Taking on ServiceNow: Coding Agents for the Rest of Us with Serval Co-Founder and CEO Jake Stauch

[00:00:00] This is the ultimate moonshot in enterprise software. These agents that go off and do work for you and magically discover all your problems and solve all your problems. And I think no one's really figured out how to do this well. This is kind of the OpenAI hugging face incidents or other things where agents are given a task and then they do something that's unexpected. The agent becomes pretty competent. They could theoretically just start writing code that could take action on your systems and that is an unbounded security risk that I don't think anyone's going to be able to take anytime soon. On the one hand, you want these to be very autonomous without somebody baby sitting them. On the other hand, autonomy is very scary in the enterprise. Serval is the boundary. Serval is the guardrails. We are the way that a large enterprise with a lot of rules and approvals and process and politics can adopt these tools safely and comfortably at scale. >> Hi listeners. Welcome back to No Priors. Today Alod and I are here with Jake Stauch, co-founder and CEO of Serval. Serval with all the speed of the African wildcat is trying to attack ServiceNow [00:01:00] and more broadly bringing the power of code generation to IT and enterprise teams overall. Welcome, Jake. >> Jake, thanks for joining us today on No Priors. Good to have you. >> Great to be here. >> Now, maybe you could tell us a little bit more about your history and how you started Serval and what what is the problem that you started with or why did you start working on this to begin with? >> Yeah, so background, Serval is enterprise automation, enterprise service management. The idea is you can ask questions and get help at work and automate a lot of the parts of the job you don't enjoy and we do that through code gen agents. So, that's uh what the company does. We compete with companies like ServiceNow as historically the big legacy provider in the space. I discovered this category actually working at a previous company called Verkada uh where we sold enterprise physical security products to IT teams. Spent many many years talking to IT teams figuring out what to build for them. And one of the things I loved to to do with these teams is ask them, "What do you spend the most money on? And what do you hate the most?" And ServiceNow would top a lot of those lists. And so, I was always interested in this category of maybe there's a better solution here, but it wasn't [00:02:01] clear for most of my time there how you'd ever beat ServiceNow. They're a great company. They've done so much so well, and why would you be able to compete with them? >> What was frustrating for users about it? >> I think so hard to build in the platform. So, it's a very robust customizable, powerful platform that can map to all these enterprise workflows. Fundamentally, it's a workflow automation platform. And so, it can do so much, but every idea you have for doing something cool, you have to go and implement and a lot of times you're bringing consultants to implement it. So, it's it's always kind of running 6 to 12 months behind where you want it to be. And it's fine if you're building something that's going to last 5 10 years like a big onboarding flow and nothing about your business is going to change, so you can invest all that up-front effort, and then it gets deployed and it works for a while. But, for just trying to keep up with all the automation that you want to build, ServiceNow is not able to catch up. And And then it's very expensive and it keeps getting more expensive over time. So, that frustration of it's not keeping up with where I want to go as a business and it keeps getting more expensive, that's what was driving a lot of the frustration. >> I think one thing that you mentioned [00:03:01] that's really interesting about what you do is if you look at the world of AI adoption, a lot of the really early adoption is coding agents. And that's engineers using it to build applications for their business, for products, other things. And what you folks have been doing is really pioneering expansion of the category in terms of saying we should let anybody internally an IT person, etc., build internal products or tools that can be used broadly across the enterprise. You're basically providing like coding agents for for >> the And all the internal stuff. >> Yeah. >> Uh for people who often aren't that adept at coding. So, do you talk more about that? I think it's like super fascinating. >> Yeah, we love this idea of coding agents for the rest of us because if you think so much of the early enterprise adoption in AI has been engineering teams. And if you think about why that is, there's a lot of reasons, obviously early adopters in a lot of different technologies, but also they're able to build and experiment with these tools because they have this process of code review. It's very safe for even a 19-year-old intern to build something very powerful and then go through this process before it goes into production. We want to bring that same capability to the rest of the enterprise. So, we build a platform that brings coding agents to IT departments [00:04:02] who then can bring it to HR and finance and people teams and legal teams. And they can do it because under the hood it's coding agents, but then you have all these abstractions and guardrails and controls, so things go through approval, they go through a staging and approval cycle and audit trail and all of that. And so it's how do you build a system that makes it easy for everyone to be a builder? And the core insight here was that IT actually wants to build stuff. And for most of the world, they don't have an engineering department, they have an IT department. Most companies have an IT department, not not a a team of software engineers. And so IT is what's building and configuring technology for the company, and we're building essentially the AI platform for them. >> How does How does that run into things like all the different more like workforce agents that are being developed? Like Dot from Open AI or, you know, some of these things where you almost have a virtual assistant that will go and do things for you. >> Yeah, I think those really work well for consumers and SMB and even a lot of the mid-market organizations. In a large enterprise, there's so much, and I didn't even really appreciate this going in, there is so much process around [00:05:04] approvals, around what you can and can't do, the permission sets of any individual are so limited that even if you were allowed to bring your agent to work, which a lot of cases you're not, what it can what it can do is so incredibly limited. It can It can barely even read your emails. Um and so when you think about all the stuff you need to do at work and all the systems you want to touch and all the interesting automations you could build, your personal permissions end up being so limited that these personal agents are also incredibly and all not all that useful. So what we can do because we work with these IT teams that can connect with, you know, service account credentials and admin level permissions, we can create a structure where people can access elevated permissions and do all these crazy things in the work, but with very, very strict deterministic approval cycles, workflows that are deterministic, um and an audit trail because under the hood we are also a ticketing system. So you say, "Okay, I want my agent to go do X, Y, and Z." Well, most organizations are going to be very uncomfortable with that at the large enterprise scale, but with Serval, [00:06:05] that's going to create access requests that go through an approval process, and all of that's going to be documented logged, and then you can get just-in-time access to be able to run that deterministic workflow one time, and all that's going to be tracked. And yes, it's a little bit more annoying than the magical personal agent experience we love with Instinct and Muse and all these tools, but it's what has to be true for a large enterprise, especially regulated enterprise, to ever adopt these kinds of technologies. >> And this also, I guess, feeds into the traditional diffusion of this technology, which has been consulting firms, the Accentures and Deloittes and the like of the world kind of owning a piece of this, doing the implementation for people. You're kind of taking that and putting it in the hands directly of the IT department. Are you also exploring this more as we're sort of forward deployed motion, or how do you actually get this really built out in the enterprise or used effectively? >> Yeah, absolutely. Diffusion is obviously the gap here. The technology can do so much, and and enterprises have barely scratched the surface of what's possible. And a lot of that is just understanding the people, the process, what workflows actually exist, how do you want them to exist in the future, and we think that you still need people [00:07:06] for that. So, we deploy both the a deployment strategist, we call them, and a deployed software engineer that go on site with customers, figure out how do we implement this, how do we solve all your problems as a business, and then walk you through the implementation. What we found though is that a lot of times after we've got this implemented and deployed, customers that have a long tail of more stuff they want. And and as we've discovered with software engineering, there's actually unlimited demand for work. And we've started to build out a team, we call it Pro Serval, uh or or automation engineering is is the field, and we bring in these automation engineers to kind of do ongoing continued support and services because there's so much that they want to automate, and they need the help to be able to figure out what can be automated, what do the processes look like, how do we navigate all of our systems, and then put it into production with a product like Serval. >> How old is the company today? >> Two and a half years. >> And um, you know, if you just look at ServiceNow as a business, I feel like Serval's one of the only companies that is like, we are direct attack replacing [00:08:08] enterprise systems of record including for large enterprises. Um at the beginning did you think you were going to do that? Like do you How do you eat the elephant? You know, just in terms of, you know, how big that product suite is. >> I like to say everything has gone exactly according to our very delusional plans. So we set out from the beginning to be the largest enterprise software company of all time. We actually made a list of all the companies. We circled Service Now and we said we're going to beat them and we're going to build something much bigger. We said that on day one when it was two people in my co-founder Alex's living room and we just said we're going to do it. And so even though that was crazy, that was always the intention. We didn't grow into that ambition. We set out from the beginning and I think that that actually >> of people would have started with like, you know, automation on top of their ticketing system for example. >> Yes, exactly. So the the way you typically go after these these large enterprise startups at least they they build like the layer or they go down market. And those are kind of the two common approaches. Let's build a small layer, play nice with the big incumbents, AI agent on top of their existing work. >> Which like you know, it's it really caps maybe the only one that worked is Vevo or something, right? Cuz they they [00:09:09] started off on Salesforce and then migrated to become their own platform. So they're they're the only one that I can think of that backwards integrated. >> It's really tough. It like caps you unless you find some niche to kind of like expand into later on. Um the other approach is we just go down market, you know, go down market, service the SMB and mid-market. And you see that with a lot of, you know, competitors to Workday, competitors to Salesforce. They just say we're going to be that but for the lower end. >> And we just looked at This is Rippling or HubSpot. >> Exactly. >> 10 years. >> Exactly. And we looked at that and we said why not just go after the big one? It's it's much harder but at least the prize is quite big if you win. And I think that was so important for us because you end up making so many different decisions if you're building for the large enterprise from day one. All the product decisions, how you build your team, how you think about growth, they're fundamentally different than if you go and you serve one market and then you hope to expand later on. >> How'd you choose this versus Salesforce server? You know, there's a few other sort of canonical giant companies in different categories. >> I think we didn't really understand how we'd beat the other companies and why we had an advantage and what what AI unlocked that they couldn't catch up to. And what we understood about Service Now [00:10:09] because of our experience with IT is that fundamentally it is a workflow automation platform. And our belief, me and my co-founder, and my co-founder really had this this idea early on that code was the right way to express automation in the enterprise. That you should just build all automation in code and these drag-and-drop workflow builders is a painful level of abstraction because it's too complex for non-technical users and it's annoyingly abstracted for technical users. Code is a better way of building automation. The problem obviously is not many people can write code and and do it quickly enough for it to make sense for automation. And so as AI made this possible, that was what it clicked for both of us that actually if code is the right way to build automation and now anyone can write code, we can build a better automation platform. And what is an automation platform is actually what is the best market for an automation platform? It's actually taking on these massive legacy software companies like ServiceNow. >> I guess two and a half years ago when you got started though most of the coding stuff was sort of tab complete. It did not work. >> Yeah, and so [00:11:09] yeah, but in practice when we started building this >> What was the assumption? Was it that the the coding would get much better? Was it that you'd build tooling and then you didn't need to because things got so good? I'm sort of curious of that transition. >> The bet was that coding agents would be would get very good. Um and the theory there was it's a verifiable domain and and it should actually hill climb fairly well. And so we believed fundamentally that coding agents would get good at least for these very I didn't expect them to get as good as they are for just general purpose software development, but for deterministic procedural scripts of reset password, get approval, like all of these things are just so defined and verifiable that we felt like it would get good at those things. And so from a workflow automation perspective, we thought it would get there. In the early days it did not. So what we ended up doing in all our early demos, we would prompt the LLM and say, "Hey, here's a suggested way you might want to build a password reset workflow." And so we'd we we'd seed it with like a lot of examples [00:12:09] and then over time we'd make that library bigger and bigger, but then the models just got so good that eventually it became hard to break. It became hard for you to give it a workflow that it couldn't automate if the APIs existed. >> Yeah. >> When you think about where coding agents are today and what your customers want, like what are you Are you and Alex excited about them getting better from here? Are you nervous? Like what do you think it looks like in terms of impact on your own product? >> We're so excited for them to get better because I think it unlocks a lot of the things that we'd love to do around, you know, better browser use and computer use and more of a approach to background agents and personal agents that can be really useful in the enterprise. So, we're excited for the evolution of the category. We've always believed fundamentally that we are not building the LLMs ourselves. We are building the guardrails. We are building the the boundaries. And the boundaries are the product for us because we are the way that a large enterprise with a lot of rules and approvals and [00:13:09] process and politics can adopt these tools safely and comfortably at scale. And so, Serval is the boundary. Serval is the guardrails. The intelligence getting better just makes our product more powerful and also makes our customers want to actually clamp down even further on those boundaries, on those guardrails. And I actually think access is going to get more and more controlled for individuals in the enterprise. And as access becomes stricter and you let people do less and less and your permissions get more and more narrow, which I think is going to happen, >> Why is it going to happen? >> Because I think if if you think about, you know, I might safely give you more permissions than you need because I don't actually think you're going to do all that much damage. But if you have access to all these agents, then the damage you can do with any kind of like slightly elevated beyond normal permissions expands. And then we're seeing this in cybersecurity risk right now. >> Absolutely. >> And and so, what I'm actually going to want to do if I'm a large enterprise is shrink everyone's permissions down to truly least privilege. Like you cannot access any document and you cannot take any action unless you absolutely need to [00:14:09] do it for your job. But then you're going to have exceptions all the time. We are the layer that grants those exceptions and makes it possible for you to set up approval procedures around that, have an audit trail and a ticket associated with that. So, by owning how privileges get escalated in the enterprise, we actually become more powerful and more essential the more you clamp down on end user permissions. >> Is there um like a story or a win you can share as to, you know, people would be taking a big risk going with you to replace a very embedded incumbent, right? It's a investment of their own time, I assume. It's like personal risk. Like, why do that? Or is there a is there an experience where people have already gone through to the other side? >> I think we have to convince ourselves and our customers that Serval is the way you get promoted at work. It it can't be the fact that Serval is a risk that you take to hopefully get something on the other side. We have to convince customers that you do this and you're going to make your career. You're not just going to not get in trouble, but you're actually going to get a [00:15:09] promotion. >> And this is top-down, right? >> Top-down. So, we sell into the C-suite. We sell into the IT leadership in in many cases and then distribute into other departments. And that is the message is this is people built their career on ServiceNow. You're going to build your career on this next generation. And we're seeing this play out. So, one of our largest customers is Fox. Um Fox Corporation adopted Serval company-wide and the person who led that migration at the on the on the ground level actually got nominated to be the MVP of the entire company. >> Wow. >> And and got an award for his work there. And that is really what we're about is we're convincing our customers saying like this is the way to not just prove that your your useful to your organization, but to prove that you might be the most essential person in your company because you're leading this change. And we're so pivotable pivotal to the AI transformation story that I think it's a it's something we can sell really nicely to our customers. >> When you started Serval, I think you were very um adept at thinking ahead, right? So, you knew coding agents or coding would get better. You knew there's way of transformation. You you kind of identified the good market [00:16:09] segment to go after. What do you predict will happen in the next 2-3 years not only for your company, but for AI more broadly? Like, is Is else you're seeing on the horizon that you're very excited about or a big shift that you see coming? >> I think consumer often gives us a win-window into what's going to happen next in enterprise. Um and it all it doesn't always look the same, but I think it's it's a nice window. So I'm very bullish on what do personal agents look like in the enterprise? I think in the same way that email and Slack and these tools transform how we communicate at work, I think personal agents will also be a big part of that. I think everyone will have their personal agent that does tasks for them, that delegates to other personal agents. And so your agent will talk to other agents at work. So I'm really excited about that, but then how do we secure that in a world of increasing cybersecurity risk and increasing concerns at large enterprises around what's happening with everyone's agents taking action in all their computers and and using their browsers. And so that tension between what employees, consumers want out of their personal agents at work and what enterprises need from a governance [00:17:10] perspective, I think is really interesting. And I think there's going to be a lot of opportunity in that in that tension of >> think there'll be a separate personal and work agent? >> I think so. I think so. >> Yeah. >> I think so. In the same way that I have a Gmail and I have a a corporate email. Um yeah. >> If there's a premise that I I I mean there's a bunch of different ideas here, but one is that um uh you know, exceptions or access control is going to get complex and require different level of scale, right? Another is that you're just going to have more automations overall because people, you know, if you make it easy, people are going to extend in all different places. Are there like core design principles beyond uh code generation should be the actual form of automation that you have put into the product today that reflect that? >> I think the biggest one is the software development life cycle. Something that we take for granted in the engineering context of the hey, there's actually like a dev environment and then there's staging and then there's prod and there's like these gates between them and maybe you've got, you know, branches [00:18:10] that people can develop on that they're not going to harm the other systems. >> And sorry, just to be clear, that's not how IT automation works today. >> Uh it does in some cases, but I think most new AI products are not embracing that. They're basically saying, "Hey, let's build these cool agents and ship to production." But, this idea of, "Hey, you have to actually follow an SDLC process if you're a large enterprise, a large financial institution, is not just going to ship new agent capabilities uh without a review and user acceptance testing and and all of these >> Compliance checks. >> Exactly. And so, I think that's something we think about a lot is what is going to convince the largest companies in the world to adopt this technology, not just what is powerful and cool and something we're excited to get in the hands of customers. >> it takes a lot of AI companies, particularly if they're vertically centric, a year or two to figure this out, sometimes longer. And it's interesting to watch the whole industry now adopting both the forward deployed motion, starting to think through uh software development life cycle, compliance, other aspects of the enterprise in a way that was almost missing. >> I I think in some cases they never get there. Like, some companies just they they look at what enterprise software [00:19:10] takes, and they decide, "We don't really want to build that." And it takes, you know, you look at the story of a lot of these companies that we mentioned earlier that have gone down market. I think sometimes they do look at the opportunity to go and build for the enterprise, and it it requires a whole different set of building principles and products that you have to build, and some just don't choose to do it. And they they see that ground other companies. And we've chosen to say like, "Hey, give me your your gnarliest problems, and we're going to solve them and make AI um transformations possible in this this crazy world, in this world of risk and and governance concerns." >> When you um have your, you know, deployment, you have sort of key customer environment engineers, and then ongoing automation specialists, and then you have the core platform. How do you decide what is actually brought into the core platform or, you know, in every individual customer's environment? >> Yeah. So, when we deploy the software, I think what's really important is we actually never build custom features. We don't build bespoke product capabilities. And luckily for our category, no one asked us to. [00:20:10] Everything that people ask for is something that every other customer is going to want at some point. Maybe they impact the prioritization, but it's still something we always wanted in the platform. Now, because it is a CodeGen platform under the hood, what you build in Serval is very, very unique. So, the password reset workflow for one company is going to be very different, shockingly, than the password reset for another. Um, same with onboarding and offboarding and all these different automations internally, they look very different, but that's just baked into the core primitives of the platform. Serval almost becomes the IDE, right, for the IT department and other departments to build their automations and manage them at scale. So, what we do do is if we look and we see the same customer building the same automations over and over and over again, and having to customize them and and build them in the product in the same way, we start to consider, "Hey, should that be a first-class feature in the product?" So, one example is we saw a lot of customers building workflows to detect knowledge base articles that were out uh conflicting or out of date. And so, we said, "Hey, we should just do that automatically." And we we had customers [00:21:11] that, you know, would would set up automations to reclaim old licenses. So, licenses for software you're paying for that no one is using, let's automatically cancel those subscriptions. Well, we just decided to build that in the platform. So, it is a way for us to kind of test the waters of what should be really first-class features versus what should stay as automations uh in Serval. >> Our enterprise customers, as it relates to just general spend on code generation for software teams, have come through a journey this year, right? They're like, "Uh, we're going to let people try it. Then we're going to token max and we're going to like restrict that and figure out who's actually doing this productively." Um, how do you think that reflects for your your business and how do you think about um the sort of actual intelligence cost? >> Yeah, I think we're still watching this. I do see our CIO customers trying to pull back from that initial token maxing era, but I also think we're so early in the adoption curve that it's not going to be a a clamp down in aggregate. It's actually going to continue to be a pretty dramatic rise because most companies are still figuring this out for the first time. And they're they're [00:22:12] all going to go through this cycle of rapid early adoption and then kind of a pull back of spend and trying to measure and calculate ROI. Um, but what I hear from our our customers all the time is not really a concern about the uh long-term budget impacts. They realize that there's going to be a short-term pullback, but they're still planning on increasing spend. What they need though is more proof of ROI. And that I think is what's going to transform in the industry is is people looking for how can I prove that this spend is giving me something? And that is hard. I don't know that anyone's really figured out how to measure what you're getting from the tokens that you're buying, but that I think is going to be a huge opportunity for us to show the work that's being done and try to equate it to work that would have been done in other platforms or with other cost models or by labor. And so that's what our customer asked for is help me prove that this was worth it. And and that's what the assignment is. That's what we have to do. We we try to price on workflows run because I think it's a better reflection of actual work being done versus just tokens, which is more [00:23:14] like verbosity and inefficiency, but but I think we're all going to figure out different models of proving that what we're doing makes sense. >> One of the things people have been talking about is the sort of SaaS-pocalypse, where the idea is, you know, people will just build their internal SaaS software and all these categories will go away. And a lot of people, to your point on calculating the ROI in tokens, are kind of trying to sort is it worth rebuilding these systems? And can you actually do that effectively? What is your view on the SaaS-pocalypse or this sort of shift in in SaaS versus AI? >> I think all the companies I've talked to that tried to buy code these tools internally, especially the main system record tools, not the kind of point solutions, but the main system record tools, have discovered that it's quite expensive to build and maintain enterprise software. Even if you can do it, it doesn't make economic sense in many cases to do it unless your needs are very bespoke and unique in some way. And so buying off-the-shelf for things that are not your core competency, I think has always been the build versus buy argument. I think that that is is true now as it is as it as it's ever been. Um [00:24:14] because it still is hard to build enterprise software. A lot A lot of companies are discovering this or relearning this that actually enterprise software is complex and you need people to maintain >> What are use cases and edge cases? >> Edge cases, uh thing things that um break down and people need to update it, so then you have to staff the team and then you're paying for that labor and and then no one really wants to work on the internal tools team in many cases. So, you have all these problems with doing that. Where I do see it is in kind of the point solutions where a lot of companies have hundreds and hundreds of SaaS applications. They probably need a couple dozen tool. And so, I do think you're going to have a shrinking of the the sprawl of different point solutions and they're going to be eaten by the core systems of record and the core platforms. >> middleware, too, I think is just going to go away. >> Exactly. Exactly. So, I think there is going to be consolidation. Um and so, this there will be a SaaS apocalypse, but in that kind of point solution um smaller market cap type companies. I don't think that you're going to truly get away from the core systems of record, the ERP, the HRIS, the ITSM, the CRM. I think those are going to stick around. They might transform and what we're seeing with Serval is is we are displacing the those tools. Um so, I [00:25:15] think there will be a new generation of incumbents. Um but I don't see those categories just disappearing. >> Uh I'm curious as to what mental model uh what or what theory of mind do you have for the IT user um given, you know, they're not software engineers, but they understand their business, they understand what they're trying to go automate. Is it, you know, when you talk about vibe coding, I'm like, "Okay, but do you understand like security architecture? Like, do you understand how this is going to integrate with governance?" Like, how do you square, you know, code generation and how we think about vibe coding and what the challenges are with that with, you know, we want to build robust IT systems? >> Yeah, absolutely. I think IT is really interesting because they're they're closer to a software engineer that I think a lot of people give them credit for. Uh especially in in large enterprises, the IT department is the engineering team in a lot of cases. They don't have the the software engineering team, so IT runs all these systems and and IT often does write code and manage the systems. And so, I think we treat them as adults. We give them the tools [00:26:16] that we would give software engineers. Now, we abstract away some of that complexity. Uh we make it easier to use in some regards and and we don't just kind of hand them cloud code and tell them good luck. We actually we build a platform that's dedicated to their use case. And we don't expect them to be able to necessarily write code or or understand what's being written, um but we still give them access to it under hood. So, you can always see the code, and you can always treat this through a full software development life cycle, but you also don't have to. And so, a lot of the the product principles is thinking with a few different personas in mind, you know, an IT engineer who actually can and does write code all the way through someone maybe in the marketing department and the the people team that doesn't want to see code ever, but still wants to have that flexibility of code gen under the hood because they've got a complex workflow that they want to build that they don't want to drag and drop their way to. So, I think a lot of it just comes to having multiple personas in your head, and also treating IT like an adult. I think a lot of the products that have been out there have actually kind of been pedantic to IT of like, "Oh, you [00:27:16] don't actually understand this stuff, so you need this very stripped-down, more opinionated, simpler version." Actually, IT appreciates the complexity and appreciates the power of these platforms. We want to deliver that to them. >> You guys built an agent that helps you build automations. Can you explain how that works? >> Yeah, so one of the things I think every company discovered this as they were building kind of the first generation of these these AI tools is that you could build such amazing complicated systems, but then you had to train people to understand all of your product primitives and how these systems fit together. And so, the first generation of this is we deployed all these FTEs to explain to customers, "Okay, here's here's how to build on the platform. Actually, let me just build it for you." And then we realized it would be really nice if customers could just talk to an agent that understood their requirements and then translated that into the product primitives. And so, we built this agent called Catalyst that does exactly that. It's an agent for managing the platform. So, it's an agent for admins to build out all those underlying automations, but also configure the rules around approvals, um permission checks, um build out different policies, [00:28:16] update all these different systems, understand the the entire deployment. And so, because Servaless all code gen under the hood, having the agent on top of that to help you manage the system just made a ton of sense and is so powerful. Because now I don't need you to train yourself on how to use Servaless. You can just talk to an agent that actually understands how to use Servo and how to build on it and and that is your primary interface into the product. >> I I think one thing that's under appreciated and I think is cool is that uh the experiences for agents in different products and actually in you know code generation agents as well being able to bring a user along as they are building a product or an implementation or something is actually like raising the technical competence and ability of a huge number of users and and turning people into more you know engineers actually. >> Absolutely. You have this blank slate problem in the old generation of automations of where where do I even begin? I know I want to automate all these parts of my job but where do I begin? Products like ours say, where do you want to begin? And just can start making suggestions and you can iterate [00:29:17] with them and you can say I actually don't understand that. What are you trying to say? Can you explain this to me? Okay, I'm trying to do this. How would I even go about doing this? And they can give you that information, they can fetch the information, they can explain concepts to you. And so it does become a build partner and you can achieve so much more than just prompting an agent to go and build something and it can actually walk you through the process and help you explore new opportunities that you wouldn't even have even thought of. But so one thing that seems a little bit under discussed right now is background agents. Uh could you tell us more how you think about that and what you think is coming in terms of this next wave? >> Yeah, I always describe background agents to the companies like this is the ultimate moonshot in enterprise software. Just these agents that go off and do work for you and magically discover all your problems and solve all your problems. And it would be amazing if we could get that deployed and it could work and it could follow our permissions. And I think no one's really figured out how to do this well because on the one hand you want these to be very autonomous to go off and figure stuff out and then solve problems without somebody babysitting them. On the other hand, autonomy is very scary in the enterprise. And so you do want to see what exactly can they read. This is kind [00:30:18] of >> OpenAI hugging face incidents or other things where agents were given a task and then they do something that's unexpected in order to accomplish that task and so what are the guardrails you can put in place? >> Exactly. Because my best employees, what do you do with them? You say, here's the general high-level mission, go off, don't talk to me and go do it and you'll come back and you'll have done amazing things. I want to be able to trust agents to do the same thing for me where I can give them this high-level task and they can come back and solve this problem and come back to me. I don't think we've figured out how to do that in a way that is feels safe >> Mhm. >> in the enterprise. >> What do you think is a way to make it safe? Is that I mean people have experimented with different things over time, everything from constitutional AI to certain forms of RL. I'm just sort of curious if you have a solution in mind or or is it just >> We think of it in in the large enterprise, it's not perfect, but it's a it's kind of an incremental escalation of capabilities. So you go off and you do these things and it comes back and says, "Hey, I would like to be able to do all this stuff. This would help me do my job." And you approve them. And all this stuff are deterministic workflows. I want to call this tool. I want to run this script that does X, Y, and Z. Can I do that? And you just kind of approve [00:31:18] these with certain permissions and guardrails around them and you say, "Okay, yeah, that's okay. That's okay. That's okay." And over time the agent becomes pretty competent because it has a a big enough toolkit that it can solve most problems. Um and it's not perfect because it's annoying to have to babysit that process, but I think it's what you have to do because otherwise it could theoretically just start writing code that could take action on your systems and that is an unbounded security risk that I don't think anyone's going to be able to take anytime soon. >> Do Do you think to your point of being excited, or you and Alex being excited about future capability, um like we're we're still missing some model capability here? The reason I ask is uh you know, the ability to be proactive and then predict what an individual, Jake as CEO, me as an investor, um or me as a human being, you know, and and get it right is still very hard, right? You need a lot of preference data. Think about employee working for you like, "Okay, what are Jake's actual priorities? When he's gives me this high-level mission, what does he mean?" [00:32:18] Do you think that's a model problem or is it entirely like a a guardrail and harness problem today? >> I think it's um it's both, honestly. I think memory obviously has to be better and better because you want to understand the preferences. You want the context to be insanely large because I don't want to have a bunch of different chats with you. I want to have kind of one single chat that goes back all the way to our very first conversation and you remember the key points of that that are relevant. Um, and then I think the permissions problem will always remain the biggest thing because even if I feel like you are a very, very valuable, smart agent and I you can do all these things for me, how much do I trust you to just run wild and not have to babysit your actions? >> to do that with people, too, right? I mean, anybody who works for you has to come back and check in and is what you wanted and here's three options. And so, I think it's a very normal just process, right? Yeah. >> Yeah. >> The company is about two and a half years old. Uh, you have gone very quickly to at least an enterprise mature product. How big is the team today? >> We are 200 people today. [00:33:18] >> 200 people. >> in January, so it's been an incredible ramp year. >> That's quite a year. One, I guess, do you expect to continue hiring so fast and as somebody who clearly believes in AI, like why? And then two, just, you know, you've been part uh, you and your team have been part of startups before, like what are you operating, how are you operating very differently, you know, the last two years than the previous 10? >> Yeah, the the hiring velocity I think will will increase even from where we're at today. Um, we believe that there is unlimited demand for work and there's unlimited competition and there's unlimited ambition. And so, even though every single employee does so much more than what they could do a year ago, we have even bigger ambitions for what they could do. And so, I think because every employee is so much more productive, we actually want more of them, not less of them. >> Given your, you know, time at Verkada before, uh, how are you operating differently over the last two years than the previous 10? >> I think what's so unique about Verkada and the category we're in and the moment we're in is the TAM is unbounded. So, it [00:34:18] feels like the the the dressable market is really more about the size of our ambition than it is about fundamental limitations on how big this business can get. Because we are automating essentially all kinds of work for every single employee in the world, the TAM is as big as we want it to be. Uh, and it's really how fast can we grow and what is our ambition and making decisions based on that versus, okay, here's here's what the market looks like. Here's what we think we can actually do. And that's a different set of challenges. It's not it's not strictly better because now there's so much open space and there's so many more decisions we have to make about what we actually want to focus on. Um but it is really fun to not be constrained and and hemmed in by what the market allows for. >> How given that scope is very broad, how do you organize like the cadence or the you know, um the EPD team? >> We are embedded with customers. So even the engineers that are not necessarily deployed, they are in the customer Slack channels. We are talking about customers non-stop. They're hearing from customers every single day. Most engineers are on [00:35:19] at at least one customer call a week. I think most of them are actually on at least a customer call a day. And so what this is, it's it's like immersion in a country to learn a foreign language. You become so immersed in your buyer and your customers' mindset that you understand what they want, you understand how they're thinking about things, and you are adapting to their preference changes in real time. So as they're moving with the market and they're starting to talk about different products that they're trying out and things that they're excited about, our engineers are constantly aware of all of this and able to adapt very quickly. So we've decentralized product development really to the engineering team. We don't have a product function because they're the ones talking to customers every day and they're able to rapidly adapt to whatever customers are saying today and this week versus what they're saying a month ago. >> That sounds like a a really like natural way to keep pace with um a like a ripping market and changing priorities for your customers. How do you and Alex and leadership then make sense of it? Like you know, in a [00:36:19] traditional enterprise software company, you'd have like architectural review >> Yeah, you have a quarterly road map and you prioritize the road map and you you know, train all your customers on the new launches. >> And you'd communicate with your customers that like things are going to change too fast because they can only absorb it so fast, too. >> Yeah, it's a big challenge. Our product fat ships and changes faster a lot of times than anyone can keep up, including myself. Um we just try to make sure that one, the engineers that we hire are really product oriented. That's one change we made is they have to really understand our customers and we want to put them in front of our customers because they're going to be have to make so many decisions at the edge and not every single product decision can be bubbled up to leadership every single time. That is a fundamental change is you have to be willing to push a lot of the product decision-making down below and you have to be willing to move fast and also undo and revert if you go in the wrong direction and and kill your darlings. You have to be willing to get, you know, change course on things that you thought were fundamental to the product and switch gears when your engineers bring you something that, "Hey, this is how the customer sees [00:37:19] things now and this is what we should do differently." And one of the things I'm proudest of at Serval is how many times we've decided to erase things we've done in the past that were very committed to and just say, "You know what? The market's changed, the customer's changed. We're going to do things entirely different." And and I think that is what's enabled us to keep up is that ability and willingness to reinvent ourselves over and over again. >> Some of the most durable things in this era. Thanks, Jake. >> Thanks for having us. >> Find us on [music] Twitter at No Priors Pods. Subscribe to our YouTube channel if you want to see our faces. Follow the show on Apple Podcasts, Spotify, or wherever you listen. [music] That way you get a new episode every week. And sign up for emails or find transcripts for every episode at no-priors.com.