How to Build Team Agents

Nathaniel Whittemore (0:00)

2026 has been the year of agents. From open-claw 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, 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.

All right, friends, quick announcements before we dive 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. 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. 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.

Nufar Gaspar (2:34)

Yes. Happy to be here again.

Nathaniel Whittemore (2:36)

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 this fall's edition of a free self-directed training program.

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. This 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, 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 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. So, super excited to have you back and excited to dive in here.

Nufar Gaspar (3:55)

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. And 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. And 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. So 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. Only with enough of those running around, we kind of get into an agent's sprawl. 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.