The Multiplayer AI Sprint: Build Your Team’s First Shared Agent

0:002026 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. Alright, friends, quick announcements before we dive in. First of all, thank you to today's sponsors, KPMG, Blitzy, Harbor, and HyperAgent, to get an ad-free version of the show, go to patreon.com slash AI Daily Brief,

1:01or you can subscribe and upload podcasts, and to learn more about sponsoring the show, send us a note at sponsors at AIDailyBreathe.ai. Finally, last call and blissfully for those of you who are not signing up, the last time that I will be yapping at you for a while about our super intelligent agent executive programs. The next cohort starts this week, so one last time you can check them out at training.bsuper.ai Welcome back to the AI Daily Brief. The day that this episode comes out is Labor Day in the US, 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.

2:13Agents would come a little bit later. In February, we released Clock 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 OpenClock. Agents OS came just a little bit later, and was the more mature grown-up version of Clock Camp that was not only platforming to a lognostic, 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.

3:14But the other thing that they all are, ultimately and at core, is individual. Now with the 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 time is spent working with others. Another survey found that 57% of time is spent communicating, i.e. meeting, email chat, etc. versus 43% creating individually, and yet another survey found that about 60% of time goes to work about work, communications, search, coordination and process.

4:15In 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 sub-agents 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.

5:21And 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.

6:28The 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 clockamp moment, the team at every started their agent 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.

7:31A 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 Claude and Slack that existed before. The original Claude integration in Slack was one where an individual could tag in their personal Claude bringing them into a conversation and then sending them off to do things and managing their personal Claude from that work interface. Claude Tag was something different. Claude Tags are instances of Claude 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 Claude to do the work. In other words, when you tag in Claude now in your coding channel, it is not your personal Claude that you're tagging in but the shared Claude agent that works across your team. The team at Anthropic wrote, Tag in Claude comes with a few new advantages. At Claude is multiplayer. Within a given Slack channel, there's one Claude that interacts with everyone.

8:39That 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 Tag in 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 needs 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% of their product teams code was now created by their internal version of Claude Tag.

9:41I.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 hears 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.

10:46Colin 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. A new study from KPMG in the University of Texas at Austin found that when people work with AI, similar skills don't guarantee similar outcomes. Researchers studied more than 500 early career professionals and found that the best performers consistently amplify the value of AI by guiding, evaluating and refining its outputs.

11:46These top performers, called AI amplifiers, weren't defined by what they knew alone, but by how they worked with AI. Learn more about what separates AI amplifiers from everyone else at KPMG.com slash US slash AI amplifiers. Every AI coding tool on the market does the same thing first. It starts writing code. Blitzy does the opposite. Before writing a single line, Blitzy spends days reverse engineering your entire code base. Thousands of agents ingest millions of lines mapping every dependency, every undocumented constraint, every architectural decision made over the last decade. The result is a dynamic knowledge graph that understands your software the way a principal engineer would after 30 years in the building. Other tools guess it context with grip searches and markdown files, Blitzy never guesses. It builds true understanding first, then delivers over 80% of entire software epics autonomously. Validated end-to-end tested production grade pull requests. That's why Fortune 500 engineering teams trust Blitzy with the code bases that matter most. See for yourself at Blitzy.com that's B-L-I-T-Z-Y.com.

12:47Every episode we cover the competition between OpenAI and Thropic, SpaceX AI, Google and Meta. Chances are you've already formed an opinion about who's leading, but every AI lab is taking a different approach, building different technologies, forging different partnerships and developing a unique ecosystem. Harbor Capital Advisors AI Lab Ecosystem ETF Suite gives investors a way to gain exposure to the AI ecosystem they believe is best position for success. Search Harbor AI Lab Ecosystem ETFs wherever you invest or follow ad Harbor Capital on X to learn more. Visit harburecapital.com for a prospectus containing investment objectives, risks, fees, expenses and other important information. Reading considerate carefully before investing, risks include principal loss and artificial intelligence related risks. Harbor ETFs are distributed by ForSide Fund Services LLC. Harbor is not affiliated with AI Daily Brief and the funds are not affiliated with sponsored by or endorsed by any AI lab. This is a paid advertisement and not personalized investment advice. Investing involves risk, including possible loss of principal. This episode of the AI Daily Brief is brought to you by HyperAgent, where you run fleets of agents your team can manage together. Forget local agents and chat workflows waiting on your laptop to be prompted.

13:49HyperAgent deploys always on agents in the cloud, doing real work across the tools your team already uses. Marketing agents turn competitor moves into landing pages. Sales agents enrich leads, draft emails and updates the CRM. Ops agent chases the paperwork and tracks the budget. Every agent has access to shared context and follows your rules about scope and approvals. It's time you add agents that feel like teammates. Higher yours at HyperAgent. Get $100 in credits at hyperagent.com slash AI Daily Brief. So now you have the very advanced users of the every team, the open-cloth team and the anthropic team, all shifting from this individual agent behavior to this shared team multiplayer agent 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 commentator partners share a request for startups.

14:50And 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. Why commentator 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 working 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 a 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.

15:50Anyone 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 team 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, analyst building a model, and marketer 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 in AI well. Enter this Falls free self-directed education program. The multiplayer AI sprint for teams. You can find this linked from either the aidelebrief.ai website or the extremely clever direct URL,

16:50multiplayer AI dot AI. 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 could 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 GP to your clawed? 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.

17:54But 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 apart 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 all together 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.

18:57You 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 their 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 guard rails and constraints and governance protocols that your team will face as well.

19:57That 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 two or sprint two 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 share 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 R.A.I 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.

20:59It 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 stainless cost.

22:00How 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 unreal work. Now certainly if you have access to cloud 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.

23:03That'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.

24:06In 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 in 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 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.

25:07Again, you can find all these links at aidelybrief.ai or go direct to multiplayer AI dot AI. 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 AI DB operator circles to look for a link for that in the show notes as well. Alright guys, for now that is going to do it for today's aidely brief. Appreciate you listening or watching as always and until next time peace.