Cloud OPUS 4.6 just launched with a feature that gets me generally excited since it will likely completely change how we work with AI. It's called Agent Teams. Instead of prompting one AI, you describe a complex task, and it spawns multiple specialized agents that coordinate to solve it. Everyone predicted this as a trend for 2026,
but no one expected a production ready version to come out so early in the year. In this video, I'll explain everything you need to know about them, what they are, why they're so game changing for complex tasks, and show you how to start using them yourself.
So what are agent teams? Unlike sub agents, which operate within one session and only communicate their results back to the main agent, you also have the option to engage directly with individual teammates instead of routing everything through the lead. Here's how it actually works. You give a complex task to the system. Let's say,
build me a marketing campaign.
And instead of one AI trying to do everything, the system spawns multiple agents,
teammates. A research agent gathers data about your audience. A strategy agent plans the campaign structure,
and a creative agent writes the copy. Also, you could have a review agent, which checks everything for quality. They work in parallel where they can, in sequence where they need to, and they coordinate their outputs. Here's why this matters. A single AI has to be a generalist. It's okay to everything that's not great at anything, but when you specialize agents, each one can be optimized for its specific task. The Research Agent is amazing at research, the Creative Agent is amazing at writing, and when they join forces, the output is better than any single AI could achieve. Plus, they can work in parallel,
which means complex projects finish way faster. You may ask, why is this a big deal? Clearly, this is a fundamental shift in how we use AI. Until now, we have been trying to make one model smarter and smarter, more parameters, more data, better training. Just look at all the benchmark graphs on each LLM release. Agent teams live that. Instead of one super brain, you get a coordinated organization.
So what becomes possible with agent teams? Long horizon projects, tasks that used to require constant human oversight, can now run autonomously,
with agents checking each other's work. Complex workflows,
multi step processes
with research,
analysis, creation,
and review can happen in one coordinated flow. Better quality.
Having a dedicated review agent catches mistakes that a single AI would miss. Faster execution. Parallel work means things that took hours can finish in minutes.
Basically, the tasks that were too complex for AI,
a lot of those just became possible. To be clear though, agent teams aren't a completely new concept. People have been experimenting with multi agent systems for a while. But Cloud Opus 4.6 implementation
is the first one that feels production ready. Here's what they got right. First, automatic orchestration.
You don't have to manually spawn these agents. Claude figures out,
on its own, what agents are needed based on your task. Second, intelligent coordination.
The agents don't just run independently,
they share context and communicate naturally.
Third,
built in specializations.
Cloud has preconfigured agent types optimized for different tasks,
research,
coding, writing, analysis,
QA.
And fourth, Error handling. If one agent gets stuck or produces bad output, other agents catch it and course correct. A few technical things worth knowing. In contrast to sub agents, individual team agents can request information from each other. Like, the writing agent can ask the research agent for more data. And there's a supervisor agent that coordinates everything, making sure agents work in the right order and don't duplicate effort. It's surprisingly sophisticated for a first version of this. So now I'm going to walk you through how to actually set this up and use a Tenreal project that I definitely need help with creating a week's worth of social media content for a personal brand. So the first thing you will need is Claude Opus 4.6
access that you can get with their pre subscriptions. As you can see, you have an option of a Pro plan and a Max plan. In the demo we're gonna show today, the Pro plan is sufficient
and it should get you by for two or three tasks per day even using this rather powerful agents mode. However,
for professional use, we would recommend to use the max plan and that would cost a 100 or 200 if you want even more credits. While the max plans are more expensive, they can be definitely worth it because for many people, this agent's team mode will save a lot of time. So for this demo, we assume that you have Cloud Code already installed on your system, so we're only gonna go through that step. However, agent teams is an experimental feature, and it is not enabled by default for everyone.
So you would need to add an additional configuration to your settings JSON file. If we would open up the Cloud Code docs, you would see the specific line you would need to add into the file. Let's do that directly in the terminal. You would need to go to your
cloud
configuration folder,
open the settings JSON file,
and add the following lines. I have already them added to my configuration, configuration, so that's all you need. Close the file. And now there's an additional step that we would recommend. We would recommend to install TMux because that would allow in your terminal to see every agent working individually. Otherwise, you would only see the same thing you would see on the cloud desktop application,
which is every agent in the same single conversation,
more or less. It is possible to switch between them in your terminal, but it is much nicer if you can see all of them and also interact with each of them individually. So for that, we're going to install t mux.
Alright. And this might take a while. And here we go. We have our t mux installed.
Let's enter a new TMUX session and
jump into a project folder. So for any sort of thing you're working on, we would recommend to have a folder and for Cloud Code is
mentoring.
So in our case, that is the video demo.
Now let's launch
Cloud Code. We also have a prompt already prepared for this demo, so we're not going to go into detail from engineering techniques. But as long as you provide
a clear task
and what are your deliverables you want, your prompt really doesn't have to be much more complex than that. One additional thing we would recommend to add for a prompt where you're trying to work with agent teams is to specify
each individual team member you want to spawn. It isn't necessary.
You could just tell the model to orchestrate
a couple of team members for the task. But if you want consistent results, we would recommend to specify the roles you want to fill. In our case, that is a strategist,
a copywriter,
a visual concept agent, and a reviewer for quality control. Alright. So
let's get into it. This might take a while, and here comes another recommendation.
Do not try to run agent teams for simple tasks. For simple tasks, we will still recommend to use a single AI instance
or to use sub agents for very simple flows where you just have, let's say, maybe a writer and a reviewer.
When you need more collaboration,
then we would recommend to go for agent teams. We can already see that
the model is preparing a plan, and soon enough, we should see how it spawns a couple of team members to help it out. Alright. Here's the beauty of TMax. We already see individual
team members
on panels on our right. And we can even see what each member is trying to do. So this is the key difference between agent teams and
sub agent flows where you really have no way to interact or even sometimes observe what individual sub agents are doing. Here, we could even stop any of the team members working course correct if they're going the wrong way, or even provide an additional task we have not provided in our original prompt. So the strategist agent has already worked and prepared a plan, and it has delegated its first task to the copywriter.
And now, the copywriter does not have the permission to interact with our file system and it has asked for permission directly
through the main lead agent.
So we could
do the permission. Alright. I see the copywriter has finished its first task and now the visual designer
has started working on on its own assignment.
So to copy done, start visual concepts.
Okay.
So tasks with agent teams take a while, so we have left the team to work on the task. Let's check back with them. As we can see, the copywriter has already finished with its task and now the visual content strategy agent is creating a document. Oh, it's already created directly. A new doc right here. Again, asked for permission.
Though,
you can also start Claude Claude code with an additional flag which would allow you to just give permissions to all
team members automatically if that's what you prefer. Here, we're working conservatively,
so we are explicitly allowing team members to deal with the file system. If you're in a hurry, you could just preemptively give these sort of permissions. Okay. So the reviewer has found a few issues to fix, and we're even seeing which exact lines has found and what types of edits it's doing.
So
it is using the diff style, you know, what kind of line it is changing. We can even investigate and say
that maybe this agent should focus on something more
specific. Alright?
So TMax paints have already closed, so that means that agents have finished with their work, but that does not necessarily mean that the task is is already done because the reviewer has flagged up five action items on areas where it could still
improve. So what we could do now,
we could again prompt our main team lead and ask it to delegate these as tasks to team members to iterate on the documents again. So we give the team lead the same tasks that the reviewer has flagged, and we can see that it's already working on a new plan on how to iterate on these docs. It is adding new items to the to do list. So first, it will need to source and verify all statistics and citations.
And again, we're seeing that it has decided to spawn researcher, which is a new agent which we haven't specified before, a strategist,
a copy editor, and a copywriter. So here we can see that it has used two of the agents that it has previously seen in our last iteration,
and it also has decided to bring in two new agents, which we didn't even explicitly specify on our prompt. So as you can see, quite often, you can get away with broad prompts. Now all four agents are working in parallel.
So the researcher is looking up information online to double check that our mentioned statistics are correct, while at the same time, the copy editor is trying to identify the the issues that the reviewer has flagged. Also, we see that the copywriter has tightened the Wednesday reel to be thirty to thirty five seconds,
and it also improved the call to action. At the same time, we see that there have been some placeholders in our content and and a copywriter has filled them in. Also, the copy editor is working on that on tightening up the Wednesday reel to be thirty to thirty five seconds. It's also trying to improve the call to action. We already see that the researcher has flagged up some of the metrics that we have cited in our
material,
and it has replaced them with more factually accurate ones it has found online. At the moment, out of our four teammates,
three of them have already completed their tasks. We're just waiting for the researcher to wrap up its task. The researcher has also prepared a sources document where it lists all the references
for the facts and the statistics it has updated in our document. Alright. I'm seeing that that task is being wrapped up. Team cleaned up. All revisions are done. Your full content package is production ready and in the folder we started with. Let's jump into that folder and try to review its work.
So, you can see there are quite a few files, and I will go to the post it has recommended for Monday through Wednesday. Okay. So for Monday, it recommended a LinkedIn post at Twitter or XPost and
also Instagram caption. We can see that the post is already
well structured. It seemingly
we need to double check this and because this is still to some degree a draft. And as we can see, all these posts match the platform requirements.
So for LinkedIn,
it has more of that LinkedIn style with a bullet list, with more metrics, more styling.
Meanwhile, that Twitter post is short with a couple of relevant hashtags
based on the industry we're working on. And the Instagram caption has also plenty of hashtags through which it would recommend us to market. Alright. Also, the model has provided an image concept and a video idea, a thirty second video. So these were not generated.
You would need to either shoot these visuals yourself or use an image generation model,
something like Nano Banana Pro, GPT image 1.5, and for videos, you could use Vio 3.1
to create these sorts of visuals. It has put in a lot of work, even provided a color scheme for the image concept and even the specific format.
So for
Instagram, it recommends a square image with a very specific resolution, and for LinkedIn,
it recommends a banner. And it has provided very
similar structure for upcoming days. So for Tuesday and
for Wednesday. For Tuesday, I see the video ID is a bit different. So it's also also forty five seconds. And for Wednesday,
it has a longer video
with
very detailed breakdown of what should be in the video nearly second by second. If you would try to prompt a single AI model to do this sort of task, usually, it wouldn't have enough of the context window to really get into every single day, every single post, and mesh every platform that we need. So this is the power of agent teams. In just fifteen minutes, we got a good first draft, and I still would need to double check it and inject some of my personality into it, but it still saves me hours in production, and all of it was done with a single prompt. If I tried to do that with a single agent, it would take me multiple tries and likely a few hours. As amazing as it is, one downside is the price.
Let's check how much the single generation cost us. So if I would jump back into Cloudera code and I would write
slash usage,
we would see that I have spent around
7
close to $8 in usage just for this single task.
If you would want to know how that what that means for pro and max usage,
that would be around 50 percent of your single session usage for pro. For max, you would probably be able to fit about eight or 10 tasks in five hours. So to avoid unnecessary cost based on testing this for a couple of days now, here's when agent teams make sense versus just using regular Claude. Use agent teams when the task has multiple distinct components
Quality matters more than speed. You need different expertise applied to different parts, and you want built in quality control. Use single AI when it's a simple focus task. Speed is more important than sophistication,
and you're on a budget and can justify the extra cost. For that social media content, agent teams made sense. If I just needed one post caption, a single AI 100%
of the time. In general, I keep single agent clouds for quick tasks and only spin up teams for complex projects or to tackle issues where the single agent fails. A quick few tips before you try this yourself. One. Start with a low stakes project. Don't hand it your most important client work on day one. Two, be very specific in your initial brief. Include brand voice, target audience, goals, any constraints,
kind of like working with a new colleague. Three, review everything. The agents are good, but they're not infallible.
The classic AI rule to double checks everything still applies in this case. Four, keep an eye on your API or subscription usage in the dashboard. Set spending alerts if you're worried about costs. So clearly,
with this new update, Biotropic, we're shifting from AI as a tool to AI as a workforce.
Agent teams are the first step toward AI organizations that can handle complex projects autonomously.
Alright.
So that's agent teams in Cloud Opus 4.6.
The concept is simple. Specialized AIs working together beat one generalist AI. The implementation is sophisticated.
Cloud handles orchestration,
coordination, and error handling automatically.
And the result? For complex tasks, this will be a game changer. If this works at scale, it changes what's possible with AI completely.
If you would like to see a video on more use cases and results of my testing, subscribe to not miss it when the video drops. And leave a comment. What would you use agent teams for? What complex projects would you hand off to a team of specialized AIs? If you want to understand the technical foundations behind this stuff, how these models actually work, not just how to use them, check out Turing College's AI courses.
Links below. Now we have all heard of the layoffs that are happening in the industry, so we have made a video on the most essential AI skills to master to get ahead in 2026
and become irreplaceable.
Click here to see it. That's it from me. Catch you in the next one.