00:07:15 · Dan Rogers
[00:07:15] S0: Yeah. Well, if you start back in the beginning eighteen years ago, we solved a simple problem that was fairly ubiquitous, which is when you get more than two people together in a room and you get them to work on something that nowadays we would call a long horizon project, so something that takes a lot of time, then you have a coordination challenge. At some point, you're going to need to figure out who is doing what by when and how we're gonna get it done. And so you end up trying to create some version of what we would call a pyramid of clarity, which is, okay. This is the goal we're trying to do. Break it up into these projects. These are the tasks. Let's assign them in this way against this. And suddenly, you need a way of manifesting that pyramid of clarity. This is the original Asana. And this is why we're ubiquitous today. 300,000 customers, 85 of the Fortune five hundred. Because it turns out coordination is a real problem. And so you could actually... Today, I don't think it would be an exaggeration to say any team that is working on a long horizon complex multidisciplinary project is probably using Asana or maybe some variant of, you know, new entrant, a type of Asana. But the change that there is afoot is that the modern team is no longer just humans. So it's not two people in a room. It's two people and five agents in the room. But it turns out that the agents also need coordinating. As you work individually with a, you know, some chatbot, it's great. They provide amazing answers to lots of things. That's not quite the same as coordinating that work inside a workflow. But the modern team is going to need to take all of those outputs from LLMs and integrate that deeply into how they work. So Asana is fast becoming the operating system for human agent teams. If you think about maybe the models, the models you can think of them as providing answers, providing quality text, providing documents, providing code. That's not the same as coordinating action across teams. So the models themselves become quite interchangeable. Some people say the models might even be commoditizing. What happens on top is you need two things. You need context, which is really a good sense of, again, who's doing what? Which systems do we need to call on? What needs to happen next? How have we done this in the past?
00:09:40 · Dan Rogers, Guillaume Moubeche
Are there some rules of the road that we need to be following? Are there some guidelines that we need to be following? Is there some governance that we need to be following? And then what I would call the harness, which is really actually making that work happen, pushing that work out to the right people, taking action, involving those people, and then the right moments making sure that they can prove things and that it's fully governable, and in the end, ultimately a repeatable process. So this is where you need something like Asana, which is the human agent operating system, which is really that enterprise ledger, that strata for humans and agents to collaborate off of off of the same living plan. [00:10:24] S1: So your view is down the line to kind of have like agents perform like as many tasks as possible and get all the context from Asana while always like keeping a human in the loop to validate like that the work has been done properly, that the project is going forward, etcetera? [00:10:44] S0: Yeah. That's a very nice description. Maybe I'll make it even more real. So today, as an example, we have a thing called teammates. These are our agent characters. We have 30 pre built teammates. They perform a particular package set of actions that you have scoped and defined. With AgenTic Work Management, which is really where our platform is headed today, we launched it today actually. So it's very, you know, lucky to [00:11:08] S1: be Congrats. Conversation.
00:11:10 · Dan Rogers
[00:11:10] S0: Where with AgenTic Work Management, these teammates make themselves available to you as you're working in Asana. So it might say, hey. I noticed you're working on a product launch. I'm the product launch assistant. Would you like me to help? Hey. I've noticed you're working on some pricing thing. Do want me to help? I... I'm like a pricing analyst. Or, hey. I noticed you're doing some company competitive research. Well, I'm your competitive research assistant. So we have 30 of these people agents they make themselves known to. But what's beautiful about Asana is because we have this multiplayer coordination, any of those agents can interact with many team members. Many team members can improve them, correct them, and say, actually, no. No. These are our competitors. Please refer to this knowledge base. Now that competitive analyst that's been maybe informed by two team members can be used by 30 other team members, and it knows who the competitors are. And as it's coordinating actions, it's doing so again within Asana. It's literally taking tasks, performing them, writing it back to Asana, letting the humans know what's happened. So suddenly, this is fully coordinated and fully contexted. And it's live and a living plan because everything it does helps the very next run of that agent. So you'll end up with this tapestry of these agents working deeply within a workflow. It's super important. It's super different than how to think about some AI bolt on feature or even interacting with a chatbot. This is real enterprise productivity. It is kind of a game changer from where we've been. Where we've been in this evolution was single player interaction with models. Good, you know, helpful from a personal perspective. This is not how any work happens. The work is that myriad of handovers, the myriad of crossovers, the myriad of little things that need to happen to keep things moving. So for example, maybe you wanna make a change to some software and maybe you have like a billing system and you want it to do some new calculation. Well, it turns out you probably need to inform your customers. It turns out it's gonna affect the way you recognize your revenue, you're gonna involve your finance team. You've got some engineers that need to work on it.
00:13:12 · Dan Rogers, Guillaume Moubeche
You probably have some folks, you know, product management needs to describe this new thing to your customers in turn, etcetera. So let's say there's like five or six parties just for a single change in your product functionality. It's really the agents that we wanna do a lot of the coordination. It's the agents we want to figure out. Okay. Well, this is probably the 25 tasks that are gonna need to happen. And by the way, I'm gonna need to hand it over in this way. And realistically, we should say this is gonna take six weeks. It's one of the agents you want to kinda create that based on everything you already The humans meanwhile, you want to say, yeah, I wanna approve this. I wanna take a look at this. No. This is the way we like to do this. This is the way that things are done here. This is our taste that we want Nida to perform. And then at the end when it's all finished, it's the human that kinda says yes, this met our specification. Great. And now we can do it even better the very next time. So that's kinda human agent operating system in action. [00:14:10] S1: And for the models that you are using, are you leveraging like existing models from companies or have you like also built in your own model directly?
00:14:10 · Guillaume Moubeche, Dan Rogers
[00:14:10] S1: And for the models that you are using, are you leveraging like existing models from companies or have you like also built in your own model directly? [00:14:20] S0: Well, as I would say, from a model perspective, they become quite interchangeable. So we're very focused on being really the kind of the quality and performance of those agents for our customers, which actually does mean certain tasks get routed to certain models based on their efficacy of delivering them. And so under the hood, yes, we have interchangeability of many of the models. But we also work with the foundational models because some people don't actually want to work in the canvas of Asana. They want to work in the canvas of their chat screen. And so for things like, you know, codex, for things like Claude, for things like etcetera, we have this MCP integration where if you are an Asana customer and you're working with one of those foundation models, you can actually view tasks, hierarchy, project, and you can talk to Asana really through those chat discussions, and not even have to come into Asana. And so we have a very nice relationship as they kind of fold into that tapestry.
00:15:19 · Guillaume Moubeche
[00:15:19] S1: The reason I was asking is because I think the q three results were actually like really great, and I think AI teammates from what I saw went from 17% to like 25% of net new ARR, which is huge and means like the adoption is actually like really, really nice. Also, what I've seen is like, you're putting AI in most, if not every paid tier without changing prices, which means that you are also absorbing like a potentially a token cost. So can you like maybe walk me through, like, because for me it's the same, you know, like obviously I built a software company, margins were pretty much like, you know, like insane, and with AI token, obviously you need to make some sort of arbitrage, but it feels like you've already managed to still get like a really good margin on top of the input cost of token. So maybe you can walk us through like your strategy regarding tokens and how you manage it.