Krishnamurthy is describing what happened when Stripe pointed internal agents at its own data and infrastructure. He is not warning about a hypothetical: in the same answer he says agents went rogue and nearly took down core systems before the team caught it. His point to Claire Vo is that agents do not introduce new categories of weakness, they find the ones already there faster.
100%. That is such a great question. So, like I said, luckily at Stripe, we care about our data so much that we've invested a lot into both the data querying layer. We use Trino as our data sort of querying layer and our warehouse in that perspective. We've invested a lot into making that super resilient, right? And those investments have helped agents slam it like crazy and not bring it down, right? We've invested a lot our data, the data platform side of things. We've invested a lot in a catalog of data and tiering of data. So we have access to schema that can quickly tell us, oh, these are the relevant data sets that you might want to find and use and how would you use it, right? But even there are higher-level investments as well. There is a blessed analytics layer where like the really key metrics go in, right? And like there's a tiering system where there's an analytics layer. If you fail that, you go look at all the standard data dashboards that we have and you use the queries from there. And if you fail that, then you use the data catalog and search through for the high quality data sets and figure out how to use it. Agents are incredibly good at figuring this out. However, the key part, and you asked about the ask data skill, the key part is we have some really smart data scientists as well who sort of said, hey, this is probably the right way that most data queries should be handled. And what the skill does, if we dig into the ask data skill itself, what it's going to be saying here is route to direct artifacts first, use the analytics layer first, and if that fails and fall back and fall back and fall back until you like hit, you actually hit the data catalog directly, right? So these investments were made for humans, but have held up really well for agents because terms are the reasoning through it, agents have the same problem. They can answer the question, but they have no idea if it was the right query or the right table. And these investments have paid off in helping guard that.
I want people that are listening to hear a couple things. And, you know, I'm going to make the Stripe team blush. I say this specifically about Stripe a lot, which is, I think one of the reasons why Stripe has been able to benefit so much from AI is prior to AI, there's been a commitment to developer experience, developer platform, data platform, analytics layers, like all these things that made humans really efficient at the company pre-AI are foundational investments that now give you extreme leverage when you throw agents at it. And so, you know, when people ask me, like, Claire, what can I do to ship more product with AI? They think I'm going to say something about product development. And I say, double the size of your DevX team, double the size of your data team. Like work on platform investments, good for humans, good for agents. And that's what will let you run. The other thing you said, and I don't want people to miss, because I love this idea of this like three-layer triage that a data agent can go through. And that's really smart. Like find existing reports, please. Then use the analytics layer to find the right query. And then if like you really have to, fall down to the data catalog and write your own query. The thing that I also heard you say is your data warehouse has to be very resilient to high volume queries because when in doubt, an agent will just brute force it. And so again, this is like infrastructure hardening investment, performance investment, not sexy, not what people are thinking about when you're building these data agents, but actually allow agents to do a really effective job because you don't worry about like, you know, turning over your data warehouse because an agent is hammering it.
100%. And like everything that you said makes so it's, it's, it resonates so much with all of that. My personal history at Stripe has actually been on each of the kind of teams that you referenced. So I'm like, yes, someone gets it. So this is great. The thing about resilience, agents are very creative at bringing your infra down. What can I say? They're like, it's almost like all these scripts that they were trained on just teach them to be script kitties or something, right? The thing that we really did well is thinking about agentic identity. Like we haven't solved this yet, right? But thinking about how do we say that, you know, this is an agent and this is what it's trying to do. Like what is the use case it's trying to use as it goes around doing its thing in our infrastructure and using this. That as a way to think about priorities and load shedding and all of that good stuff. Again, not super sexy, very like deep infra stuff, but the same principles apply. It turns out that agents just like dial up all your failure modes. It just multiplies the amplitude of problems you can get, right? And the investments, I wouldn't claim that we did not have any issues. We definitely had a bunch of issues where when we started doing this, like there were agents that went rogue. There were agents that may have almost taken down core systems, but we caught it in time. And now we've hardened those systems as well.
I love it. Okay, so we've yapped while Kai ran. Let's show what Kai actually generated using these skills and tools in Sandbox.
Yeah, of course. So here's what you see. You see that, you know, Kai adoption is looking good. And this is something I'm personally super happy about. Like pretty much everyone at Strike uses Kai, like 86 plus percent of the company now. So really AI for everyone, which is how we started out this process. And you see this ramp that's gone from a fairly low number. I think if we had done this a couple of weeks ago, it would have been in the hundreds up to a very high number. So happy to talk more if you're interested, if viewers are interested into how we manage that. But okay, we have a dashboard. Dashboard looks good. It also looks like vaguely stripey. So I need to go back and see how the agent figured out that it needs to make things blurbable. So I got to go figure that out. But it has a bunch of things here. It's an interactive dashboard and it has links to a bunch of things, right? That's fine. This is great. We can already see how this can be useful for like, I now generate a dashboard every meeting I go to because it's so easy and it helps me drive the meeting a lot better. But the real power here starts to come in when you talk about multi-turn conversations, right? So great. We have a dashboard. Awesome. But let's do something more. Let's sort of like get Kara to iterate on this for us, right? So, hey, I love this dashboard, but let's do some more here and use this query, get a breakdown, yada, yada, yada. And it's going to do some interesting things here. So I'm going to kick this off, but I'm going to talk through what I'm doing, right? A, the dashboard isn't like, the artifact isn't like created and like it's not fire and forget, right? We give a chance for people to do deep work by iterating on their artifacts, and that's really powerful. It's better for token efficiency. You don't want to be throwing away your HTML dashboard every turn, but it's also really moving into this idea where the agent and you are collaborating on a task, right? And we have turns that are like super deep, like hundreds of turns over multiple weeks. So the idea here is you have sort of like a somewhat like a pretty smart collaborator who has some artifacts and you can iterate with them on it. I'm going to add this query and I'm going to do some really interesting things. And this is something that I think it's worth getting into. I'm telling it, okay, it's not just pulling the data. It's not about pulling the data and displaying it. I want you to do things with the data. I want you to like munch the data in some way or form so I get what I want. And the reason why I'm touching upon this is a lot of the data sort of things that people want to do end up being last mile data. You think about people's workflows, it's so different. It's so hard to build a dashboard for everyone to do every part of their job. And then you have like a gazillion dashboards and how do you manage them? You can't keep the right dashboards at the right level of quality. Using an AI like Kyle to do this means that you can create like light apps almost like the whole like the lovable style thing where people are creating apps to just hyper optimize for their workflow. And the fact that they have a sandbox that anybody, regardless of whether they're an engineer or not, can get the agent to write code for them and do whatever the heck they want with the data. It's really powerful. And I'm pretty sure that, again, as expected, it's gone in. It's sort of like said, okay, here's the actual data. And I want you to go do some summation somewhere to do the other tab and out at K. So super interesting. And if I open up the updated dashboard, it's the exact same dashboard. And you should now see this really cool little segment below that. So I could keep yapping about this dashboard. I love the fact that our marketing team is like 100% all in. They need
it, right? I don't know a single marketing person that doesn't either want some sort of app built or some sort of dashboard. You have product market fit. That leads me to my next question, which is: how do you roll out? I'm just curious, kind of, you know, inside inside the doors of Stripe, how do you roll something like this out? Is it really organic adoption? How did it get built? How did it get shared to the team? Was this like 20 engineers? Like, how did this come to be?