use it and how much you can use is the account users need to calculate next.
According to the official help instructions, Plus users can use Astra in Work and Codex, but cannot use GPT-6 Pro in Chat.
This multi-agent upgrade in Chat is currently being gradually pushed to eligible Pro users, and Plus users cannot use it for the time being.
Netizen @WhosCinq spoke out for Plus users in the comment area, questioning that this update excludes them again.
Users who have already paid are also concerned about the price.
Netizen @JcCastaneda05 showed his Pro subscription and usage page, and his appeal is just one sentence:
Please make it cheaper.
Subscribing to Pro does not mean you can use it without restriction —
First, check where you are using it.
The message quota for the Pro model in Chat is calculated separately from the shared usage pool of Work and Codex.
The user screenshot above also states that shared usage does not include Chat conversations.
Then, check which subscription tier you have.
The current official help documentation shows that the $200 tier Pro has a weekly quota for GPT-6 Pro; in the $100 tier Pro, GPT-6 Pro and GPT-5.6 Sol Pro share one weekly quota.
The $200 tier has a weekly quota for GPT-6 Pro; the two Pro models in the $100 tier share the weekly quota
