The AI Daily Brief: Artificial Intelligence News and Analysis - The Multiplayer AI Sprint: Build Your Team’s First Shared Agent
Episode Date: September 7, 2026AI agents have transformed how individuals work, but the next leap is moving them into the shared spaces where teams actually collaborate. In this episode, NLW explores how Anthropic, Every, OpenClaw ...and others are shifting from single-player AI to shared agents—and launches The Multiplayer AI Sprint for Teams, the latest free AIDB learning program. The four-part, self-directed sprint helps teams assess their current AI use, build shared context, identify collaborative workflows and put their first multiplayer agent to work.Do the sprint - https://multiplayerai.ai/NEXT COHORT - Executive Agent Leadership - Returns in September -- Learn how to use agents - https://training.besuper.ai/Brought to you by:KPMG – Research from KPMG and the University of Texas at Austin shows the highest-impact AI users treat AI like a reasoning partner — and those skills can be taught at scale. Learn more at https://kpmg.com/us/SophisticatedHarbor - Invest in the AI ecosystem. https://www.harborcapital.com/aidailyHyperagent - Hire a team of always-on agents. New users get $100 in free credits. hyperagent.com/aidailybriefRackspace Technology- One accountable partner to build, operate and run your full enterprise AI stack https://www.rackspace.com/Section - Section turns AI investment into workforce transformation and ROI - https://www.sectionai.com/Blitzy - Want to accelerate enterprise software development velocity by 5x? https://blitzy.com/AssemblyAI - The best way to build Voice AI apps - https://www.assemblyai.com/briefRobots & Pencils - Cloud-native AI solutions that power results https://robotsandpencils.com/The AI Daily Brief helps you understand the most important news and discussions in AI. Newsletter: https://aidailybrief.beehiiv.com/Interested in sponsoring the show? sponsors@aidailybrief.ai
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2026 is undisputedly the year of agents.
For years, we were talking about these things, but now they are actually here and they are
changing how we do work, at least on an individual level.
The thing is, not all of our work happens on an individual level.
Most of us, in fact, split our work pretty comfortably between work that we do on our own and
work that we do in teams.
So far, agents have really only been able to impact about half of that equation.
I believe strongly that that is about to change, and that the best, most dynamic AI using teams are going to shift from single-player AI to multiplayer AI.
The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.
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Welcome back to the AI Daily Brief.
The day that this episode comes out is Labor Day in the U.S.
The traditional end of summer and the beginning of back to school and back to work.
This is one of those inflection moments where a lot of folks come back to the office, whether it's
virtual or real, reinvigorated, and ready to crush out a couple great months before the
holidays descend. In fact, I think in many ways, outside of New Year's, this is the time where I
see the most excitement around new ways of working on an individual and a team level. Now, this year,
Labor Day also happens to fall on my birthday, and I thought that I would give all of you guys a
present. So far this year, we have released four free self-directed learning programs. We kicked
off the year with the New Year's AI Resolution, a 10-week-10 project adventure, which was really
meant to provide a very broad basis of basic core AI skills that were notably in general pretty
pre-agentic. Agents would come a little bit later. In February, we released Claw Camp, which was a
zero-to-agent team program that was not simple at all, but which gave people a guide to diving
into this new crazy agentic world that had been enabled by OpenClaw. Agent OS came just a little bit
later, and was the more mature, grown-up version of Claw Camp that was not only platform and tool agnostic,
but helped people build not just a single agent or even an agent team, but an entire agentic
operating system capable of taking on increasingly complex work. Finally, the AI summer adventure
was a choose-your-own adventure style program that provided a bunch of fun skills at a variety
of different levels to people who wanted to stay sharp over the summer. Now, there are a few things
that all these experiences have in common. They were all free, self-directed, project-based learning
experiences. The whole goal of which was to provide a framework for you to actually go do this work.
The pedagogy behind them is pretty simple. It's that to learn how to use AI, you just have to use
AI. They're all kind of anchored in the truth that at this point there are still no AI experts.
There are just people who have practiced with it more. But the other thing that they all are,
ultimately, and at core, is individual. Now with AIDB New Year's, you could form a team,
but it was a team only in the sense of mutual support. People going through an individual,
experience in parallel.
All the other programs follow the pattern that pretty much all agentic work has so far,
which is people building and leveraging individual agents for their individual work.
The thing is, not all of our work happens individually.
In fact, for most of us, some major and meaningful portion of our work happens as part of a team.
A recent survey of around 16,500 office workers found that something like 39% of the
workday is spent working alone, while 42% of the workday.
percent of time is spent working with others. Another survey found that 57 percent of time is spent
communicating, i.e. meetings, email, chat, et cetera, versus 43 percent creating individually. And yet
another survey found that about 60 percent of time goes to work about work, communication, search,
coordination, and process. In other words, a significant portion of knowledge work happens on a team,
and the majority of knowledge work runs through team context. Collaboration, meetings, email, chat,
coordination, search, those are the substance of a huge amount of work. And yet, so far,
the vast majority of agentic efforts have been entirely personal. Think about all of the early
experiments that you've heard about. It's all people building their agent teams, their researchers,
their writer agents, their coding agents, their personal chief of staff agent. Advanced users are, yes,
experimenting with agent teams, but it's agent teams that serve only the individual. Now, to be clear,
I don't think that's going away.
I think the fact that all of us now gets to be a manager of a big extensive team of agents
that themselves can spawn subagents to do lots of different work is now just part and parcel
of being an effective knowledge worker.
However, what I don't think that has changed is the fact that much of the meaningful work
that we do will not be in our own individual silos but at the intersection of where we work
with other people.
And my strong, strong belief is that the next frontier of agent design is going to move.
agents from the individual silos in which they have operated so far to the shared spaces that
teams inhabit together. That means team-owned context, not repeated context where everyone's
individual folders have the same documents in them, but one single repository that is
shared across the team. It means shared sessions, observable work, and live steering and handoffs,
where the agent is working, again, not in the solo of someone's individual computer,
but in some sort of shared environment, where multiple people can have input all at the same time and in the same session.
This is the move from single player AI to multiplayer AI, from individual AI to team AI.
The move from single player to multiplayer AI is a move from private outputs,
where teammates can only see the final answer to visible work, where everyone can see what the agent is doing.
It's the move from feedback prompts, where teammates can only interact after some output has already happened,
to live participation where teammates can redirect, annotate, and join while the work is happening.
The shift from single-player AI to multiplayer AI is a shift from personal memory to shared context,
where the durable context belongs to the team, to the channel, or to the project.
And ultimately, the shift from single-player to multiplayer AI represents a shift from agents
being about only individual leverage to becoming team capability,
not just personal efficiency tools, but agents as reusable organizational infrastructure.
Now, as some evidence that this is in fact a trend and not just something that lives in my head,
I think that there are a few examples from the last 90 days or so that have made this pattern pretty clear.
One, which I don't know, it might actually have been longer than 90 days ago because they're pretty ahead of the curve.
At the beginning of the year with the Claw Camp moment, the team at Evry started their agentic experimentation
by having everyone have an individual agent that was the mirror of the person who owned that agent.
Before long, they realized that that wasn't really how work got done,
and they started to shift to a model that had more agents in their shared spaces doing work
that intersected the teams.
A big product moment for this,
and this might be the clearest product expression of multiplayer AI so far,
was when Anthropic introduced Claude Tag.
Claude Tag lives inside Slack,
but it's different than the previous implementation of cloud in Slack that existed before.
The original clod integration in Slack was one where an individual could tag in their personal
clod, bringing them into a conversation, and then sending them off to do things and managing
their personal clod from that work interface. Clod tag was something different.
Claude tags are instances of clod that are shared across an entire team, as expressed by
specific channels that can each have their own context, tool access, data access, or other
types of access that allow that shared clod to do the work. In other words, when you tag in
Claude now in your coding channel, it is not your personal clod that you're tagging in, but the
shared cloud agent that works across your team. The team at Anthropic wrote, tagging Claude comes
with a few new advantages. AtClaude is multiplayer. Within a given Slack channel, there's one cloud
that interacts with everyone. That means that anyone can see what it's working on and can pick up
the conversation from where the last person left off. This makes tagging Claude very different
from working within a single chat or for a single task. It's much more like interacting
collaboratively with the teammate. Because Claude is multiplayer, it can also learn over time.
It follows along with the channel that it operates in building more context about the work.
Because it is operating in that shared team multiplayer space, each individual user that comes in
and need something doesn't have to explain things over and over again from scratch.
The multiplayer team-shared Claude also has the ability to take initiative.
Teams can enable a setting that allows ambient behavior that proactively interacts with the
information from the channel that might be relevant for its type of work.
Now, at this point, Claude Tag is only just starting to dissipate across teams, but certainly
for Anthropic themselves, it has fundamentally changed the way that they work.
In fact, in that same announcement post, they wrote that 65,
percent of their product team's code was now created by their internal version of Claude Tag,
i.e., not a bunch of individual developers using their own Claude agents to contribute PRs,
but a shared space with that shared agent working between them that's pushing nearly two-thirds of
their code. Another recent example of this pattern comes from the OpenClaw 2.0 launch.
After pushing relentlessly for the first part of the year, delivering an update every couple of
days, the OpenClaw maintainers went quiet for about seven weeks to coordinate around a much
bigger and more extensive rebuild. Initially, they tried to collaborate by having all of their
different individual agents work together in Discord. But what they found is that that still wasn't
collaborative enough. One of the company's maintainers, Colin wrote, what we wanted was a way for both
developers to see the work itself. If an agent paused because it needed clarification, either of us
should be able to jump in. If something needed a second set of eyes, we should be able to open the same
session and look at the same context. No screenshots, no copied transcripts, no here's what the
agent has done so far data dump. Just open the work and continue. And so they ended up building a new
multiplayer web UI for OpenClaw that allowed for exactly that. Colin continued later. The feature that
made the difference wasn't simply seeing another avatar online. It was being able to share a session
while work was happening. When something needed another opinion, we could both open the same thread.
When the agent needed information one of us had, that person could add it directly.
There was no need to copy the conversation into Discord, explain what happened, and then carry the answer back.
We were working inside the same context.
That sounds like a small interface improvement, but it changes the way you collaborate with an agent.
The session stops being a private conversation between one developer and a model.
It becomes a shared piece of work another trusted developer can inspect, steer, or take over.
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So now you have the very advanced users of the Every Team,
the OpenClawe team, and the Anthropic team,
all shifting from this individual agentic behavior to this shared team multiplayer agentic behavior.
and I don't believe that this is just going to be constrained to coding.
What it is to me is clearly agents evolving along a natural pattern
to speak to the other half of work that we do that they don't touch yet,
which is the work that is shared with other people.
One more piece of evidence that this is going to be a thing.
Each quarter why combinator partners share a request for startups.
And it's a great way to see where a very advanced group of investors
think the world is headed by them asking for companies
that are addressing particular issues.
One of their themes for fall 2026 is, you guessed it, multiplayer AI.
Ycombinator partner Aaron Epstein wrote,
The best work tools of the last two decades won by going multiplayer.
Google Docs replaced Microsoft Word, Figma beat Photoshop,
and they turned solo tools into places where teams do their best work together.
But AI hasn't had its multiplayer moment yet.
AI agents are the most powerful new tool a team has,
but it's the one thing people still use by themselves.
That's because right now,
with AI is largely single player. You open a chat, type a prompt, and get an answer, in a box
only you can see. When you want to collaborate with your teammates and agents, the best you can do
is send the link to a read-only transcript they can't touch. That's about to change. Agents are starting
to run tasks that take hours, days, even weeks. Work at that scale was never meant to be done alone
and pulls in many people across a company. Anyone on a team should be able to drop into the same
live agent session to watch it work, redirect it, and hand it off, the way they'd work with any other human
member. This turns the work a team does with agents into a shared living thing instead of a
thousand private threads. We think there's a version of this for every kind of work.
Shared agents for engineers coding together in real time. For sales teams working a deal together,
for support teams resolving a ticket, for lawyers drafting a contract, analysts building a model,
and marketers shipping a campaign. Anywhere a team already crowds around one problem, there should
be multiplayer agents they all share. Now we here at the AI Daily Brief are not addressing the
startup and build side of that question, i.e. we're not releasing multiplayer agents for some
specific use case. But what I think that we can do is help your team build the foundations for using
multiplayer agents and AI well. Enter this falls free self-directed education program, the multiplayer
AI sprint for teams. You can find this linked from either the AI Daily Brief.A.ai website or the extremely
clever direct URL, multiplayera.ai.a.i. The idea of the program is to build a shared
four-week sprint for your team to get yourselves ready to use multiplayer agents and then to actually
try one in practice. Now, at the time I'm recording, we're still finalizing some pieces of this
platform. So what you see on your screen now might change slightly by the time you're actually
signing in. But what I wanted to do was go through the four weeks at a high level to give you a
sense of what the sprint involves. Week one or session one of four, you certainly don't have to do it in
just one week. You can do it all in one fell swoop if you want. But in any case, the first session is an
inventory to find out what your team is actually running. The goal of this is to take stock of how
people on your team are currently using AI and agents. Is everyone still just prompting chat GPT or
Claude? Has anyone actually built or managed some agent that does recurring work for them? Does
anyone have context files or has even broached into using skills with an agent? Almost inevitably on any
team there is going to be a wide range of what people are doing currently. But figuring
out where everyone is is a really important first step in making the leap from single-player
AI and agents to this multiplayer team type of usage. As with each of the weeks, there will be
two parts of this experience, a part that people do on their own and then a meeting where they come
together and share. The part that you do on your own is to actually build the portfolio that
helps you explain what you've done to your team. Now, certainly you can do that on your own, but you
can also use the builder that we have natively on the multiplayer AI site to take a short interview
with our AI to help you build that shareable portfolio automatically. If you do choose to use our tool,
it can contribute to a shared space that is just for you and the rest of the members of your team,
where you can see all of the answers to how people are using AI altogether in one single spot,
kind of creating a living artifact of the type of thing that you would discuss in that shared meeting.
Now, for each of these different weeks or different sessions, we wanted to give people a lot of
different options. You can, as I said, do everything inside of our platform, but if you have
security considerations that you don't want to deal with. You can also just download the worksheets
and do them in your own spaces without ever touching or leaving anything about your team on our site.
You are also welcome, of course, to simply take inspiration from this and do your own version of it.
I don't care at all about you doing the work in our space. All I care about is you getting
ahead of everyone else by understanding that multiplayer AI is coming and getting there first.
Now, some of you might be thinking to yourselves, what if our team is still really nascent and
actually doesn't have a lot of AI or agents running yet? Does that mean?
we shouldn't do this inventory week? The short answer is no, and I think that there are two valuable
things that you could do with it. The first is the other type of inventory that you could take
would be to explore what your barriers have been, to see if those are things that your team as a group
can address to make this multiplayer AI a little bit easier than the single player AI that hasn't
happened yet. The other approach, however, that I really like is to go find someone or some set
of people in your company who are advanced, even if they're not on your team, and ask them to
present to your team how they're using AI in agents, presumably within the same guardrails and
constraints and governance protocols that your team will face as well. That can be a great way to see
what power users inside your own company are doing, even if that's not exactly what your team's
experience is so far. Week 2 or Sprint 2 is all about context. Remember, one of the key things that we're
going to be doing as we move from single player AI to multiplayer AI is moving from individual memory
to shared context. But to do that, the members of your team kind of have to figure out what that
shared context repository needs to know. So in the second sprint, we have a tool that can help you
extract context about yourself that can be shared with the team, or again, you can download
the worksheet and do that in some other shared space. Again, there's an individual and a sharing
component of this. You write down your context on your own with the assistance of RAI or some other
AI, and then you bring it together with your team to put together that shared repository.
week three, we move from figuring out what context we share to mapping out the areas where our work
actually overlaps. Now a note here, the multiplayer AI sprint is assuming nothing about how sophisticated
a team's understanding of itself is. It is absolutely possible that some teams will, right out of the
gate, even as you are listening to this, know exactly what shared work you want to focus agents on.
And so to the extent that that's the case, you certainly don't have to go through this process,
All of this is just designed as an asset so you can go from zero to multiplayer agents inside the context of this sprint.
But the idea of this sprint or week three of discovering the shared work is once again to use AI to do a short interview that walks you through your common work and maps your work streams.
For each recurring work stream, the interview is going to ask what it is, who else touches it, what context it needs, and how often it's run.
When you have work that overlaps with others, that becomes a candidate for where a shared agent could.
live. And from there, we're going to give you a framework for how to score a candidate. I suggest
looking at it across four different dimensions. The first is shared need. How many people need the
same context for this? Obviously, more is a better candidate for a multiplayer agent. The second is a
staleness cost. How much does it hurt when each person's version drifts? The third category is
permission sensitivity, how much restricted data does it touch? And the fourth is checkability. Can you
tell quickly whether an agent got it right? A simple way to
figure out where you want to start experimenting, would be to score each of those axes on a one to
five scale and then squint at the ones with the highest scores as your potential candidates.
Now, the fourth and final part of the sprint is actually one that you can repeat over and over
again. The idea is to ship and start to use one shared agent on whatever tools you already
have access to, loaded with your team context, used by at least two of you on real work.
Now, certainly if you have access to Claude Tag, that's a really easy way to test this,
but we'll have some ideas for other ways that you can set up a shared agent as well inside the system.
The goal then is to test one shared agent, give it some amount of time to see if and how it changes the work,
and then ask if it actually improved things.
It may be that some use cases, despite there being a lot of shared overlap between you and others,
just aren't the right shape for an agent to get a lot of value currently.
That's okay. That means you move on to the next shared use case to see if an agent can help there.
And again, theoretically, after a single rotation of this, you can be done if you'd like,
But you can also run this basically repeatedly working through each potential space that you uncovered
that could be a good fit for a shared multiplayer agent.
So that's the idea.
As with all of our programs, this is just meant to provide a loose framework that allows you to
dive in and figure something new out through experimentation.
When you sign up, you'll get an invite code that you can share with other members of your team
to make sure that you're all working in the same shared space together.
And if you choose to share your artifacts, can all resolve to the same spot.
Now, if you'll permit me, I would also be remiss at this point to not note that if you are sitting here feeling a bit overwhelmed,
and like you don't exactly have the foundations even on single player AI to really be effective with this,
I would recommend either A, going back through some of our free programs like the AIDB Agent OS program,
or if you want even more support than that, checking out some of our paid executive programs,
specifically the executive catch-up program and the executive agent leadership program.
In particular, the agent leadership program is going to leave you in the top 1% of people who
actually understand how to build and interact with agents, which obviously gives you a huge
advantage as you shift from thinking about single-player agents to multiplayer agents, and the next
iteration of that program is actually starting this week, just following Labor Day.
There's a link, of course, to that on the show notes.
Overall, the important thing to me here is that it seems fairly obvious to me when I squint at it,
that this is the direction that things are headed.
Again, it's not that you won't use individual agents.
You absolutely will.
And not just one or two, but you will probably have entire fleets and teams that you manage.
What I'm convinced of, though, is that that only represents one part of the work that we do.
And so it is only natural that we start to figure out agents for the other part of work,
which is the work that lives between us.
I think that if you and your team work your way through this sprint,
you will be in a significantly better position to use these new multiplayer AI tools as they come online,
and be a leader in this exciting new world of team and multiplayer AI.
Again, you can find all these links at AIDailybrief.aI.
Or go direct to multiplayeraI.com.
Yes, that is two AIs in a row.
And I'm excited to join this experiment with all of you.
We'll also set up a community for this on the AIDB operator circles
to look for a link for that in the show notes as well.
All right, guys, for now, that is going to do it for today's AID Daily Brief.
Appreciate you listening or watching.
As always, and until next time, peace.