How Stripe built “minions”—AI coding agents that ship 1,300 PRs weekly from Slack reactions | Steve Kaliski (Stripe engineer)

At Stripe, we're landing about 1,300 PRs that have no human assistance besides review per week. A lot of where our work begins is it could be in a Google Doc as we're planning a new feature, or maybe a Jira ticket comes in, or we're talking about something in Slack. I can click an emoji and then the menu will sort of attempt to one-shot resolving that prompt using all the tools that are available at Stripe.

When you're in larger organizations, there's so much friction that can come between a good idea and getting it into the world.

Not only can I have one of these, but I could have many, many of these running in parallel in isolated environments, making isolated changes all at the same time.

How are you getting all this code review done?

Whether the text has been written by Steve or the text has been written by Steve's robot, you still want that CI environment that's providing confidence that the code that's being changed is safe and that as it rolls out, we're having blue-green deployments so you can roll back to. All that is super critical independent of the nature of the authoring of it.

No matter how juiced these laptops are, you get 3 or 4 work trees in and like it starts to sound like an airplane taking off. It's no good. And so I do think on this multi-threading agentic engineering work, cloud environments and virtual environments are so important to unlock velocity. Welcome back to How I AI. I'm Clara Vo, product leader and AI obsessive, here on a mission to help you build better with these new tools. Today we have Steve Kalisky, a software engineer at Stripe, and he's going to show us how the Stripe team deploys a bunch of minions to do their engineering work. We'll also watch an agent spend a little bit over $5 to plan a birthday party all in Claude Code. Let's get to it. This episode is brought to you by Optimizely. Most marketing teams aren't short on ideas, but what they are short on is time. And that's exactly what Optimizely Opal gives you back with AI agents that handle real marketing workflows. You know, like creating content and checking compliance, generating experiment variations, personalizing user experiences, analyzing pages for GEO, even tasks like approvals and reporting. It's your AI agent orchestration platform for marketing and digital teams, plugging seamlessly into the tools you already use, handling the boring busywork, and keeping everything on brand. That leaves marketers with more time to do your actual job. See what Opal can automate for your team by signing up for a free enterprise agentic AI workshop with Optimizely. Find out more at optimizely.com/opal. Attend live and you'll get a free pair of Ray-Ban Meta AI glasses. Steve, I'm so excited to have you on How I AI because I saw the Stripe minions on the timeline and one, exceptional branding, don't sue us. And two, I just love the idea that you and your colleagues in the team at Stripe have created Not just one agent, but minions all across the company that can help with development work. And I'm so excited for you to show us how that helps you in your day-to-day here. So welcome to How I AI.

Thank you for having me.

So tell me, what has been the effect that minions have had on you personally at Stripe and at the Stripe team as a whole?

Sure. So, you know, for me personally, I think sort of anecdotally, I don't remember the last time I started work in the text editor.

Right.

So I do end up there often. Um, but you know, what I found is that, you know, a lot of where our work begins is, you know, it, it could be in a, in a Google Doc as we're planning a new feature, or maybe a Jira ticket comes in, or we're talking about something in Slack. And those are sort of like the more natural entry points to starting work, right? And then you end up in a text editor when it's time to, you know, actually do the work or make the final tweak. Um, and it's just felt very natural. Um, and, and I think in particular, the sort of like activation energy of starting work feels a lot lower. Right. So if, you know, you're in a Slack thread and maybe there's a piece of user feedback and it's something simple, like, you know, we have to update the docs or maybe it's something more consequential and we just want to build a prototype, I can click an emoji and like the work begins. And often the work finishes too. We at Stripe, we're landing about 1,300 PRs that have no human assistance besides review per week. But at the minimum, the activation energy of like, starting to write code, seeing tests pass, maybe a test fails, occurs without me even, you know, participating. And then I can jump in and I can tweak and I can kind of like have them— that momentum sort of, uh, sort of like generative momentum, you know, that I can hop in halfway through.

What I think is magical about this, and I won't call Stripe a big company, but you do have a decent amount of employees and a very, very large business, is I love that concept of activation energy going lower because when you're in larger organizations, there's so much friction that can come between a good idea and getting it into the world. And it's not malintent, right? It's nobody is like, oh man, I really want to slow this process down.

Yeah.

It's either, you know, functional. I don't have access to a technical area of expertise to actually get from here to there. It's operational. I don't know how to organize people and communicate effectively to get the next step done. Or it's just kind of like people get siloed in their day-to-day and don't think of new ways to get work done. And one of the things that has been so revelatory about AI for me personally is like all that just kind of goes to zero because coordination costs can go down, execution costs can go down, communication costs can go down. You just get closer to the work, which I think is the fun part we all really care about. So show me how you actually activate a minion. And, you know, we skipped this a little bit, what a minion is.

The quick spiel of a Minion. When I, as an engineer, sort of in pre-AI time, you know, want to make a modification to Stripe, well, Stripe is a huge codebase with tons of services. It can't run on my computer alone. So Stripe already has a long history of, you know, investing in great developer tooling, having hosted development environments that I can spin up that, you know, have all the code already there and services running, and I can SSH in and make modifications. And we have a ton of great CI tooling around that. So that's the context. You know, we have all that. The idea with the minion is that I can provision one of those environments seeded with a prompt, and then the minion will sort of, you know, attempt to one-shot resolving that prompt using all the tools that are available at Stripe, right? So all of our internal documentation, our internal CI, our, you know, test data, so on and so forth. And it will loop through that in an attempt to solve that problem. So let's go ahead and jump in and see what sort of a prototypical experience might look like. So I'm in a Slack channel, it's called Steve Klisky Robots Claire. I actually have a Steve Klisky Robots channel that has 76 humans in it, but I do have every, it started off as just me and my robots, and now there's some sort of audience observing. But let's imagine that, you know, maybe I'm thinking of a new feature idea or I want to improve documentation that we have. So we have a launch coming up soon and I want to sort of embellish the documentation. So I'll say, I have this cool idea for docs.stripe.com/payments/machine. This is our new machine-to-machine payment worker, which we'll look at later in our call. And I want to, you know, Make sure the landing page really sticks and gives a good code example of how to get started quickly.

Right.

So maybe someone posted a message like that or it came in through a ticket or whatever the origin may be. All I have to do now is, you know, add a reaction, which is create minion pay servers. This is a particular repository within Stripe. We get the 1 sec cooking from the DevBox agent. And then we get a reply in here saying your Minion for Pay server, it's the repository for a new branch that's created, landing page code example has been created. And it's going to kick off our docs service so I can eventually preview it. Now I'm going to click follow along. So right now what it's doing is it's provisioning that development environment I was talking about earlier. Right. So this is, this part isn't new. It is excellent, but it's not new. And basically it's going to spin up an instance in the cloud. It's going to apply all the configuration that's required for both me and the agent to do coding within Stripe. So this will just take a few seconds. It's going to check out that repository with a new branch, configure the local database, apply my Git config. It's going to set up a VS Code server so I could connect to it just through the web or locally, as well as some extensions. So what's really like great about Minions is, you know, obviously there's the agent loop that's, you know, making the code modifications, but it's built on top of like a ton of incredible work that our developer productivity has done around just making it easy to get like a perfectly operating Stripe development environment for coding, which means that, you know, not only can I have one of these, but I could have, you you know, many, many of these running in parallel in isolated, you know, environments, making isolated changes all at the same time. So, you know, that little one-click emoji, I could have done that with a few messages at the same time, which is really great. So.

Yeah, one thing I wanna call out here is we had my friend Zach from LaunchDarkly on, and one thing he said was, look, what's good for the developer is good for the agent. So there's this virtuous loop of if you have or do invest in developer experience for your human engineers, Your agents will benefit off of that. And in turn, if you invest in developer experience or agent experience for your agent engineers, your development team benefits from that. And so I always tell people, you know, engineering team, you've always asked like, can we just give a little bit more time on the roadmap to DX? Like, pretty please, can we invest here? And I think if you attach it to an AI initiative, that's like the secret way to get some of that, that good stuff done.

Totally. Yeah. I mean, imagine you're, you know, some code bases are small, but, you know, Stripe is huge. You know, imagine you show up day one and there's, there's no documentation and there's no tools and there's, and they just say, good luck. Like anyone would have trouble. And even if you threw the agent at it, it's very likely that the, the context window would be blown by the whole code base. Just scanning through it to understand all the intricacies would be like impossible or extremely expensive. So. If there's a very blessed path for 90% of the common activities in being an engineer at Stripe, that makes the propensity that the agent succeeds really high too, right? So imagine we wanted to make an API change, which we do hundreds or thousands of times a year. We have really good documentation on how to add a new field or a new method or a new resource that the minion would read and would execute against. And then the propensity that it would one-shot is very high. So Good docs for developers are equally important for the agent, to your point. So we've now transitioned from, you know, booting up the development environment to now we're in the first agent run. So we have that prompt that I posted in Slack here. And now what it's going to do is boot up an instance of Goose. That's basically the harness that's, you know, going to run through all this.

We did have an episode with the Block team about Goose, this open source agent harness. That got set up. And I want to call out one thing for folks that are not watching and are listening, which is I love your system prompt. So sophisticated. It says, implement this task completely, colon, and then just whatever you put in. Yeah, no mistakes. No mistakes. You forgot no mistakes. But, you know, I think people really think they have to over-architect their initial prompt. And I think if you have a great harness, it can go a long way to extracting out um, a successful outcome from a pretty loose prompt.

Totally. And, and we, a lot of this is an experiment in some way, right? You know, as new models come out and, you know, we build new tools, like there is this sort of dynamic nature to it. And we, we've built a lot of interesting, you know, bots that help write the prompt, right? So, you know, maybe first it will do the task of searching through the code base or looking at other pull requests or Google Docs or whatever it may be. Yeah, I, I think now it's Stripe. Most things that could have an MCP server have an MCP server. So we're able to interact with a lot of the internal data we have, and then it can make a prompt that I could then paste in here, or I get assigned to the agent. So that's sort of, I, you know, part of why I wanted that public channel we were looking at is like, you know, working on a CF that we don't pair program anymore, but we, you know, pair prompt, right? And that activity could be with other engineers or other data sources or other agents too, right? To figure out if we can properly explain to the agent how to do it correctly. In any case, what it's doing now is it's taking the link I gave it, which is to public documentation. It's going to search through the code base and use some of our code searching tools to locate where that change in particular should go. It's going to execute a whole sequence of tools. And over time, as it figures out where in the code base it should work, what the modification should be. They'll ultimately commit those and make those available in a pull request that, that me and my fellow colleagues can review.

Yeah, I have a couple, couple questions on this because we've seen a few examples of folks building their own cloud agents and, and kind of, and I'm curious, you know, why, why Goose, you know, versus doing something on your own or doing sort of a more commercial solution. I'm curious if there was an internal discussion or how this— or did this happen organically because it worked for one engineer? Curious how you kind of seeded the idea of, of Minions on top of your development tools.

Yeah, sure. So, you know, we also make Cloud Code and Cursor and tools like that widely available to engineers at Stripe. So, you know, I think our general sentiment is like we want to accelerate development so we can build new features for our users. And there are going to be new models coming out, new tools, and we want to be able to proliferate those as much as we can. In the particular case of Minion, it's very, I don't want to say very specific, but it's very specific to like the Stripe developer experience in the Stripe developer environment. And we had been experimenting with Goose early on, and I think in this particular case, we'd forked it to make some modifications as well. And really what we were looking at is like sort of a base harness and Loop to apply all of our own tools and software to. So we spend a lot of time on like making good tools available and making sure that the sort of routes that the minions go through, you know, work closely with like the most common Stripe developer workflows. So it's, you know, sort of like commercial versus, you know, custom things. Like there are things that are very specific about Stripe's codebase and being a developer at Stripe and the way we build things. That it was just sort of easier for us to build and deploy that. But the commercial solutions are great and we use those extensively. And even later on in this demo, I can sort of show like I can, you know, for example, I can pop into VS Code Web where I could manually edit some of the code that's going on here as well. But I can also boot up Claude and I can have sort of the typical Claude experience with all the Stripe MCP tool, internal Stripe MCP tools available as well. So, you know, there's, there's no singular tool to rule them all, but I think the like overall end-to-end development story at Stripe is built on Minions. So you can see I'm in that dev box in Claude now. Yeah.

Cool. I have one other question and then, and then an observation I want to make sure that, that the listeners don't miss. So my first question is, you know, Stripe is a very well-resourced, I would say, engineering organization. So I'm presuming you have a team dedicated to working on not just your dev tools, but as well as, as Minions and managing that as an internal product itself? Has that team been sort of built, um, as a standalone team that's focused specifically on internal developer experience? Is that how it works?

Yeah, we, we've had a developer productivity team for as long as I can remember. I think I've been at Stripe 6 and a half years now. And, you know, that team's focused on all the tools that I engage with, um, and, and making them more useful, right? So that's All the way from, you know, how we interact with Git and version management to our text editors and our configurations there to our development environment and how that whole story pieces together. And, you know, we, you know, just as, you know, as a product engineer at Stripe, I care deeply about our external users and them being successful with Stripe. That team cares equivalently about engineers at Stripe being successful and being able to build things quickly. And I think that's been even more accelerated by, by AI in the last couple of years.

And then one other observation I want to make, because I think you glossed over it a little bit at the beginning, but it's so important for folks that really want to go ham on coding with AI.

Sure.

Which is, look, all of us engineers have a MacBook Pro that weighs 8 million pounds. That can, can do some damage. Mine, for anybody who wants to know, its nickname is Big Boy. So whenever I need my kids, To get my, my coding laptop, I say, can you bring me big boy? Because it's— I call it San Francisco rucking when I carry 2 of them in my backpack.

Oh my God.

But, you know, no matter how juiced these, these laptops are, you get like 3 or 4 work trees in all running and like it starts to sound like an airplane taking off. It's no good. And so I do think on this sort of like multithreading agentic engineering work, Cloud environments and virtual environments are so important to unlock velocity. And that's one place where I haven't seen enough large engineering teams invest in those environments to really unleash the power of either AI-assisted coding for their software engineers or agents in general. So if there are any CTOs, VPs of engineering listening, If you were to invest in something to really unlock growth in the next year, getting that situation locked up would be really good. Because again, I hear so many people being like, oh, I can Claude code everything. I can spin, you know, I can Codex anything. I can spin up all these worktrees. I'm fine. And I'm like, are you running all these local? Like, what are you, what are you doing? And so that's one thing I just want people to not miss is the limitations of your actual machine on how multithreaded you could be, especially in a complex Codebase like Stripe's.

Totally. And, you know, I have Slack on my phone, right? So I can even kick off one of these minions on the way to work, right? As I'm sort of going through Slack on the subway. And then, you know, by the time I'm there, I, I, I can jump in halfway through. And I think like maybe like the hyperbolic thing here is like, imagine if all engineers at a company could only like work on, didn't have Git. We all had to like coordinate working on the one codebase together. Like, that, that would be crazy. Um, and, you know, the equivalency here is like, uh, imagine if I'm bounded by, you know, my agents are bounded by just what's available and can work on my computer.

Yeah.

Um, if the, the, the, the, the 10x thing to do is, um, you know, be able to have 10 of them run in parallel, but also not be contingent on my, like, it's like everyone's buying a Mac, a Mac Mini, right? So it doesn't fall asleep, right? Uh, it's like, There's a whole business around just the computer not falling asleep.

I legitimately, first of all, I have like 4 Mac Minis upstairs, and one of them is just basically a laptop that doesn't close. Like, I use it as a laptop that does not shut, and it's really unlocked my velocity. So, okay, we thank you for going on this side quest about virtual environments and localhost and all those things. I'm a founder, so I know most people don't start companies because they love running payroll or managing compliance, but somewhere between hiring your first employee and raising your next round, you end up in the weeds with HR, IT, and all that other stuff. That's what Rippling was built to solve. Rippling is a unified platform that lets startups run HR, payroll, IT, and finance in one system from day one. The Rippling Startup Stack replaces disconnected tools that don't sync with a fully connected platform. Over 15,000 startups, including Cursor, Clay, and Sierra trust Rippling to scale fast without adding additional ops and HR headcount, so founders like you can keep building. Right now, venture-backed startups can get 6 months of Rippling Startup Stack for free. Head to rippling.com/howiai and sign up today. That's R-I-P-P-L-I-N-G.com/howiai to sign up for 6 months free today. Focus on what you're building and leave the rest to Rippling. Okay, so you are now running this. You're going to— it's, it's— you said one shot at the beginning. Really, you're trying to take one, one prompt and not a single reply gets you, gets you what you want, but it goes into the harness, it goes through its own loop, hits the tools it needs, and ultimately you as the end user get one response back, which is, here's a successful implementation.

Exactly right. So we can already see that it's identifying the relevant files. It's keeping track of its own to-dos. Like that's something that we've codified in it to focus on. It's making changes. It's, you know, preparing the commit and so on and so forth. And ultimately, it's sort of like taking it out of the oven. We'll see a response at the end of just like, it finished. You know, like you can go ahead and look at the pull request and the sort of normal human review part. continues. So, yeah.

Let's talk about that really quickly. You said 1,300 code or agent-initiated PRs per week, something like that. And then humans are involved in code review. How are you getting all this code review done?

Well, you can make the argument that, you know, if I'm spending less time actively writing code, I can, you know, uh, you know, recenter my time on reviewing the code that's being written or working with users and so on and so forth. So, I, I think that's a big part of it. I think the other side of it, like, it comes back to that CI environment, right? So having really good test coverage, having synthetics that run to simulate end-to-end interactions with your product, those all help inspire confidence in the code you're reviewing, right? So absent those, like, it'd be really difficult to look at code, especially in a huge codebase, and have high confidence that it works. So, you know, again, whether the text has been written by Steve or the text has been written by Steve's robot, You still want that CI environment that's providing confidence that the code that's being changed is safe and that as it rolls out, you're having sort of blue-green deployments so you can roll back to, like, all that is super critical independent of the nature of the authoring of it. I do believe like if coding becomes easier and coding historically has been the bottleneck in product development, it's just gonna shift to other areas, right? So, if like coding in effect becomes free, the review's gonna be really challenging, right? Or getting enough ideas in the first place could be a big problem or distributing them, right? So, I think the attention is just gonna move around to other areas.

Great. And then one other question before we go onto your next workflow, which I am so excited about, spoiler alert, is, Are more than engineers using Minions? Are you seeing product managers, designers come in? How is this going across the company and across functions?

Yeah, I think, you know, part of why I like the Slack example is the entire company's in Slack, right? And, you know, to that point of activation energy, you know, even if like you had the text editor on your computer and I gave you the docs and whatever it may be, you know, to someone who's not an engineer, It can be really challenging or intimidating or, or whatever it may be. And, you know, for whether you just want like a proof of concept or you're gonna make a docs change or, or whatever it may be, like you can, you can probably write out in plain text the, the thing you want to occur, right? You, you might be writing the product brief or you might be, uh, giving design feedback. Like you're, you're in effect just writing a prompt at some point. Um, so being able to just click an emoji or, or, or, you know, tag the, the robot to spin up a minion, we're, we're starting to see, um, more non-engineer usage there. Yeah.

Amazing. Okay, so let's go to our next workflow, which I am psyched. As somebody with a stack of Mac Minis downstairs, I am excited about.

So, you know, at Stripe, we're, you know, we're thinking about AI in a few ways, right? So the demo we just showed is how we're thinking about using AI internally to accelerate our product development and engineering. The second way is, you know, thinking about how we're supporting all these businesses that are leveraging AI in their own products and how we can support their business models. And that's with things like usage-based billing. And we just announced our beta of our LLM token billing product. But there's a third side, which is like this sort of idea of agents as economic actors or agents that can spend money as part of their attempt to solve a prompt. And before we jump into the demo, like just the thing I'll illustrate is like, you know, often you give a prompt to Claude or, or some other agent and it will use its own model to generate text and response, right? Or maybe it will do a web search or call an MCP tool or whatever it may be to gather information or to, to affect change as part of that response. And, you know, of course there's the shopping cases, but we imagine a future where like, Third-party services are going to want to sell into these kinds of experiences, and that those interactions will cost money. So we have to equip our agents with the capacity to spend so that they can not only consume tokens, but so that they can also pay services as part of achieving the prompt. So I'm going to give an example. Jen, who's a product manager I work with, is awesome. I think her birthday is coming up soon. If not, The demos, it's her birthday party, and we're gonna ask Claude to help plan it. And along the way, it's gonna interact with a bunch of different real third-party services that are really gonna accept money over a payment protocol we're calling the Machine Payment Protocol, which we've co-designed with Tempo. And we'll see some real transactions along the way. So I have a, you know, sort of a pre-baked prompt we'll paste in just to skip that part. And I will go ahead and give it. So I told it to research Jen Lee, who's my product manager, figure out what would be a good idea for her birthday, find a place to have the birthday, send invites to the birthday. And then, you know, we've burned all these tokens along the way, so we should probably donate to Stripe Climate at the end to make up for all the energy consumption. So Right now we're just still getting the environment set up, just setting up our ability to pay Tempo. The first thing we're going to do, we can see right here, is that we've actually paid BrowserBase to create a new browser session. So I didn't sign up for BrowserBase beforehand. I'm just paying for this one session. It's going to do that. I gave it her website somewhere up here. So it's going to go ahead and spin up that environment. You can see right now it's writing some Playwright code locally, which will connect to that browser-based session. It got to her website, right? So Jen likes, she, I think she bakes and she cooks. So it actually found out by running that browser session that she's a matcha-obsessed baker working on a cookbook. We're going to go ahead and turn off that browser session and we can see the net cost was just a fraction of a cent. And again, like we like really paid that business just now. The next thing it's going to do is using its knowledge of Jen and her interest in matcha. It's gonna, uh, search online using, uh, parallel AI to find relevant venues in New York that we could host this party. You know, something that matches, matches her matcha interest.

I'm gonna just do again a side quest, a callback to, um, our episode with Andrew Nabil, who, who used AI to set up a, um, tabletop gaming business they were building in the East Bay. And my friend texted me and she said, this is the most San Francisco thing I've ever seen, which is 2 dudes that need AI to help plan their game night. And I was looking up at your original, original prompt and I was like, this is such an engineer's prompt for how to plan a birthday party. It's like source env and then insert Jen's name. Yeah.

You know, you're doing something wrong if I have to load environmental variables to celebrate someone's birthday.

Exactly. It's just like so funny.

Yeah. So I found this matcha cafe in New York on Bowery that's, it thinks is a perfect fit for a matcha interest, which is great. Now we should, you know, send an invite in the mail. You know, we're taking it offline. So now we're interacting with this service called Postalform. Postalform will take a PDF and actually send it in the mail. So again, right now what we're doing is we're, the LM is writing code locally to generate a PDF image of the invite. So there's this sort of interesting balance of like, what can the LM do itself, right? With its own tools in my local machine versus what it needs a third-party, uh, service for. Like obviously the robot can't send mail. And I think if the robot could send mail, that would be, uh, kind of concerning. So, you know, now it's trying to fix a couple things with the PDF. I'm sure the invite looks— it'll be very interesting to see what it looks like.

It looks machine generated.

It'll look— yeah, it's just a bunch of binary. No one's going to come to the party.

How do you— I mean, I know this is a little bit of a demo you're giving us here, but I think so many of these even consumer, you know, facing products like I've never heard of Postalform. It sounds amazing where it solves like a very you know, individual user problem of like, how do I get mail out the door? So many of them are going to be interacting with agents and like the API as, as the interface. And you and I were talking about that a little bit before the show and you were saying you were getting user feedback recently that sort of spoke to that.

Yeah, we've been talking to, you know, I think maybe including Postalform, we've been talking to a lot of users as we've been integrating this machine payment stuff. And, you know, it's very, very normal of Stripe to ask for feedback. And, you know, typically they go, oh, I'll get back to you and write up some notes. And I would get these like in, in 30 seconds, I'd get 2 pages back. And the, the engineer over there had used, you know, Claude or Codex to, you know, read the Stripe docs and implement the feature. And then figured since like they hadn't really written it themselves, that they'd ask Claude or Codex to send feedback back to me. And like it, it, it happened once. I thought, okay, that like, that's, that's funny. And it happened like 4 or 5 times that week, and it was just extremely jarring. And it added this sort of physicality to who the new user is here, right? That like the, we'd have to hear from the agent directly. All right, we're just gonna check in quickly. We sent it in the mail, and then, you know, we burned some tokens along the way. So we actually made a $1.65 donation or contribution to Stripe Climate to erase 4.4 kilograms of carbon. based off of our 70K token usage. And you can kind of see here an agent receipt of the services it interacted with and the cost of each. So at some point I'm going to get an invite to a party in the mail.

I want to just recap this for folks that are not watching. So we started with a prompt in Claude Code that said, plan my friend Jen a birthday party. This is what we know about her. Yeah, it preceded— there was some like movie magic here where it preceded, here are some tools I know can take agent payments. that might be useful in the pursuit of this. And instead of a human having to go into those tools, log in, drop a credit card, buy a plan, there was a machine-to-machine transaction that happened that gave micro access to the, the tool for the capacity the agent needed to do the job at hand.

Yeah.

And we see it use browser-based and parallel and postal form, and it, it issued those those, those payments programmatically, accessed just what it needed, did a little offset Stripe Climate purchase, and then got your party planned. And what I like about this is what's really interesting about this particular example is it makes it very clear the economics of doing something agentically. I like this little, you know, we got a little Stripe Climate shout out here. But it also just calls out like this actually does cost you in tokens whether or not your agent is doing outside transactions. So we're already operating in an economic framework, right?

Yeah, I think I'm on a Stripe plan here, but you, you know, in general, like, you know, people have a subscription relationship to, you know, these providers and, and that costs money and we get a certain number of tokens and any prompt I give, even though I'm not Like seeing the penny count move by has an ultimate dollar cost to it, right? And, you know, maybe in the typical coding example and, you know, consuming tens of thousands, hundreds of millions of tokens, we've sort of justified the value of that, right? Because the code has business value and this has monetary value. But like the sort of like token and the currency that backs it are like They feel closer than ever. And whether I'm spending a penny or a dollar on a third-party service, or I'm spending tens or hundreds of thousands of tokens with the LLM, we're sort of doing a similar activity, right? Which is that we need intelligence or we need data or we need operations or we need a service to execute on that prompt and achieve some outcome. And I think it's like, it even just this view feels very provocative and it, it feels early, but I, I think it's gonna feel very natural over time to see the, the token and, and the dollar side by side. And you know, for me it's like, you know, I planned a birthday party for, I mean, it's probably, I don't know if the, it's any good, but I planned a birthday party for $5.47. That doesn't seem too bad.

Again, we're, we're doing this episode in the year of our Claude, 2026. Like we're gonna show the terminal, the terminal example. And most people watching this, and again, how AI is for everybody, super technical and not, they're gonna look at this and be like, okay, I'm— but yeah, like, I'm not going to plan my birthday party in the terminal. But let's just pull that thread 6 months in the future, 12 months in the future. There's going to be a bunch of builders out there that are going to wrap this in a much more consumer-friendly user, user experience. And then you're going to be able to build such interesting products that can interact and transact in just a much more human way, which again can just solve problems in a different, in a different mindset. Yeah.

And I think it'll be really interesting to build a business where your primary consumer sort of wants an ephemeral interaction with you, and it doesn't necessarily require you having a dashboard or an admin panel or a landing page or, you know, all the, all the other typical things that are really useful, you know, when a human or a business is interacting with you. And instead you could focus on like just a hyper useful single API and monetize that directly and make your, you know, audience primarily agents. I think a lot of just like really interesting businesses can emerge out of that opportunity.

I completely agree. And then we're gonna have agents identify what those businesses are, build them, transact with other agent customers. Agents all the way down. Well, Steve, this is awesome. All the way down. Just to recap for folks, we saw minions and how to kick off development work from Slack and the benefits of investing in developer experience. Again, be a piece of engineering, just like carve off a DevEx team and give it some love and product managers get out of the way. You'll get more product at the end of the day if you just give some time and effort towards developer experience. And then we got to see these machine-to-machine payments, which I think by the time the episode is live, we should be able to maybe talk about or see. So fingers crossed this will be live by the time our episode goes live. And we showed you how to plan a— I just got to zoom in— a matcha cheesecake birthday party in your home.

Jen Lee's matcha party, April 19th, apparently. All things matcha. I guess I didn't pick the date. So the robot has decided that would be a good birthday.

So Saturday, April 19th, 3 to 6. Sounds perfect. We plan a birthday party for $6, carbon neutral. Steve, this is awesome. Before I send you off, couple lightning round questions.

Sure.

One, you know, we showed kind of a, a contrived personal use case, but what are your personal workflows for AI?

The thing I've been really interested in is the sort of like disposability of software. And I mean, I've, I have a, a 4-month-old now and a, a almost 2-and-a-half-year-old now. And the 2-and-a-half-year-old keeps grabbing my phone to like try to change music. So I've, I've toyed around with like music apps that are extremely controlled to just 6 songs. I have no idea how to build iOS apps, but the robot does. So I, I've been toying around with like little, little engagements like that. And then I, I use, you know, the, all the AI apps sort of in the normal way, I, I guess, in addition.

Yeah. Well, if folks want to create an app like that, we just did an episode with Jesse Janae who built a like minimalist YouTube for kids where it can only— like, her kids can only watch the videos that she pre-approves, and you can only swipe back and forth. You can't do any— like, no other buttons. It's very, very streamlined.

Oh, okay.

So very similar to your music example. Okay, and then my last question, which I got a sneak preview of a little up on this Claude example, but when AI's not listening, You know, when your minion does not one-shot, what is your prompting strategy? And you're a parent, so like, do you gentle parent your AI? Are you like, I know you can do it? Or do you, you know, do you, you bribe it? Do you offer it 15 cents carbon neutral? Like, what do you do?

This sounds crazy, but like, I have made a concerted effort to always be polite. And, and I don't, I mean, like, I, you know, I like sci-fi and I like alien stuff, you know, all that. Like, there's this sort of like, who knows if that's going to happen or not. But like, I definitely don't want to be caught being rude, even though like, I think I've read some stuff of like, you know, being more intense or being rude can result in better. It's like, I don't want to— like, I'd rather have to do a little bit extra work than have it on the record that I was mean because you never know. You never know. But the more serious answer is One, asking it to explain or justify itself has helped quite a bit. And then I think in other cases, I've tried, like, in other cases where I know the right direction to go, I will start going in the right direction. And then I will ask it to look at sort of like the git status, to look at the diff, or like look at other sort of like breadcrumbs that I've left. As like the directional thing to help guide it. And then of course, like if I'm doing a thing that's not recurring, but like that I'm gonna do again, I try to keep that in, in some skill or, or, or prompt or otherwise that I can inject back in later.

Got it. So you're doing like the dad teaching his kid to ride a bike move where like your hand's on the back of it and then you let it, let it go. You're like, here.

It really hit me until you said that. There's something really, there's something really weird about raising kids at the exact same time that the The, the, the robot emerges. That hadn't really clicked with me yet. So I don't know what's informing what, but they are happening at the same time.

Yeah, I said something like, it's really interesting to be raising kids and literally writing like SOL.md files into my agents. Like, I guess that's a virtuous cycle of skills. Well, Steve, this has been awesome. Where can we find you and how can we be helpful?

We can learn more about the work we're doing at Stripe at stripe.dev, which is our blog. So you can learn all about, you know, some of the interesting things we're building. The demo I just showed you, you can learn more about at docs.stripe.com/payments/machine. And I guess I'll plug my Twitter, which is just @SteveKolisky. So those 3.

Well, yeah, thanks for joining How I AI. This was awesome.

Awesome. Thank you so much for having me.

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