How to Build Team Agents

0:00Nufar Gaspar:2026 has been the year of agents. From OpenClaw at the beginning of the year to now platforms like Muse and GrokBot and Instinct that are getting people to actually take advantage of these incredibly powerful autonomous tools that are getting increasingly large portions of their work done for them, we really have gone from agents being the next big thing to just being here. The problem is, our work isn't just done alone. We tend to work in teams with other people. And yet up till now, most agents have been solo affairs, only covering the portion of our work that we do on our own. I think that is shifting now.

0:33Nufar Gaspar:A trend which I've talked about as multiplayer AI or shared or team agents. But what does it mean to even build a team agent? What are the types of considerations that go into it? And how different is it really than just building an agent for yourself? Those are the questions that I get into with Nufar Gaspar on this Operator's Cut edition of the AI Daily Brief. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.

1:09Nufar Gaspar:All right, 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 aidailybrief, or you can subscribe on Apple Podcasts. And to learn more about sponsoring the show, send us a note at sponsors at aidailybrief.ai. Just a couple other notes before we get in. Obviously, this is a pre-recorded episode. There are a bunch of things cooking today. We will have a lot to talk about. So we will be back with our normal format tomorrow. I also wanted to share a couple of upcoming opportunities.

1:40Nufar Gaspar:First of all, this Thursday, October 1st, we have a free live webinar all about building your personal AI benchmark. the whole idea is that when you get a new model like opus 55 or sonnet 55 or gemini 4 or whatever model comes next this will help you put together your own standard benchmark to better understand where that model is going to fit into your own process that is completely free and if you register you will get all the materials after even if you can't attend again that is coming up this thursday october 1st now speaking of training if you want to go a little bit deeper the next cohort of our super intelligent executive AI and agent training programs is coming up.

2:19Nufar Gaspar:The executive agent leadership program is where you learn how to build AI agents for real business needs, as well as building a playbook to scale them safely across your organization. And if you feel you need a little bit more background before you get into that, you can also do the executive catch-up program. The next agent leadership cohort starts on October 5th, while the next executive catch-up program starts a week later on October 12th. All right, with all that out of the way, let's talk about how to build team agents. All right, Nufar, welcome back to the show. We got an operator's cut today.

2:52Yes, happy to be here again.

2:54Nufar Gaspar:This one has its genesis in some conversations we were having as we were coming up into the fall around what we wanted to do with the, you know, this fall's edition of a free self-directed training program. And we were talking a lot about this idea of multiplayer AI and a shifting pattern from people just building solo agents that they were using themselves to a prediction that we're going to start to see. And I guess we're starting to see early evidence of more agents that live in between people's shared workspace. And this kind of just follows the natural way that people work. A lot of your work is done individually, but then lots and lots is also done at the intersection with other people.

3:33Nufar Gaspar:and that's where team agents can live. And as we were building out that course that's available right now at Multiplayer AI and just thinking about this concept more broadly, one of the things that we kept coming back to was that this is nascent enough that what it means to actually build a team agent won't necessarily be super obvious. And so the goal of today's Operator's Cut is to help actually think through how to build team agents, to understand what team agents look like, to understand in what ways they are different from or I think probably what we'll argue here, similar to the types of agents that people might have already built and where they can go from here.

4:09Nufar Gaspar:So super excited to have you back and excited to dive in here. Amazing. So I'm going to broaden your definition and I'm going to call them team agents. And the concept is teams that your entire team can work with, whether it's because work happened between them or just because they are something that can be shared across team members. And kind of the short version is that some agents should stay yours and private while others should become the team's level agents. And the ones that do become the teams need a few decisions made on purpose. Some of them, as you said, overlap with any good agent configuration, and some of them are more unique or at least more intentional.

4:44And that's what we'll walk through. And I wanted to start as a means of motivation to give you like two stories that you will probably recognize for you from your company or your ecosystem. So the first is about a person that everybody that I work with, every company that I work with has at least one like that. And this person, they really know how their price and exception work or what the data is all about or how our biggest customer setup was configured three years ago. And when they're swamped, then work has to wait for them because they're the only one who knows. And when they're on vacation, someone still calls them.

5:21And when they leave, a piece of the company lives with them. So in one of the companies that I work with, they had, I think, a person like that for each and every domain. So no one gets to take vacation without getting a call from their peers. And obviously that's not a desired state. The second scenario is work that nobody fully owns. So a customer can move from sales to marketing to customer success. And then sales made them a promise during the deal conversation. And marketing is running a campaign with slightly different messaging. and then customer success finds out these promises were made to them a few weeks before the renewal and each team has their own piece and nobody has the whole picture because the work sits between them.

6:02So to your point, and even if they are using AI in each step of the process, these agents don't talk to each other and only worsen the problem in many cases. So an agent that is built for the whole team can help with both of these problems and they are of course quite different problems. So there is another reason, I think, why this matters right now and why you should pay attention now, even if you feel a little bit like this is above your head. And that's the pattern that I think what we're starting to see across the most AI forward companies. And it goes in basically three steps. The step one is that everybody builds their own agents and they are happy with their productivity boost.

6:41Only with enough of those running around, we kind of get into an agent's pool. Lots of agents doing overlapping work, each maintained by one person, each with slightly different picture of the company. And each one stops being useful the day that the owner loses interest or leaves the company. And then what you see in the most AI forward company, they started to merge some of those agents into team level agents. Those will typically be much fewer agents, much broader in their scope, and each with a named owner and used by many people. And ideally, refined over time as the team learns what they should and shouldn't do.

7:17And this is happening very publicly. There are many companies already talking about it. I think you mentioned Avery's experience. They started by giving every employee an agent early in the year. And then by May, they have moved to shared team agents. Sierra merged many of their agents specialists into one. Shopify is internal agents. So we see a lot of these in very public speaking companies all over the place. And most teams that I see are probably either in step one or two. But I think that it's very important for all of us to look at these AI forward companies and understand how to get to number three and how to do it properly.

7:51And that's the entire purpose of today. So to make sure that we are talking about the same thing, because there are multiple names to basically the same thing. Some people, including yourself, call it multiplayer AI. You probably also heard shared agents. Some people refer to them as AI teammates. And even company brain is sometimes thrown into the mix or interchangeably used to mean agents being used with shared knowledge across the company. I'm going to refer to them throughout the episode as team agents. And what I mean by that is we have one agent that many people talk to with shared knowledge, shared memory, and one configuration.

8:28The instructions, the skills, the access, and the owner, they are all shared. And you might be thinking when you hear me saying that we already share skills, right? Most companies have an amazing skill library or working on a skill library. And that's awesome. And that's great standardization of how you do the work in the company. But a skill is a playbook for a specific task where a team agent is something that your whole team works with on diverse set of tasks, ad hoc as well as repeated stuff. And it does carry the team knowledge, remembers what it learns and using the team level skill if you have them.

9:03Those, of course, can also tap into skills marketplaces and so on. So a skill library perhaps is one of the ingredients, but they are not one and the same. And I want you to today think about how and when to start building your next team agent. And one more note on scope, because there is a lot of excitement right now about all of the personal agents. I'm talking about Muse and Instinct and some of the other in this category. Those are for like a home or private life. Today, the focus is going to be on work. So that's one thing to make sure that it's clear about the scope. We're talking about agents that you build for your job.

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13:07do apply to any agent that you build or use. And a team agent just makes some of them even more critical. And even if you are working solo and you have a team of agents or you're contemplating building a team of agents, you have the same decisions. The other player in your ecosystems are probably not your peers because you work alone, but perhaps you're building it for your customers or you're building it for a future you to make sure that it's robust enough and representative enough of diverse set of work. So that's the motivation or who should pay attention. And one thing that I wanted to make sure that it's very clear is that not every agent should be shared.

13:44There is a dial here or a spectrum with three settings. We have a private agent that's yours for your work with your taste and your access. For example, my own social media agent stays private. I'm probably not going to be able to share it with anybody because I'm the only one that wants to share or write in social in a specific way. So nobody should ever sound like me. And then we have shared knowledge. Those can be shared knowledge and skills, but still private agents, meaning the team maintains one body of knowledge. For example, what we sell, how we work, what our words mean, often with a shared skill library, and everybody points their own agents at that.

14:22But this is where the skill library lives, by the way. And it's often the right answer, and it's the easiest place to start. Another concrete example, say every salesperson has a prospecting agent tuned to their own style and their own preferences. keep those agents as they are because every salesperson wants to have their own voice, but give them all the same well-maintained picture of the ideal customer and the messaging for the company. So that's a hybrid mode that some companies or some use cases should remain. And lastly, we do have the team agents where we have one agent that many people work with that has its own job and its own owner and agents can move along the dial.

15:02And if you have a scenario where your colleagues keep asking to borrow your private agent, that's probably a sign that you need to make it a team agent or to consider sharing it with others. So the next question that I want to answer is, are all team agents from the same archetype or do they all follow the same type of use cases? And the answer is not. Like across the teams that I work with, I can roughly categorize the existing or future built team agents into four kinds. And knowing which your kind is, It helps you not only identify use cases, but also refine the use cases. And also it can tell you what to pay attention to in order to get it right.

15:41So the first type of team agent, I'm calling it the expert agent. It's the one person's know-how or one small team's know-how. It's available to everyone who depends on it. You'll recognize it by the person who can take the vacation. You remember from the beginning. If you want another example, it can be the data agent that can answer any data question across multiple departments in the company or a pricing and deal desk agent that serves a lot of go-to-market organizations, compliance agent, and so on. In order to get it right, the knowledge has to come from the experts that holds it in the company.

16:17They need to be interviewed. You need to collect the answers they already gave in multiple forums, whether those are direct messaging or emails in other places. And the experts have to be involved from day one. Because for them, this is what finally makes the vacation possible, but also a lot of job insecurity. So tread carefully when working in this domain. The second type is what I refer to the common work agent. That's the scenario where many people are doing similar recurring work with one shared way to do it. The way to recognize a use case that falls into this category is when three people have each built their own version of the same agent.

16:58You can think about a team research and meeting prep agent. That's a very classical one. Or marketing teams content agent. And I'm sure you can think of others. In order to get this archetype right, you have to agree on how work is done. And it's easier to say than to actually execute because you're merging the best of three versions. And that's really a conversation about what you agree in terms of the standard for the company. So an interesting conversation at the very least once you start contemplating unifying an agent like that. And then we have the bridge agent. That's the work that flows between roles where nobody can do it alone.

17:35You'll recognize it when the handoffs break and every stage has to explain the context or the agents have to somehow work together between different departments. Example can be a customer agent that spends sales, solution, customer success and delivery. That's a very classical one. And to get it right, we have to have each function, their own piece of knowledge that is being fed into this agent. And people with different access will be eventually using that. So we have to also pay attention very, very carefully to permissions and you'll have to work hard through that. And lastly, we have the chief of staff.

18:09That's the agent that owns the team operating, like operationalizing of the day-to-day work. It can be the decision, the commitment, the status, onboarding. And you will recognize this one when the team keeps repeating itself and new joiners take weeks to find their footing. And in order to get this one right, what you need to do? You need to clearly define what it is allowed to learn. How can it learn the processes and the ongoing? And how does it do so automatically, which is not very trivial? And then there are also, of course, many questions around permissions and so on. So while you're thinking about these archetypes and which one of them might fit some use cases that you were pondering or that you should be thinking about, and before I give you the playbook on how to actually build these team agents, a quick detour, because I do want to give a quick reality check.

18:56there are some signs that a team agent is the wrong move or at least not the right move for you at this moment. So one indication where you shouldn't build a team agent, at least yet, is when taste beats standards. If different people truly need different answers or are not willing to agree on a standard and their own judgment or their own voice is the point, those need to remain private agents so people can remain authentic and not have to fight about the ground truth. And the second indication not to build is nobody can own the knowledge. If the team cannot agree on how the work is done or nobody is willing to own and maintain the shared knowledge over time, the agent will drift within weeks, sometimes within days.

19:39So sort out the ownership before you go and build the team agent because that's going to be a no-go. And lastly, whenever you're realizing that trying to build a shared agent, a team agent, only complicates more than it simplifies because you have conflicting needs or tangled permissions, endless coordination. If you realize that the result is more work than what the agent can provide for you, that's the answer. Don't build it, at least not until you are able to untangle some of the complexities. And of course, notice what's missing from the list, sensitive data and high stakes. Those are design questions and they shape how you build it, which is where we're going next.

20:16So I don't think that when data is overly sensitive is a reason against. It's just something that needs extra careful attention. In order to build the candidate use case that hopefully you've gone through the decision checklist that I shared before, it comes down to five core design decisions for your agent. It goes through what it does, where it lives, what it knows, what it can touch, and how can you run it. These are the core questions. In order to make it more concrete, I'll use one example, the whole way throughout. So it's going to be a customer agent and it's going to be the bridge kind, meaning one that holds everything the company knows about each customer and everything we've promised them.

20:56And it's probably going to be used by sales and solution engineering and customer success, delivery and so on. They will be using that example agent to do various customer related activities. So let's break down some of these decisions to make it actionable. So first, the decision that you have to make is what it does. A quick caveat here, there is a lot of scoping that is very similar for any serious agent at work. It needs to have a clear job and a definition of done and a list of what it does and do. I'm going to stick here to what changes when it's built for a team versus an agent that you build just for yourself.

21:31So the first decision is who it serves by role. For example, sales asks it different things than delivery does. So write down each role and what they'll come to the agent for to make sure that you're covering all the scope. And of course, you can aim for one broad area of work because I think the team agent should be quite capable agents. Otherwise, it's harder to justify their existence. In our example, I would expect that the customer agent will be able to prepare a meeting, answers, where do we stand with this customer? We'll be able to flag promises that commit other teams and draft every handoff.

22:05for example, of a quite broad scope. And what keeps it focused is the area of work. And that is primarily, in our case, customers. I want you also to pay attention to the team don'ts list. This is the part people often tend to skip. For example, it never makes commitment on someone's behalf or it never settles disagreement between people. Those go to its owner. You can think of another such examples in your case. Also make sure that the don't list includes permissions and data handling stuff. So, for example, it never carries information from one private space into a shared one. So it never discusses one customer in another customer's space.

22:46And it doesn't speak for one person to another. And of course, like with any agent, ideally start with narrower scope, reading and drafting. Only when it earns sufficient trust and was validated enough, then you can increase the scope as you gain more and more confidence. So that's the first decision and we can move to the next one. The second question will be where the agent lives. And this is the question that gets asked most. So let's be a little bit concrete. Basically, if I'm trying to make it as simple as possible, there are roughly three ways to share an agent from the simplest to the most involved.

23:20The simplest method will just to create a shared folder with your own tools, like the tools that your company already owns. And then each person just point their own tool to the shared agent. and the folder will include instructions and potentially how to store new information as part of the way the agent overall behaves. That's a very naive and basic way, but as a stepping stone to building shared agents and team level agents, that can be a very good start, especially if all of your team members are already using similar agentic tools or agentic tools that can point to folders as sources for their information.