# The most underrated dataset in enterprise AI is your org chart | Rippling's Albert Strasheim | Dev Interrupted Powered by LinearB

> AI agents can't transform an org they can't see. Albert Strasheim, CTO at Rippling, joins Andrew Zigler to explain why agentic transformation starts with the employee graph.

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The most underrated dataset in enterprise AI is your org chart | Rippling's Albert Strasheim

# The most underrated dataset in enterprise AI is your org chart | Rippling's Albert Strasheim

By Albert Strasheim

|

July 21, 2026

![Blog_Comprehensive_DORA_Guide_2400x1256_68_43be6688ce](https://assets.linearb.io/image/upload/c_limit,w_2560/f_auto/q_auto/v1/Blog_Comprehensive_DORA_Guide_2400x1256_68_43be6688ce?_a=BAVMn6ID0)

AI agents can't transform an org they can't see. Albert Strasheim, CTO at Rippling, joins Andrew Zigler to explain why agentic transformation starts with the employee graph, the system of record for who does what. He shares how Rippling assembles teams and primitives across silos, why evals are the new unit test, and how compensating controls keep AI output from turning into slop. When agents do the work, you still have to know who, or what, shipped it. LinearB attributes the work, whether it came from humans, AI assistants, or autonomous agents.

### Show Notes

* Rippling:Explore the workforce management platform at[rippling.com](https://www.rippling.com/)
* Introducing Rippling Data Cloud:[AI-powered BI that understands your workforce](https://www.rippling.com/blog/introducing-rippling-data-cloud)
* Follow Albert:[LinkedIn](https://www.linkedin.com/in/albertstrasheim)

### Transcript 

_(Disclaimer: may contain unintentionally confusing, inaccurate and/or amusing transcription errors)_

\[00:00:00\] **Andrew Zigler:** Joining me today is Albert Strasheim, the CTO of Rippling. And, you know, on this show, we've spent a lot of time talking about how fragmented the tech stack is becoming in an increasingly agentic world, how companies are struggling to understand the power of the data that they're already sitting on top of. And more increasingly than not, we keep dividing into newer and newer silos, reinventing the same processes over and over again, uh, and ultimately spinning our wheels, unaware of all of the efforts others are doing. But right now in the market, at the same time, uh, there's a lot of people building for workers that don't exist.

\[00:00:40\] **Andrew Zigler:** You know, we're building tools for AI agents and ephemeral automation, the non-human seat in the org chart, so to speak. But Rippling is leaning the other direction. They're doubling down on the human layer, the idea of unifying the technology that makes HR, IT, and finance possible into something that's closer to, like, an \[00:01:00\] employee graph.

\[00:01:00\] **Andrew Zigler:** And that bet is what powers all of their agentic work and the work of their consumers. And an agent that knows who reports to whom and who has access to what and can more, more accurately understand the shape of your organization comes in with so much more context and a better ability to deliver the work that you need every day.

\[00:01:20\] **Andrew Zigler:** So today, we're talking about building that human-centric foundation that'll become the f- the jet fuel for the agents of tomorrow and what it means to lead an engineering org through that kind of challenge. So Albert, it's great to have you here today.

\[00:01:35\] **Albert Strasheim:** Thank you for having me. Uh, that was honestly a great, uh, sales pitch for what we're trying to assemble here.

\[00:01:42\] **Andrew Zigler:** Amazing. Well, I'm glad I, I set it up well, and, uh, I'm kinda curious to learn more because, you know, it's a lot of big words about breaking down these silos and unifying these parts of, um, operating a company that traditionally had very different tool stacks. So, you know, you've been leading the engineering org at \[00:02:00\] Rippling through a time of massive scale.

\[00:02:01\] **Andrew Zigler:** Like, what are some of the things that you're seeing right now, the biggest insights you've gained about building, uh, in this environment, and what has ultimately led you to innovate on this new kinda AI platform?

\[00:02:13\] **Albert Strasheim:** I mean, I think first insight has, and, you know, some of this for us is also pre-AI, um, but I almost feel like AI is, is making it easier now, uh, is you really need a maximally ambitious vision, right? Um, you know, I, I give Parker a lot of credit, f- uh, our CEO, a lot of credit for this. You know, I, I think he was maybe the, the first, uh, vision maximalist, uh, you know, as he conceived of this compound startup idea, you know, trying to build, you know, so much business software, uh, you know, across the entire, uh, you know, company stack.

\[00:02:49\] **Albert Strasheim:** Um, but, but I think you really have to start from that. And then I think once the vision is there, you, you can ask yourself, you know, "How do you do this?" Uh, a- and then \[00:03:00\] a l- a lot of the, the, the pieces begin to fall in place. And I think the, you know, how do you do this, you know, is going to lead you more often than not to some kind of platform, uh, play or, you know, the answer is going to be start with, uh, you know, a fundamental set of platform building blocks.

\[00:03:18\] **Albert Strasheim:** And, uh, I, I think something the, the early Rippling team did and, you know, something we're obviously trying to carry forward now is, uh, significant investment in those platform building blocks. And I, I think that's really, uh, been important to our success up to this point. But I think it's also really important for any company trying to build, uh, in the AI era now, is you, you need a big vision, and then you need a big platform to do, you know, all of the things, you know, and probably not just in one product area, but in a, a number of adjacent product areas.

\[00:03:51\] **Albert Strasheim:** And it, it is then really incredible just the, the kinda like use cases that fall out of that, the solutions that fall out of that and the, you know, like the happy customers \[00:04:00\] that, that come from building, you know, in this wide-ranging way. And I, I think it challenges some of the conventional wisdom around, you know, focus, just do one thing.

\[00:04:09\] **Albert Strasheim:** I, I think we're, uh, seeing the end of that era, at least for a while, and the, the kinda like go broad, go fast, go big era is here right now.

\[00:04:18\] **Andrew Zigler:** Yeah. And, and, and I'm curious too about the bets that y'all are making in that world. At first blush, when people would look at Rippling and understand the data shape that it has and the world in which it lives, maybe there's a lack of th- for thought or innovation or modularity in interacting with that layer.

\[00:04:38\] **Andrew Zigler:** So traditionally, it's something that I'll, I think maybe a lot of developers, a lot of worlds have felt more locked out of. But what are, what are the opportunities you see for new innovations in that kind of data space, but also too keeping in mind the things that are so critical for it, like, um, making sure it's secure, um, and that the data is used sensitively?

\[00:04:59\] **Albert Strasheim:** \[00:05:00\] Yeah, absolutely. Yeah, I, I think really the big opportunity here is that you can just build significantly better business applications, you know, whether it's for humans or humans working with agents or for agents operating autonomously when you understand the organization you're, you know, servicing or, or, or supporting in much more detail.

\[00:05:22\] **Albert Strasheim:** Uh, and, and so I think the interesting thing about, you know, our approach, you know, way back when starting with HCM and payroll is that it gives you a handle, you know, and, and to some extent an up to-- a very up-to-date view on what is happening in an organization. You know, as you can imagine, everybody, uh, in a company is incentivized to keep the payroll system up to date.

\[00:05:44\] **Albert Strasheim:** You know, as e-every, every time somebody starts, you know, they need to get paid, so they go into the payroll system. Every time somebody leaves, uh, you know, you need to stop paying them, so you update the payroll system. And so it's this hook into the organization that is, you know, up to the minute accurate.\[00:06:00\]

\[00:06:00\] **Albert Strasheim:** And from there you can, you know, build many other solutions much better, right? You can do a better job of, you know, IT and access management because you know as people come and go, you, you can revoke their access. Uh, you know their role in the organization. You know who they report to. You know their department.

\[00:06:18\] **Albert Strasheim:** Uh, you know the s- the sets or subsets of data they should have access to depending on their level or a bunch of other criteria. And so to some extent, you know, HCM and payroll was just the stepping stone to get a good hook or like a good trigger into this like dynamic, uh, part of the organization.

\[00:06:38\] **Albert Strasheim:** And then you can build, I think, almost every other business application just a little bit better, like once you have that hook. So, uh, you know, th- that's kind of how I bridge from, hey, you know, like HCM and payroll, you know, that's kinda like the boring, you know, old school human software to, hey, it's actually just the entry point that allows you to build a lot of innovative business software in \[00:07:00\] other domains.

\[00:07:00\] **Albert Strasheim:** Um, you know, it, it's almost like the Trojan horse that gets you access to, you know, all of this foundational organizational data.

\[00:07:08\] **Andrew Zigler:** Right. I love that you call it a hook. Uh, that's exactly what it makes me think of. It's a big human hook on the organization that, you know,

\[00:07:15\] **Albert Strasheim:** Yep. Yep

\[00:07:16\] **Andrew Zigler:** runs and it keeps... And that it becomes then, um, like a really great source of context. And in a pr- in our old world that we lived in, you know, you were still keeper and custodian of this data.

\[00:07:27\] **Andrew Zigler:** You built the infrastructure that makes knowing and having that data up to date and all the relations of it accurate and trustworthy. All of that was already built in in there 'cause it needed to be because it's something as important as payroll. But now, it's almost as if you built a highway that ended at a, at a, a gate, and you just didn't know that it was a gate and not the end of the road, and then now the gate's open.

\[00:07:50\] **Andrew Zigler:** You're like, "Oh, I'm already going 1,000 miles an hour and I'm just gonna fly through this gate. I go on my highway I've already built." And now there's so much more downstream opportunities that can \[00:08:00\] consume and use this, uh, very trustworthy, very well sourced, but then also very rich and interconnected information.

\[00:08:07\] **Andrew Zigler:** And for enterprises and people who are provisioning and deprovisioning all sorts of access and roles and permissions and applications at scale, uh, within like a large org, like having that minute-to-minute updated information is critical for building workflows right now that are gonna be durable. I think a, I think the trustworthiness and the reliability of that kind of data is a huge blocking point for most enterprise orgs that wanna try go to, try to go down that route.

\[00:08:35\] **Albert Strasheim:** Awesome

\[00:08:36\] **Andrew Zigler:** do you, how do y'all then identify and partner with those kinds of builders, and what kinds of opportunities have you seen now that the gate is open?

\[00:08:43\] **Albert Strasheim:** I mean, I think the, the big opportunity has really just been helping companies do more with their data, right? Um, you know, a-as we've launched our Rippling AI product, it, it's been remarkable to see... You know, you'll, you'll take like an, uh, an HR \[00:09:00\] business partner or some kind of, you know, HR admin at the company, you know, they are, uh, asking some questions about their employee base.

\[00:09:07\] **Albert Strasheim:** You know, suddenly they're able to, uh, you know, easily inspect the activity logs. You know, they can find out where, uh, you know, certain, uh, members of the, the company are working. There might be some kind of security issue. You know, sometimes there's, you know, fraud at a company you have to investigate. It, it gives a lot of the folks like managing these business is a lot more power, uh, to, to navigate, you know, all of the company data, and it is remarkable what you can, like, get done when that happens.

\[00:09:39\] **Albert Strasheim:** And so I think to some extent, uh, a lot of the building we've done has kind of like gone from, hey, you just wanna organize the organizational data to, uh, you know, get people paid on time to you can actually glean a lot more insight for how you run your business, you know, whether it's, uh, you know, delivering like better services to \[00:10:00\] customers by, you know, knowing when people are on PTO or when they need to take some other kind of leave so you can rejigger your schedules or, you know, detecting some kind of security issue or fraud issue, you know, as you run your company.

\[00:10:12\] **Albert Strasheim:** Like that access to data, you know, it wasn't just like about organizing the data for payroll, it's now like you can access data for a bunch of other reasons. And so I think, that-that's been super gratifying to see. And, you know, to your point too, it's been remarkable to see how primitives that we've built, like permissions and workflows and reports, you know, have come alive, you know, in this agentic era as well.

\[00:10:37\] **Albert Strasheim:** 'Cause you know, you now have agents that can act, uh, you know, on behalf of the humans, but they don't see more than the humans can. You know, you can, uh, you know, trigger, uh, workflows and, you know, have them, you know, take, you know, more kind of like agentic, you know, non-deterministic actions in some cases.

\[00:10:56\] **Albert Strasheim:** Um, you know, you can build reports more easily, again, \[00:11:00\] leading you to insights more quickly. And so the whole kind of like agentic, you know, engineering on top of the company data, uh, you know, really allows many more people at the company to get stuff built and, you know, run their part of the business better.

\[00:11:14\] **Albert Strasheim:** So, you know, just seeing that theme, uh, reoccur over and over.

\[00:11:18\] **Andrew Zigler:** And that's a really big problem space to solve for too. When we've talked with guests on the show who have built really ambitious, almost like chat and assistant-like services inside of their like very wide surface area applications. Like we had, we had Andrew McNamara here from Shopify talking about Shopify. You know, they have their assistant, their buddy assistant that is like a... It'll run your whole store for you. You can do literally everything in Shopify. So like how do you deliver... And anyone can sell any kind of thing on Shopify, so how do you deliver an agent that can meet all those kinds of stuff? We talked about obviously this falls into a world of, of evals and testing and building trust and, and, and how you engineer those systems in general. Um, and I \[00:12:00\] think there's hints of that in what you're saying because we're talking about unifying now into a platform and the platform becoming the opportunity. this is kind of where I wanna shift gears and understand more about how you lead an engineering org through that kind of challenge. That's a lot of what we like to get to the heart of on Dev Interrupted, and I think that's a really powerful takeaway for folks right now to understand, like how did you, uh, create, identify and create those opportunities within your engineering org that became these things that are like the AI assistant and this AI ready highway and stuff like that?

\[00:12:32\] **Albert Strasheim:** Yeah, it's definitely been an interesting journey over the last few years, and I, I think a key, uh, you know, to getting us to where we are is, uh, I think a healthy balance between, uh, investment in, you know, more like foundational platform primitives. You know, here I think about, you know, a, a bunch of engineering work we've done on our data layer, you know, both the transactional and the analytical data, the ability to store that, query that, um, \[00:13:00\] you know, define, uh, custom objects a- and custom functions and custom apps on top of that.

\[00:13:05\] **Albert Strasheim:** You know, these are all like platform, like building blocks to some extent, but then also creating a lot of space for the engineering teams to experiment with and, you know, compose those, like, platform building blocks in a w- wide array of solutions. You know, some of those became products, some of those, you know, we sometimes parked, um, but then would come back to later.

\[00:13:28\] **Albert Strasheim:** And the reason to build them was to, you know, kind of expand the capabilities of the platform or stress test new platform capabilities. And so, you know, uh, it's a bit of a, you know, not like let a thousand flowers bloom, but maybe let, uh, many dozens of flowers bloom, and then periodically you kind of wanna reap some of them into a bouquet.

\[00:13:48\] **Albert Strasheim:** And so when I look now at a lot of the, you know, the AI, uh, products we've released or, you know, things coming soon, you know, a lot of them came down to bringing together a few \[00:14:00\] product engineers, a few platform engineers, taking a couple of these, you know, building blocks and assembling them into these products.

\[00:14:07\] **Albert Strasheim:** Um, a- and so just making sure the team had the, the freedom to do that. Um, and I think the other thing that's been interesting is, you know, thinking about my own role and also a lot of the other engineering leaders. We've jumped in and, you know, kind of like line managed a bunch of these efforts over the last two years or so, where, you know, inevitably to build almost anything interesting right now, you need to pull a few people or a few components, like built across three, four, five teams.

\[00:14:39\] **Albert Strasheim:** You know, there's never gonna be one team that has all of the puzzle pieces or all of the building blocks under their control. You know, you can't always reorg to get to a team that has all of the pieces under their control or, you know, you, you realize, hey, we would need to make a team called the software team or the AI team that does everything at the company, like that doesn't really work \[00:15:00\] right.

\[00:15:00\] **Albert Strasheim:** Uh, and so you have to be a lot more agile when it comes to assembling these teams, you know, getting them to build stuff, seeing what works, you know, fine-tuning it, sometimes combining forces with other teams. Uh, and, and so just the, the willingness to, you know, break down some of the organizational silos and, you know, kind of like, you know, uh, I call it a big smoothie.

\[00:15:20\] **Albert Strasheim:** You know, make a bit of a technology smoothie and a bit of a team smoothie, um, and then, you know, stuff comes out. Um-

\[00:15:26\] **Andrew Zigler:** Make a s- yeah, make a smoothie. Just, like, break down all the barriers, put things back together. Everyone's learning again. Everyone's building from the beginning again. And we're also all too along that, this journey going to be reinventing how we even ship our software. Because under the hood of all of this innovation we're trying to deliver to our customers, we're also making our engineering pipeline, our SDLC, as agentic as possible so

\[00:15:51\] **Andrew Zigler:** we can support all of the needs that we're... You know, it's like an inverse pyramid, right? It's like everything is balancing on the one point, and that one point is your, is that ADLC. And so it's like you have to \[00:16:00\] think about that from the beginning. and, and I think mixing up the teams is really, is really smart. It lets you find new ways for teams to operate.

\[00:16:07\] **Andrew Zigler:** I'm sure that also too naturally complemented the reality of, engineers having to build new skills in order to be successful at your job. Like, what have you seen as being, like, the emerging new skills of, like, these new kind of pods or teams? Like, are... You're probably seeing a lot of, like, very broad generalists. What kinds of specialization distributions do you tend to see once you m- create that kind of environment?

\[00:16:31\] **Albert Strasheim:** I-interesting, uh, question. Yeah, I think the, the pattern I've seen a number of times now is, you know, you will have, you know, one or two AI-pilled engineers in an area that have really figured out, you know, like harness engineering or agentic engineering. and it really helps to combine them with some folks that are still figuring it out onto the same project.

\[00:16:54\] **Albert Strasheim:** So I think like having a bit of a smoothie of, you know, people that get it and people that are still figuring \[00:17:00\] it out. Um, I think another key thing has just been, you know, to the point earlier about, you know, maximally ambitious goals, just setting a very big, like near term goal for that team. A-and to some extent, it's almost a hack to force them to think out of the box and think about how they might, uh, you know, solve the problem using, you know, AI, you know, agents, LLMs, whatever the case might be, uh, in a completely new way.

\[00:17:27\] **Albert Strasheim:** And I think that has spurred a lot of innovation. Um, you know, for example, we did a project recently to expand the set of data connectors for ingesting data into Rippling. And as we kicked off that project, you know, the message to the team was clear. Like, "Hey, you know, it used to take, I don't know, n weeks to build a data connector.

\[00:17:47\] **Albert Strasheim:** You guys now have to build, you know, 10 of these in, you know, uh, two weeks. So let's go figure out how to do that." And it, basically, the team got together. They spent a bunch of time thinking about the \[00:18:00\] harness that would, uh, help them write the spec, the harness that would help them generate the code from that, and then also the harness that would generate the output.

\[00:18:08\] **Albert Strasheim:** Um, and so, you know, it, it took a couple of weeks of experimentation to wire that all up. But, you know, once you're through that, you know, everybody that was involved in that effort, like, thinks very differently about how to build software. Uh, and so you wanna do that, you know, over and over in every area is you'll find this one project where you force the team to think differently and build with AI and like, you know, also not everything's gonna work, right?

\[00:18:33\] **Albert Strasheim:** You, you learn some lessons about, hey, if you, you know, approach it like this, you're, you know, you're gonna get slop out or you're not gonna get functioning code out. But, you know, you can design compensating controls and, you know, feedback loops and eventually people, you know, make it click, and then they take that with them to the next project and the next project.

\[00:18:51\] **Albert Strasheim:** But you kind of have to engineer those enforcing functions to some extent.

\[00:18:55\] **Andrew Zigler:** Right. You kind of have to force the tiger team to have to exist. That's actually something \[00:19:00\] we, we, we heard from James Everingham when he was here on the show. He used to be a VP of engineering at Meta, and he talked about, uh, when they created their internal platform for how folks would build and innovate with agents and experiment internally about, like, what does and doesn't work. Um, the thing that consistently worked was setting impossible goals

\[00:19:19\] **Albert Strasheim:** Yep, yep.

\[00:19:20\] **Andrew Zigler:** of, of being like, " We're gonna set this metric, this North Star, this goal that we're all looking at it," and even I setting the goal, it's like it's... We- this, this is an impossible goal, but we're in a world where things that used to be impossible no longer are, and we have to at least check to see which of those are true and are still false.

\[00:19:39\] **Andrew Zigler:** And so it becomes, like, this, uh, really interesting in- investigatory time where people are thrown up against really impossible challenges, and from that you just get new ways of working. People, someone comes up with an ingenious new way of flipping the whole process or inverting it, and now suddenly you're cutting out baggage, and those are the kind of situations you kind of have to orchestrate as, \[00:20:00\] like, a leader order to get that share of information, uh, to kind of start building, to start building those practices, right?

\[00:20:07\] **Albert Strasheim:** E-exactly. Yeah. I think the other thing that's also come up is, you know, I-- you can build so much more in, you know, a, a two-day, three-day, five-day period, you know, or I, I think about 168 hours, you know, like what can you do in 168 hours? You frequently will see one or two engineers able to go very, very far in 168 hours, and so also unleashing them, letting them, you know, kinda like, you know, fight their way through the jungle and then, you know, hel- letting them show the rest of the team what's possible.

\[00:20:41\] **Albert Strasheim:** 'Cause a-again, it's an-another way to, uh, help people imagine, you know, more is possible, uh, more can be done faster, and, and so you just have to create a bit of space to, uh, do that kind of thing with the team.

\[00:20:53\] **Andrew Zigler:** Yeah. So I have a question about where you might think that the future is going. For a lot of companies like yours that are \[00:21:00\] in the market and, and SaaS companies in general, I think are facing in a lot of different environments and industries like this build versus buy dilemma, where things that yesterday would be something that you would, of course, buy from a vendor become a quick, uh, build decision.

\[00:21:16\] **Andrew Zigler:** And while I think the world of Rippling and what y'all do is far beyond a build versus buy debate, and the obvious arguments are there for why you would buy Rippling as opposed to r- you know, vibe code your own. But all of that aside, the reality is that there's a whole apocalypse happening for SaaS over here. And so you're gonna get a lot of losers in this space where their data and the world that they lived in no longer has a home, it doesn't have a place. Does Rippling become th- this kind of data layer where maybe you see types of information living from m- the, the entire organization beyond just, like, some of the initial points you've been talking about, like, you know, obviously HR and, and finance and such? Where do you see the future \[00:22:00\] being for Rippling and the kind of data it's sitting on?

\[00:22:03\] **Albert Strasheim:** Yeah. Um, I think the, you know, the, the data layer is of course very important. Um, you know, the, the big push for us right now is to ingest, you know, process, compute over, apply intelligence to much wider sets of business data. And so you're gonna see quite a few products from us, you know, landing in the next couple of months in this realm.

\[00:22:27\] **Albert Strasheim:** And so I do think, you know, to survive as a platform here in the long term, you need, you know, access to, uh, pretty expansive sets of data. You know, you're gonna see this across the industry. Everyone is going to be fighting to import, you know, everyone else's data. You know, we're, uh, we expect some, you know, like bilateral data treaties to emerge.

\[00:22:49\] **Albert Strasheim:** You know, I'll give you my data if you give me, uh, mine. Uh, it's going to be interesting, of course, to, you know, uh, if you ask some customers, they'll say, "Hey, it's not the platform's data, it's my \[00:23:00\] data." You know, uh, I, I think there's a lot to be reconciled there. Um, but generally platforms are going to work hard, I think, to amass data.

\[00:23:09\] **Albert Strasheim:** Um- That said, though, I don't think just being a data store or, you know, even being a system of record for some sets of the data is enough. I, I think there's a lot of other systems of X that you need to become to be a, a durable platform in the long term. Um, you know, for example, I think you have to be a, a, a system where you see events, uh, in the world, we're tentatively calling as like a system of triggers, right?

\[00:23:39\] **Albert Strasheim:** So you see things changing about an organization, or you see real-world events, and you are notified of them, and you're frequently the first system to be notified. Like, you need to be that. You need to be a system of record. You need to be a system of work, i.e. humans and/or, you know, agents come into your platform to do their \[00:24:00\] work.

\[00:24:00\] **Albert Strasheim:** You know, that generates more data that you can store in the system of record. It generates more events that you can trigger on, so there's a bit of a flywheel there. But like, you know, people have to do work, uh, in your system. Uh, I think you need to be a system of compute. You know, the agents should run, you know, inside of your system or inside of your platform, like close to all of the data that you're a record for.

\[00:24:22\] **Albert Strasheim:** And I think finally, you also need to be a system of action. So in many cases, if you, uh, you know, if some data changes or some compute has run, there is still some real-world side effect to be achieved. You know, you need to, you know, make a payment. You know, in, in the digital world, maybe, you know, you need to send a Webhook or send an email.

\[00:24:41\] **Albert Strasheim:** But in the physical world, you know, you need to make a payment, you need to file a tax form, you need to send a physical piece of mail, you need to ship a laptop. Um, I think it's important for durable platforms to also have, uh, you know, one foot in that realm. So if you can string all of that together, you know, all the way from triggers to data and compute \[00:25:00\] to action, uh, with some work happening on top, then I think you stick around.

\[00:25:05\] **Albert Strasheim:** If you're merely a database with some forms on top and, you know, you're not a key part of those workflows, I think you would struggle in the long term

\[00:25:12\] **Andrew Zigler:** That's a really fascinating new kind of product to think that doesn't exist yet. The idea of it's almost like a harness on your business or on your organization, and

\[00:25:23\] **Albert Strasheim:** Exactly.

\[00:25:26\] **Andrew Zigler:** advice, but you can also interact with it in a natural language way. And that's a level of insight and operability that you don't typically see from the kinds of data sources that, uh, platforms and providers are able to put together.

\[00:25:41\] **Andrew Zigler:** So it's really high leverage to be able to provide that kind of assistantship, you know?

\[00:25:45\] **Albert Strasheim:** A-absolutely. Yeah, I, I think the, the natural language element, as you mentioned, that is new and that is very exciting. You know, the, the other dynamic, you know, we see unfolding is, you know, also \[00:26:00\] natural language as another way to write code or configuration that makes the system do what you need it to do, right?

\[00:26:07\] **Albert Strasheim:** You don't always want to compute in natural language terms. It is probably sometimes a good idea to turn some of that, uh, back into code as well. Um, a-and I think the other thing that's going to be very interesting is, um, you know, kind of as you s- you mentioned, I think we are steering towards a bit of a, you know, observability and maintenance apocalypse here.

\[00:26:28\] **Albert Strasheim:** Like, I think as an industry, we haven't fully figured out, you know, after you build all of these complex workflows and complex, you know, systems of triggers and actions and compute and, uh, and, and all of that, like how do you keep it all working, especially if there's non-deterministic, you know, like AI, you know, in the mix as well.

\[00:26:48\] **Albert Strasheim:** Um, and so I, I think evals are-- they're just the beginning. We have so much to learn about how you test these systems, keep them reliable, keep them working when there's, you know, basically like so much entropy \[00:27:00\] trying to tear them down again. Uh, and so I think we're in for a exciting ride, you know, trying to build platforms that deliver reliable solutions in the face of all of this, like non-determinism and uncertainty.

\[00:27:13\] **Albert Strasheim:** Like that, that's gonna be the big challenge.

\[00:27:15\] **Andrew Zigler:** Yeah. Well, you call it an, an, uh, apocalypse for the observability tools, but really it's an explosion of opportunity for them because the

\[00:27:22\] **Albert Strasheim:** Exactly

\[00:27:23\] **Andrew Zigler:** the eval, the eval layer is so critical, um, and it, and it is so important. Something I wanna understand from your perspective is what do you think matters to you and your engineering team, uh, and where do evals fall in the process?

\[00:27:35\] **Andrew Zigler:** Do you start with evals? Um, do you think evals are... How important are they to the contract of delivering the software that you deliver now, uh, since it's such a baseline part of measuring them?

\[00:27:46\] **Albert Strasheim:** Absolutely. Yeah. I, I really think, you know, to some extent the eval is the new unit test and, you know, also frequently the integration test. and so, you know, in the same way as you couldn't build traditional software without \[00:28:00\] tests, or you certainly would struggle to build good ones, I, I think evals, you know, in this new agentic era are incredibly important.

\[00:28:07\] **Albert Strasheim:** You know, at the same time, we're finding that you still have to carefully inspect all of the subsystems that make up, you know, one of these, uh, you know, AI, uh, machines and, you know, you, you can't just rely on, uh, you know, end-to-end evals. You know, things we've seen, for example, is that, uh, you know, the models are actually remarkably resilient these days, and even when sus- subsystems are malfunctioning, they'll still kinda sorta, you know, solve your problem for you, but sometimes quite inefficiently, right?

\[00:28:41\] **Albert Strasheim:** They'll take a very long path to, to get to an answer, or they might do it right, you know, once, but like fail the next time. So there's, you know, some unreliability that can come from it. And so I don't think evals is the whole story. You still need to carefully observe, inspect, trace through the underlying \[00:29:00\] systems as well.

\[00:29:00\] **Albert Strasheim:** And so, you know, a healthy balance of, you know, like both approaches I, I think is key. And then, you know, figuring out how you also, you know, express assertions about the underlying system behavior. You know, making sure all of the, the, the, the tools that get called by the models or, you know, the retrieval systems that fetch data, like they still have to work, right?

\[00:29:22\] **Albert Strasheim:** You, you can't forget about them. So, you

\[00:29:24\] **Andrew Zigler:** Yeah,

\[00:29:24\] **Albert Strasheim:** balanced testing and evaluation process is really important

\[00:29:29\] **Andrew Zigler:** And, and to those that, that have been listening on Dev Interrupted, that is all of our guests really in, in sequence, everybody is agreeing about the importance of evals. We continue to stress that here, um, with our leaders who build these incredible products that have huge delivery spaces, how it always, uh, the eval is such a critical part of that build loop.

\[00:29:48\] **Andrew Zigler:** So just another, uh, reminder for our listeners as well. And, you know, we talked about it, um, a little bit earlier about the new problem space and, uh, or rather the new data offerings coming from Rippling \[00:30:00\] to serve customers on this kind of data front we talked about in the beginning as well. can you tell us a little bit more about the Rippling Data Cloud, um, and what that is going to look like and what people can expect?

\[00:30:13\] **Albert Strasheim:** Yeah. So yeah, as I mentioned earlier, you know, so we have a n- a number of like data capabilities coming to the platform. Uh, some of this has been released, uh, already. An, an example of this is a, a product we called App Studio that came out I think almost a year ago at this point, which allowed customers to define, uh, custom objects and custom, uh, functions in the system, and then build, you know, canvases on top of that.

\[00:30:39\] **Albert Strasheim:** You know, some of that is, you know, think of it as like pre-AI application building. Um, since then, we've added a significant number of data connectors to the platform, so you can ingest third-party data, uh, into these custom objects or into what we're, uh, calling lake objects. So yeah, so essentially hitting, uh, a large scale, \[00:31:00\] um, you know, kinda like data lake, uh, store directly.

\[00:31:03\] **Albert Strasheim:** Uh, we've built a data catalog where all of your native Rippling data and all of these custom objects live together. Uh, as you can imagine, when you have an AI agent trying to write queries across all of your business systems, having access to a catalog is critically important. We are releasing a transformations product that allows you to express, you know, in SQL like transformations of your data, you know, so from some source table to a, a destination table, so you can, you know, pre-materialize, uh, you know, additional views on your business data.

\[00:31:35\] **Albert Strasheim:** And then we have a, a ton of exciting new, uh, capabilities coming in the reports and dashboards space as well. So we've significantly expanded the dashboarding, uh, or reporting product, um, and also built a set of capabilities to allow customers to use AI to build dashboards, uh, and get new insights. Uh, it's something I'm incredibly excited about.

\[00:31:57\] **Albert Strasheim:** It, it's been remarkable to see, \[00:32:00\] you know, the, uh, the agents on the platform be able to take the data catalog, run some queries to grab sample data, and then like build, you know, insightful dashboards. So I think a lot of the point-and-click to, you know, learn about your business is gone. You know, you're gonna wake up every morning with a dashboard, you know, tailored to your needs.

\[00:32:19\] **Albert Strasheim:** Um, and so just like...

\[00:32:20\] **Andrew Zigler:** dashboards, dashboards. So

\[00:32:22\] **Albert Strasheim:** Yeah, it is, it's dashboards all the way down.

\[00:32:24\] **Andrew Zigler:** Yes

\[00:32:25\] **Albert Strasheim:** exactly. Um, and so really I just think the, the way the average person in a business is going to engage with their data is about to transform, and I think you're gonna find a lot more insights, uh, you know, served up to you and, and actually useful insights.

\[00:32:40\] **Albert Strasheim:** And, and so, uh, all of that is coming from us in the next couple of months, so very excited to get it out there.

\[00:32:46\] **Andrew Zigler:** Great. Well, we'll include links to that so folks can go learn more about all of these new surface areas that you can interact with within Rippling. And, uh, to those listening, you know, if you're not already following Dev Interrupted, be sure to find us on \[00:33:00\] LinkedIn or Substack, where we publish our weekly newsletter that comes along with this interview with Albert, where you can find also included a weekly news roundup about what's happening in the agentic engineering space. While you're there, be sure to follow Albert and myself as well on LinkedIn, and stay up to date with all of the developments coming from us. And Albert, thanks again for coming on the show. It was such a pleasure to have you here today.

\[00:33:23\] **Albert Strasheim:** Thank you for having me. Really enjoyed the chat.

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