From Workflows to Agent Teams: Wade Foster on How AI Reshapes Enterprise Automation

Has AI been an accelerant or a threat or both for your company?

I think it's both for most companies. There's this incredible technology that has emerged and, you know, I think everyone is figuring out what are the use cases that are super valuable in the future. And so the companies that can leverage their advantages today to build those capabilities are going to benefit massively in the future. And we think we have some of the, like really important assets. Like the fact that we are connected into over 8,000 different tools is a huge asset for us. But any company that has, you know, sort of existed for a, you know, any mere amount of time that says, oh, don't worry about AI, it's not gonna do anything to us, I think is making a foolish choice. And so, you know, we've been like very aggressive at trying to reposition our company in the age of AI to take advantage of the opportunity and not let, you know, others displace us.

This is AI First with Adam and Andy, the show that takes you straight to the front lines of AI Innovation in Business. I'm Andy Sack, and alongside my co-host Adam Brotman, each episode we bring you candid conversations with business leaders transforming their businesses with AI. No fluff, just real talk, actionable use cases, and insights for you. Greetings, everyone. I'm so excited for today's episode. We have Wade Foster from Zapier. Zapier makes you happier, the automation company. And today we're going to get into agents and the future of work. And really two things we want you to get from this conversation. One is we want you to have a clear understanding of the evolution from automation to agents, which have just occurred and have, um, make sure that you, you understand that. And then also a simple mental model for the future of work, which we think really involves managing agents. So those are the two things we want you to get from this conversation. With that, allow me to introduce you to Wade Foster.

Wade, welcome.

Yeah.

Howdy everyone. Thanks for having me.

And of course, my co-host Adam. Good to see you again, buddy.

Yeah, you too. Really excited to talk to Wade.

Wade, before we get started, just if you wouldn't mind, just give a, brief introduction to you and like, what'd you do before Zapier and when Zapier started and what is Zapier?

Yeah, sure. So I'm Wade Foster. I'm one of the co-founders and CEO here at Zapier. We founded the company in 2011. Before that, I worked at a— in marketing in a mortgage company for about 10 months. And before that I was in school. So just doing what school kids do.

Straight out of the mortgage industry.

Yeah, there you go. Something like that. But, you know, I think the, the reason we chose to work on Zapier is my, my co-founder Brian noticed that when you would go to the help forums of all of the emerging SaaS products at the time, so think like Salesforce, Zendesk, Dropbox, you would find this common pattern in the help forums. Customers would be asking, when are you going to integrate with XYZ product? And then in the help thread, there would be all these comments from customers saying, plus one, me too, I need that, I need that, I need that. And then eventually a product manager would chime in and say, Thanks for the feedback, everybody. We'll take a look. And if you haven't really worked at these companies, you know, that's more or less code for not gonna happen. It's gonna get dumped in a backlog somewhere. And so our kind of thinking was, huh, maybe we can make it really easy for a non-technical person to build these integrations themselves. They don't have to know what an API is. They don't have to know what code was. And this was really appealing to me, 'cause I was working in email marketing at the mortgage company. We were trying to do a Marketo implementation, and Marketo had this old school SOAP WSDL API. I'm a bad engineer. I was just having a bad time. So I thought, hey, if this product existed, I would just use this to build Marketo's integration with our, our CRM. And so Zapier started with that. It's like, how do we just connect as many tools as possible for folks? And of course, over the years, you know, we've added workflows and workflows became really popular. And now of course we're in the age of agents and AI workflows and all this sort of stuff. And those are increasingly becoming the most popular thing on Zapier.

Wade Foster: Wade, like, let's make sure we get our definitions correct. So can you, for, for, for our audience, can you define automation and agent distinctly?

Well, you know, I think I would probably make the difference, the, the, the delta between, uh, the difference between a workflow and an agent. Like to me, automation is kind of like the superset of those two things, but a workflow is deterministic. It's a human, it's code that is deciding what happens next. You're saying, I wanna start here. I wanna do this next. I wanna do this next. I wanna do this next. And then at the end, I wanna have this output. Now you can have AI in the mix there, but you still have a very concrete set of steps that are going 1, 2, 3, 4, 5. Whereas with an agent, you are saying, I'm gonna give this agent, the LLM, a goal. I'm gonna give it tools that it can go access, and now I'm gonna let the LLM decide what does it do, 1, 2, 3, 4, 5, and then it's supposed to deliver the output. And that's the key difference between the two. And neither one is better or worse than the other. You get different outputs and different trade-offs depending on what you get. But at the end of the day, you, you kind of wanna pick the like best tool for the job depending on what you're trying to solve. And both of those things are, are subset of automation.

With AI, I mean, you certainly started the company well in advance of ChatGPT 3.5 workflow. You know, your, your, I know your tool has exploded in use in the last 3 years alongside. What's the last year been like at Zapier? Bring us, bring us current with what you're like, what you're wrestling with and how things have changed for you at your company.

There's a couple of key innovations that we've been working on. One is a Zapier Agents product. This wasn't possible, you know, 3, 4 years ago, but now you could come in and you can create an agent where you give it a goal. You give it access to tools and the LLM goes and operates on it. It's a really simple experience to set these things up. It's probably, it's actually much easier than kind of like traditional workflow configuration to build these things. 'Cause all you have to do is think through, well, what is it that I want to achieve? You write it down like steps 1, 2, 3, 4, 5, and then you authenticate your tools. So that has been one really popular addition to the platform. The second thing that we've spent a lot of time working on is our Copilot experience, which exists to build both agents and workflows. This, I think, has probably been the most important unlock. LLMs are fantastic.

And Wade, Wade, let me just interrupt for a moment. When you say Copilot, do you mean Microsoft Copilot or, or—

No, this is Zapier's Copilot. Yeah. This is a tool that you can— is a chatbot that you talk to that builds automations for you, either an agent or a workflow. And this is so powerful because now you don't actually have to know what exactly am I trying to configure. You can just describe what you wanna have happen. And then on our side, we figure out, do you want an agent? Do you want a workflow? Is it better to be more deterministic, less deterministic? And that I think is one of the most beneficial tools that we built. I'd say the third thing that we built that's really important in the last couple years is our Zapier MCP server. And the reason this is powerful is you can bring the underlying capabilities of Zapier into your agent tool of choice. So this might be Claude or ChatGPT. It could be Cursor or Claude Code. And now you have all of the like action inventory that Zapier has. So you can say, while you're chatting back and forth with your AI friend, you can say, hey, can you go fetch my emails from today? Can you go look up what calendar events are coming? Can you go do research on these things? Can you go generate this for me? And you get that sort of very fluid experience of your AI having like arms and legs to go do stuff for you that is really, really powerful. And so I think those 3 innovations have been really important for us in the last couple years.

Has AI been an accelerant or a threat or both for your company?

I think it's both for most companies. There's this incredible technology that has emerged and, you know, I think everyone is figuring and out what are the use cases that are super valuable in the future. And so the companies that can leverage their advantages today to build those capabilities are going to benefit massively in the future. And we think we have some of the, like, really important assets, like the fact that we are connected into over 8,000 different tools is a huge asset for us. But any company that has, you know, sort of existed for, you know, any mere amount of time that says, oh, don't worry about AI, it's not going to do anything to us, I think is making a foolish choice. And so. But we've been like very aggressive at trying to reposition our company in the age of AI to take advantage of the opportunity and not let, you know, others displace us.

Adam, let's get you in here. What, what you've been listening to Wade for the opening here and unusually, unusually quiet this, this Tuesday morning.

Yeah, I'm, I'm, um, I'm actually fascinated by the way you're describing it, Wade. Like it actually, I've learned a lot just listening to you in the first you know, 10 minutes here. So the question I have for you is, will, do you guys, first of all, do you have an integration already with, I'll make up like Claude or ChatGPT. I'll pick on Claude because they're sort of in a weird way, the most sort of advanced as it relates to this conversation. So Claude Code or Claude Cowork, like, is there already on Claude Cowork, if I went to Claude Cowork and I said, Hey, I've got this idea for, you know, this thing I want to do to, you know, automate a workflow in some way. Does it have an integration with Zapier already where like it would say, fine, I'll go grab Zapier and Zapier's already got the integration down and I'll, I'll sort of stitch this stuff together and Zapier becomes an important part of the glue. Is that sort of in place right now?

So there's two ways that you can integrate Claude with Zapier. The first is through Claude connectors. And so there you can turn on the Zapier MCP setting, and now you have access to the entire library of Zapier connectors right within Claude, Claude Core, Claude Code, you name it. So that's way one. The second way you can integrate Claude with Zapier is in zapier.com itself. So if you're building a workflow and you want to have an AI step, you can specifically select any of Anthropic's models. So Opus 4.6, Sonnet 4.6, or some of the older models as well too. And you can use that to power AI steps within your workflows. So those are the two key ways you can work with Claude or Anthropic's ecosystem inside of Zapier.

Right. And I assume that's the same with, in theory, with ChatGPT or Codex or, um, or Gemini, you know, or whatever. That's the same idea. Yeah.

You got it. Yep.

Yeah.

But to your point, Claude is definitely, I think, a little more forward-facing on this side. This particular type of work today than the other two.

Yeah.

Yeah. It, it seems like that right now. Like, I mean, it's, it sort of came out of nowhere, it feels like, but it, it, I mean, it didn't, if you're paying attention to Cloud Code, it didn't come out of nowhere, but I, for those of us like me that are not coders, like, it was like, oh my God, all of a sudden I just wanna live in cowork. I never saw that coming.

Mm-hmm.

Let's talk a little bit about, uh, let's go back to something you said a second ago, which is like workflows versus agent. Do you feel like we're pretty quickly going to be in a world of agents where we as individuals, whether personally or at work, that we're just sort of working with our agents and our agents are helping us figure a bunch of stuff out and including figuring out how to automate a workflow or how to define a workflow, how to automate a workflow. Like, does it become like this giant black hole of like, it's just like we we all just end up using agents for everything and then agents figure out what are their best tools and the best everything. Like, can you kind of comment on, you know, because you've been living in this world and you've been building this stuff and you're, you're kind of in the center of it. Like, help us think about how you, Wade, think about agents in the agentic era.

So I do think that we are going to be in a world where our agents perform increasingly more and more tasks for us and we are interacting with them. The way developers have experienced this is their first experience was with like tab completion inside GitHub's copilot tool. But increasingly, as these tools have gotten more and more sophisticated, the developer's role has become more abstract to the point where with the, the latest batch of models that have been released, you're hearing more and more very senior developers say, I rarely look at the code anymore, which is a remarkable statement. They are prompting the code, they're doing systems design with the code, they're having the code do review, its agent review the code, its code itself. And so you're seeing this developer take on more abstract tasks and delegating more of the code creation and code review to the agent itself. I think this will happen across, you know, all of white-collar knowledge work. And I'm seeing it even for myself with my day-to-day work. You know, I'll have an agent that will prep me for a meeting instead of me going and doing the research directly. I'll have an agent that goes out and drafts, um, you know, like status updates or intro emails after I meet with a customer, for the AE team or updates to CRM rather than me going and updating the CRM manually. So, you know, I think there is a huge amount of work that is going to be delegated to these agents in the future. And our job as humans is going to move up that abstraction layer.

Wade Foster: Wade, I'm curious if you'd comment on what you've seen from your customers in terms of their willingness and ability to trust agents?

Well, I think this is one of the most crucial challenges today is that agents have really two tricky challenges compared to workflows. They are generally less reliable. They're probabilistic. They, they have agency to choose how they go and act and behave on these things. And so if you really care about a very specific output, your agent is probably going to underdeliver compared to a workflow. The second thing an agent struggles with is it's more costly. They're burning lots of tokens often to go figure out what that task is, especially if your task is more complex. Whereas a pure workflow is going to be deterministic code, raw API requests that are just not very costly at the end of the day. And so my general advice for folks is that they probably should be running more things as workflows if they can. That does not mean that your workflow may not have AI as a component of it, but if you care about cost, if you care about reliability, you're probably gonna wanna run the thing closer to a workflow than to an agent. Now that does not mean that when you go build the workflow or build the agent, you're not having a—

Wait, Wade, what?

Well, because you get the reliability and you get the cost advantages from a workflow. Like generally most those two things, I think it's hard to imagine for how many use cases you're not gonna care about those two things in an enterprise setting. Now, I find that where agents are very helpful and where LLMs are very helpful is in the creation of those tools. You're often trying to prototype or figure the thing out. You're going back and forth. You're not exactly sure if this is what you want or what you don't want. And so in that world, agents are so, so valuable because you're still trying to figure it out. But once you've figured it out, then you're like, okay, I want to lock this in.

Got it.

Yeah.

Yeah. Go ahead, Adam.

Sorry, Andy. Yeah, just to jump in, like, that's fascinating, Wade, because you're right. Like, it's— my brain is tracking this and it's sort of exploding a little bit because it's like, whoa, I could use an agent to help me figure out a workflow and actually build a workflow, an automated workflow in a way that I, you know, my, particularly if I'm relying more and more on an agent to sort of do things for me and I'm sort of used to like checking their work because they're, they're not totally reliable and they can make mistakes and they've got some agency. So you start to learn. To like rely on an agent, but not, but trust, but verify, but it gets kind of wild because what you're describing is agents can help you build these workflows and then so that they're different, but you're using one to help you with the other. And so that's where my head's kind of going like, whoa.

Well, and I, if you want to have your mind blown a little bit more, so one of the challenges with workflow—

Sure. That's why, that's what we're here for.

Yeah.

Well, so one of the challenges with workflows is that they're, because they are deterministic, they can break because of like all sorts of stubborn reasons, like an API changes or, you know, a site is down.

Right.

And when it breaks, what happens? Well, workflow, it has no way to like fix it. It's just kind of like it's broken. Dunno. So this is where it gets really interesting to say, well, what if the workflow is backed by an agent. And so when it, when it fails, the agent is then called in to troubleshoot and say, huh, this error message came back. I wonder why.

Right.

Here's 3 ideas I have for how I can maybe go fix it. And so you can start to see how the answer is not workflow or agent.

Right.

It's both. Like these tools are so valuable for different reasons.

So Wade, in that example that you just used, you talked about workflow and agent, but you did not mention human. What is, what is the role of the human in that?

Well, so I think, I think humans are gonna be designing these systems. Like we're gonna increasingly be overseeing them saying, okay, how, how does, how do we configure this system to work? And so you're, again, you're moving up that abstraction layer to say, here's what I want. Here are the goals that I have. Here's the outcome I'm trying to move it, move towards. Here's the data I have available to us. Let's pull this all into a working environment. And then you're going to be prompting and having it build this stuff up. You know, I think then our job is not to, like when the agent makes a mistake or the workflow fails, where I see a lot of folks operating today, which I think is gonna increasingly change, is a lot of folks when the workflow fails, they say, okay, me as human, my job is to go fix the output. I'm gonna go say, okay, that wasn't quite what I wanted, so I'll just go fix the last mile myself. But I think increasingly what humans will be doing is saying, the output was wrong. I need to go fix the input. Like, Why did this output generate this thing? So what instructions do I have to update with my agent? What workflows, like where, where can I like tune the workflow to tighten it up so I get a more consistent output on the other side? And then they're gonna rerun that and say, okay, it did get the right output this time. Great, let's proceed. And so, in a lot of ways, I think you might be able to think about it like a, a modern manufacturing facility where instead of like humans performing every minute tasks, now we're operating the machines themselves and we're like, you know, trying to make sure is the machine working and if You know, the machine that outputs a widget that's broken, the human doesn't sit there and manually fix the widget. No, they go fix the machine that generated the poor widget.

That's a great, that's a great metaphor. That's helpful. Let's continue on that path and like, let's, let's spend the last part of our interview talking about the future of work. And when you think out 2 years, 3 years, and the future of work, you've started to, started to lay the groundwork when talking about workflows being backed up by agents. All being started by agents. What does the future of work like, you know, blow our minds a little bit, Wade. What does the future of work look like 2 to 3 years from now? What do the most successful companies look like? Are they, are they structured the same way?

Well, you know, predicting the future is very, very challenging.

Yeah, it's okay. You're going to be wrong. You're going to be wrong, Wade. It's okay.

I think the, um, I think we are definitely going to see organizations evolve a lot. I think I would probably start with the concept of a team. What does a team look like in the future? And increasingly, I think the ideal team is going to trend towards looking like one person plus a team of agents. And the reason why is that a sufficiently curious and hardworking person who is empowered by these tools can do a much wider range of tasks, which is very different than when you think about how organizations are assembled today. Most organizations are assembled by function. There's a marketing team, there's a sales team, there's an accounting team, there's an engineering team, there's a whatever team, an operations team. And those teams exist to provide skills to complete projects. And you bring these cross-functional disciplines together and all those skills are represented now on a team. But increasingly, the LLM represents a lot of those skills themselves. And so now you start to think, well, what is the ideal team? It's like, well, it's just a, it's a domain expert who knows the problem they're trying to solve, knows the customer really well. It can now like assemble that team of agents to go tackle those problems. And so in a company, I suspect you'll start to have, you know, these like loose collection of humans that are corralling their agents to go asse— like accomplish those goals. So that to me feels like the direction we're heading.

That's super interesting, Wade, like that. So let's pull on that a little bit, 'cause I love that what you're saying. Like in other words, you end up with perhaps, you said domain experts, which may or may not be functional experts. So you end up being like, well, let's get, instead of, you know, 100 people at the company, it might end up being, I'm not trying to get into like, you know, unemployment implications of this or whatever, because that's a whole other subject. But like, you might end up with like, instead of 100 people split up into 10 teams of 10 that are split up by functions, like you said, you might end up having, you know, 50 people that are managing teams of agents and those 50 people tend to be more domain experts on the overall business and they're a little bit more generalists. And because they, they can allow the LLMs agent, the LLM powered agents can be functional experts in a way that the, and then you can abstract up at the human level to be more about outcomes and experiences and business results, for example, that are being managed by a human and letting the agents do a lot of the tasks. Individual bundles of tasks that used to make up the functional jobs. So the follow-up question, and I like what you're saying, I mean, I think it makes sense that that's where the things would gravitationally trend towards. Does that mean if you're advising the young Wade from when you were, you know, right out of college and before you got into the mortgage marketing sort of space, like you go back and, you know, someone today is 20 years old, they're about to graduate from college or they're 18 years old, they're just graduating from high school. What do you recommend for them today? In terms of their, where their mindset should be given the future you just painted?

Yeah, I, I think this is a, like, fantastic time to be entering, like, entering the, the workforce because you do have this, like, massive platform shift, which kind of levels the playing field a little bit. Someone who's been in the workplace for 20 years, their skills are maybe not as relevant for the future. And so all of us kind of are operating at the same, same thing. And so if you're a, you know, someone just outta college, you have a chance to, like, rocket your career forward by basically becoming a power user of these tools. If you really learn how to master using tools like Zapier or Claude or ChatGPT or how to orchestrate these agents, you are going to be able to have just like a massive leg up on many other people. I think the second thing that's really important is you gotta be more entrepreneurial, and that doesn't mean you necessarily have to like start a company, but within the companies you are in, you have to be a problem solver. You have to understand like, What is the customer problem? Why aren't we able to solve this? What is holding us back here? And then just start building tools to go solve those things. And you are quickly gonna find yourself in a massive point of leverage in these organizations because as exciting as like this stuff I'm talking about is, there is still a lot of reasons why most companies aren't experiencing this magic yet. Like we've had little tastes of it here and there, but by and large, you know, we aren't teams of one, like delegating all our work to agents yet. Like that's, That's not how most enterprises are working yet today.

Wade Foster brings up an interesting question. Has your hiring at Zapier changed in the last 12 months?

Yes. So we added a—

tell it, tell it, tell us about that.

So we added an AI fluency rubric for every job coming into the company. And now it's, it is somewhat customized to functions because we still do have functions inside the organizations. And so, you know, how an engineer uses AI is a little different than how a marketer uses AI. That said, the scorecard. Has a, you know, an unacceptable bar, an acceptable bar, and, um, like a transformational bar associated with it. And so we're scoring these candidates against these things. And depending on the, like, no one, we're not accepting anyone who's unacceptable. And depending on the role, we're looking someone who's maybe in that more, you know, transformative bucket or versus saying like, hey, nope, you clear it, you're good to go. We want you here. But generally, you know, folks that are like really curious, they're using the tools, they're solving problems with them, that they're showing that they're like trying to live in the future. That's kind of a baseline expectation for us for any role that we're bringing into the company today.

Are you personally using an LLM regularly?

Daily. Yes. Daily.

Hourly. Which one? Which one?

So I, I use all of them is the answer, but—

Which one? Do you have a predominant one that you use?

I, you know, uh, Opus 4.6 is my like daily driver right now for a lot of things, but I find myself switching over to Codex, um, 5.3 for like really hairy tasks, um, at times. And then, you know, I'll switch over to like Nano Banana for ImageGen and things like that.

So, has it changed the way your, has it changed the way in which your management like occurs or are you like, what are you using it for strategically?

Sure. Well, you know, I'll give you a use case that I've been having fun with lately, which is that I have this skill set up that I call my, my cabinet, like a presidential cabinet. And so basically what this skill does is for particular decisions that we're facing, say like, hey, we got to hire this candidate. Like, should we yes/no hire this candidate? Or, you know, we got to decide whether to launch this product or not, or we got to decide whether to change our go-to-market motion or not. Just like, you know, think about the kinds of decisions you might see on your desk in a given day. What the skill does is it spins up a cabinet, and now in the cabinet there's like some, some fixed positions. So there's like the ruthless CFO, the like wartime operator, things like that. But it also spins up ad hoc dynamic roles as well too. So if it's a hiring role, it will spin up a domain expert for the role. It will spin up like an expert recruiter. It spins up all these folks. And then basically those people go assess the thinking, and then all of the roles come back with their recommendation, like yes, no, or like yes with caveats or no with caveats. And then there's a master cabinet member who will summarize all that stuff and say, here's what I want to go get after. So usually what I will do when I'm faced with one of these decisions is I'll go spin up the cabinet and say, hey, go review this. And while it's thinking, I go look at it myself. And so then I'm reviewing the material just to see, well, what does Wade actually think about this stuff? And I usually can find like a couple things where I'm like, hmm, I think this isn't so good, or I think this is really good, we ought to go do this. And then I like to just compare and contrast with the cabinet. And I find it is such a helpful way to stress test my thinking, to be like, hey, am I really holding myself honest? To what I am seeing here. I'm finding it super valuable because even in the best partnerships I've had, I find that humans oftentimes wanna, like, often will try and preserve the relationship. And so that means we can pull our punches when we're trying to give critical feedback. And I find that these AIs, they don't really care about that. And to me, that is so valuable to get just somebody who's like, I call it like I see it. You know, I'm not so worried about, you know, like your feelings in this situation. I just wanna know it. And for somebody You know, like in a position like mine, that, that is like, that, that is the most important thing.

Invaluable.

Yeah, exactly. It's like, that's what I care the most about. I don't, I don't need you to like butter me up with the decision. I just wanna get this decision right. So let's think about it, you know, as critically as we can.

Adam, any final questions for Wade before we conclude?

No, no, no final questions.

Wade, I have a, I have one more, which is given the topic. Of the future of work, looking out— I loved your description of individuals with teams. The definition of team is different. Given that that's an interest of ours and of this podcast, who would you recommend that we invite on the podcast in the future? Do you have anybody?

Well, I love Howie from Airtable has been building a bunch of really interesting stuff lately. I think he would be fascinating to have on the podcast.

Awesome.

Nice.

And I, my other question is, is, is there something that you've read in the last 3 to 6 months that, that you'd be like, oh, Adam and Andy, you guys should totally check out?

So not something I read, but another podcast I've listened to is the Dwarkash podcast. And Dwarkash is just on a sensational run. He has got all the like cutting edge leading researchers and leaders of these labs companies on almost every other week. And Yeah, I just feel like I'm, I'm just getting a masterclass on what's going on by just listening to his podcast.

That's awesome.

Yeah.

All right.

Well, with that, Adam, I'm, I'm curious, uh, we've just, you know, listened to Wade Foster. What jumped out? You know, what do you want to highlight for our audience?

Well, I mean, I'm looking at my notes here. I thought Wade, that was super helpful the way that you described, you know, that it's not workflows or agents that you were really crisp in your description of Like we all have workflows and those need to be, by the way, kept working. In other words, they break, like you mentioned, they need to be optimized. And you got agents that can help you. They're different than a workflow. They can help you build a workflow. They can even help you use Zapier to help you build a workflow, but, and they can even back up a workflow. So that, that was really interesting to just start the conversation for you to really help us from a taxonomy almost in a mind frame perspective to say, look, everybody in the enterprise world is thinking about workflows. There's this other thing called agents. They're not either or, and here's how they can work together. I thought that was great. Then the conversation turned to you yourself really helping us think about, you know, the future of agents and the future of work. And then talking about, um, how we as humans are going to have to abstract up to being more agent managers and outcome managers, both. And I thought that was really interesting. I agree with that. And so that really gets into the, the idea of us as humans having more problem-solving, output management skillset and curiosity, and then being really smart about how to use AI in general, how to use agents specifically to sort of achieve that. The, the concept of teams. Being less maybe in the future around functional teams purely and being humans plus agents. That was instructive. I'll say one last sort of comment though, um, that you made me think of, which is fascinating to me, is that we're living in a weird, I don't know if it's like the quiet before the storm or the eye of the storm or whatever analogy you want to use, but it's like, if you're a young person trying to get into an entry-level position, I think a lot of people who are not in a job right now are feeling stressed and they're feeling it's a really tough time, kind of a no hire, no fire time. And yet you pointed out this might be the best time in some ways that we are coming into for entry-level young people to kind of basically embrace this sort of new paradigm. And ironically, you've got like people that have been in the workforce for a while that have just been in one domain. They might be in a worse position than the younger people, but not feeling that right now. So there's this interesting juxtaposition. And I, and I don't think it's, they're not like in conflict because I think we're not yet, as you mentioned, we're not yet at a place where agents are fully reliable and capable and out there doing everything that they're about to do in the next couple of years. And yet you can see it coming. So it's a weird time to be alive, you know, as a human and sort of watching this. And I just thought you did a great job of frankly educating me and us and our listeners on sort of how to think about this stuff. Andy, what about you?

Yeah, I mean, there was a lot to jump on, but for me, the evolution of the organization with the focus of basically individuals with teams of agents collaborating with other individuals with teams of agents and how those come together with broader capability set, I think that is the future of organizations and that that image was really, I think you articulated, I don't think I could have articulated it quite that way. I mean, I came in certainly thinking about managing agents, but you put a spotlight on that for me. And that for me, that's something that I'll take away and that I want to highlight for our audience. So Wade, with that, on behalf of our audience and Adam, thank you for joining AI First with Adam and Andy. Thanks to all those who are listening to the podcast. For more resources on how to become AI-first, you can visit our website, www.form.com, download case studies, research briefings, executive summaries, and join our email list. And of course, we invite you to connect with our AI-first community, a curated hub and network for leaders turning AI hype into action. We truly believe you can't overinvest in your AI learning. Onward.