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

Steve Kaliski is a software engineer at Stripe who has spent the past six and a half years building developer tools and payment infrastructure. He’s part of the team that created “minions”—Stripe’s internal AI coding agents, which now ship approximately 1,300 pull requests per week with minimal human intervention beyond code review. In this episode, Steve demonstrates how Stripe engineers activate development work from Slack and leverage cloud-based development environments for parallel agent workflows, and demos machine-to-machine payments where AI agents transact autonomously with third-party services. What you’ll learn: How Stripe’s “minions” write 1,300 pull requests per week with minimal human interventionWhy a good developer experience for humans creates better outcomes for AI agentsThe critical role of cloud development environments in unlocking AI-powered engineering velocityThe machine payment protocol that lets AI agents spend money to accomplish tasksThe code review strategy for handling thousands of agent-written PRsWhy non-engineers at Stripe are starting to use minions to ship codeThe future of software businesses built primarily for agent consumers— Brought to you by: Optimizely https://www.optimizely.com/howIAI—Your AI agent orchestration platform for marketing and digital teams Rippling https://rippling.com/howiai—Stop wasting time on admin tasks, build your startup faster — In this episode, we cover: (00:00) Introduction to Steve (02:39) Stripe’s minions and their effect on Stripe as a whole (04:42) Why activation energy matters more than execution (05:44) What is a minion? The technical architecture (06:52) Demo: Activating a minion from Slack with an emoji (09:04) Why good developer experience benefits both humans and agents (11:22) Walking through the agent loop and system prompts (13:42) Why Stripe chose Goose as their agent harness (16:00) The role of Stripe’s developer productivity team (17:15) Why cloud environments unlock multi-threaded AI engineering (21:14) One-shot prompting: from Slack to shipped PR (22:04) How Stripe handles code review for 1,300 AI-written PRs weekly (23:44) Non-engineers using minions across the company (24:53) Demo: Planning a birthday party with Claude and machine payments (32:15) Quick recap (35:08) The future of ephemeral, API-first businesses for agents (36:36) Lightning round and final thoughts — Detailed workflow walkthroughs from this episode: • How Stripe's AI 'Minions' Ship 1,300 PRs Weekly from a Slack Emoji: https://www.chatprd.ai/how-i-ai/stripes-ai-minions-ship-1300-prs-weekly-from-a-slack-emoji • How to Build an Autonomous AI Agent That Pays for Services to Complete Tasks: https://www.chatprd.ai/how-i-ai/workflows/how-to-build-an-autonomous-ai-agent-that-pays-for-services-to-complete-tasks • How to Automate Code Generation from a Slack Message into a Pull Request: https://www.chatprd.ai/how-i-ai/workflows/how-to-automate-code-generation-from-a-slack-message-into-a-pull-request — Tools referenced: • Goose (AI agent harness): https://github.com/block/goose • Claude Code: https://claude.ai/code • Cursor: https://cursor.sh/ • VS Code: https://code.visualstudio.com/ • Slack: https://slack.com/ • Browserbase: https://browserbase.com/ • Parallel AI: https://www.parallel.ai/ • PostalForm: https://postalform.com/ • Stripe Climate: https://stripe.com/climate — Other references: • Stripe machine payments: https://docs.stripe.com/payments/machine • Blue-Green Deployment: https://martinfowler.com/bliki/BlueGreenDeployment.html • Git worktrees: https://git-scm.com/docs/git-worktree — Where to find Steve Kaliski: Twitter: https://twitter.com/stevekaliski LinkedIn: https://www.linkedin.com/in/steve-kaliski-079a7710/ — Where to find Claire Vo: ChatPRD: https://www.chatprd.ai/ Website: https://clairevo.com/ LinkedIn: https://www.linkedin.com/in/clairevo/ X: https://x.com/clairevo — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  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 GR 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.

• 0:19Claire Vo?

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

• 0:26Speaker 00

  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.

• 0:33Claire Vo?

  How are you getting all this code review done?

• 0:35Speaker 00

  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 of that is super critical, independent of the nature of the authoring of it.

• 0:51Claire Vo?

  No matter how juiced these laptops are, you get three or four 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.

• 1:13Claire Vo?

  Welcome back to How I AI. I'm Clairveaux, product leader and AI obsessive here on a mission to help you build better with these new tools. Today, we have Steve Kaliski, 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,

• 1:58Claire Vo?

  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 busy work, 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/HowIAI. Attend live and you'll get a free pair of Ray-Ban Meta AI Glasses.

• 2:40Claire Vo?

  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.

• 3:11Speaker 00

  Thank you for having me.

• 3:13Claire Vo?

  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?

• 3:21Speaker 00

  Sure. So, for me personally, I think sort of anecdotally, I don't remember the last time I started work in the text editor. So, I do end up there often. But what I found is that 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 GR 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. And then you end up in a text editor when it's time to actually do the work or make the final tweak. And it's just felt very natural. And I think in particular, the sort of like activation energy of starting work feels a lot lower. So, if you're in a Slack thread and maybe there's a piece of user feedback and it's something simple

• 4:05Speaker 00

  like 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. 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 participating. And then I can jump in and I can tweak and I can kind of like have that momentum, sort of like generative momentum that I can hop in halfway through.

• 4:43Claire Vo?

  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.

• 5:08Speaker 00

  Yeah.

• 5:08Claire Vo?

  It's either 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 cost can go down, execution cost can go down, communication cost 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.

• 5:53Speaker 00

  The quick spiel of a minion. When I, as an engineer, sort of in pre-AI time, want to make a modification to Stripe. Well, Stripe is a huge code base with tons of services. It can't run on my computer alone. So Stripe already has a long history of investing in great developer tooling, having hosted development environments that I can spin up, that 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. We evolved that. The idea with the minion is that I can provision one of those environments seeded with a prompt.

• 6:32Speaker 00

  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 test data, so on and so forth. And it will loop through that and attempt to solve that prompt. 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 Klosky Robots Dash Claire. I actually have a Steve Klosky Robots channel that has 76 humans in it, but I do have every... it sort of is just me and my robots, and now there's some sort of, you know, like, audience observing.

• 7:13Speaker 00

  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 at stripe.com/payments/machine. This is our new machine-to-machine payment work, which we'll look at later in our call. And I want to 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.

• 7:56Speaker 00

  All I have to do now is, you know, add a reaction, which is "Create minion Pay Server." This is a particular repository within Stripe. We get the one-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 it's created, landing page code example has been created." And it's going to kick off our doc 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 is not new. And basically, it's going to spin up a instance in the cloud.

• 8:37Speaker 00

  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 VS Code Server so I could connect to it just through the web or locally. It's got some extensions. So what's really 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 a ton of incredible work that are developer activities done around just making it easy to get like a perfectly operating Stripe development environment

• 9:22Speaker 00

  for coding, which means that, you know, not only can I have one of these, but I could have, you know, many, many of these running in parallel in isolated, you know, environments, making isolated changes all at the same time. So that little one-click emoji, I could have done that with a few messages at the same time, which is really great.

• 9:39Claire Vo?

  Yeah, one thing I want to call it here is we had my friend Zach from Launch Darkly 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 always ask, 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.

• 10:23Speaker 00

  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 they just say, good luck. 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 to understand all the intricacies would be like impossible or extremely expensive. So, you know, if there's a very blessed path for 90% of the common activities in Being a engineer at Stripe, that makes the propensity that the agent succeeds really high, too. So imagine we wanted to make an API change, which we do hundreds or thousands

• 11:08Speaker 00

  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 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 going to run through all this.

• 11:40Claire Vo?

  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." And then just whatever you put in.

• 12:01Speaker 00

  Yeah, just no mistakes.

• 12:03Claire Vo?

  No mistakes. You forgot, no mistakes. But 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 a successful outcome from a pretty loose prompt.

• 12:17Speaker 00

  Totally. And a lot of this is an experiment in some way, right? As new models come out, and we build new tools, like there is this sort of dynamic nature to it. And we've built a lot of interesting bots that help write the prompt. So maybe first it will do the task of searching through the codebase, or looking at other pull requests, or Google Docs, or whatever it may be. I think now at 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 could assign to the agent. So that's sort of, part of why I wanted that public channel we were looking at is like, we're kind of see if that we don't pair program anymore, but we pair prompt. And that activity could be with

• 13:02Speaker 00

  other engineers, or other data sources, or other agents, too. 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 me and my fellow colleagues can review.

• 13:42Claire Vo?

  Yeah, I have a couple of questions on this because we've seen a few examples of folks building their own cloud agents. And I'm curious, why Goose 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 minions on top of your development tools.

• 14:12Speaker 00

  Yeah, sure. So we also make Claude Code and Purser and tools like that widely available to engineers at Stripe. So 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.

• 14:51Speaker 00

  And really what we were looking is like sort of a base harness and loop to apply all of our own tools and software to. So we spent a lot, we spent a lot of time on like making good tools available and making sure that the sort of routes that minions go through work closely with like the most common Stripe developer workloads. So it's You know, the sort of like commercial versus custom things like there are things that are very specific about Stripe's codebase and being a developer of 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 this demo, I can sort of show like I can, for example, I can pop into

• 15:34Speaker 00

  the S 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 no singular tool to rule them all, but I think the like overall end to end development story at Stripe is still time minions. So you can see them in that dev box in Claude now.

• 15:59Claire Vo?

  Yeah. Cool. I have one other question and then, and then an observation I want to make sure 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 minions and managing that as an internal product itself. Has that team been sort of built as a standalone team that's focused specifically on internal developer experience? Is that how it works?

• 16:29Speaker 00

  Yeah, we've had a developer productivity team for as long as I can remember. I think about six and a half years now. And, you know, that team's focused on all the tools that I engage with 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 tech 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 in Stripe, I care deeply about our external users and them being successful at 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 AI in the last couple of years.

• 17:15Claire Vo?

  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.

• 17:23Speaker 00

  Sure.

• 17:24Claire Vo?

  Which is, look, all of us engineers have a MacBook Pro that weighs eight million pounds.

• 17:30Speaker 00

  Sure.

• 17:30Claire Vo?

  That 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 coding laptop, I say, can you bring me Big Boy? Because I call it San Francisco rucking when I carry two of them in my backpack.

• 17:44Speaker 00

  Oh, my God.

• 17:45Claire Vo?

  But, you know, no matter how juiced these laptops are, you get like three or four 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 multi-threading 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,

• 18:31Claire Vo?

  getting that situation locked up would be really good. Because again, I hear so many people be like, oh, I can Claude code everything. I can spin, you know, I can codex anything. I can spin up all these work trees. 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 multi-threaded you could be, especially in a complex codebase like Stripe.

• 18:58Speaker 00

  Totally. 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 can jump in halfway through. I think 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. That would be crazy. And, you know, the equivalence here is like, imagine if I'm bounded by, you know, my agents are bounded by just what's available and can work on my computer. If the 10x thing to do is

• 19:38Speaker 00

  You know, be able to have 10 of them run in parallel, but also not be contingent to my like, it's like everyone's playing a Mac, a Mac mini, right? So it doesn't fall asleep. Right? It's like, there's a, there's a whole business around just the computer not falling asleep.

• 19:51Claire Vo?

  I legitimately, first of all, I have like four 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, my velocity. So, okay, we, thank you for going on this side quest about virtual environments and local hosts 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

• 20:33Claire Vo?

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• 21:15Claire Vo?

  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 the successful implementation.

• 21:39Speaker 00

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

• 22:06Claire Vo?

  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?

• 22:18Speaker 00

  Well, you could make the argument that if I'm spending less time actively writing code, I can re-center my time on reviewing the code that's being written or working with users and so on and so forth. So I think that's a big part of it. I think the other side of it, like it comes back to that CI environment. 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. 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 again, whether the text has been written by Steve or the text has been written by Steve's robot,

• 23:01Speaker 00

  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 going to shift to other areas. So if like coding in effect becomes free, The review is going to 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 going to move around to other areas.

• 23:45Claire Vo?

  Great. And then one other question before we go on to 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?

• 24:00Speaker 00

  Yeah, I think part of why I like the Slack example is the entire company is in Slack. Right? And to that point of activation energy, even if you had the text editor on your computer and I gave you the docs and whatever it may be to someone who's not an engineer, it can be really challenging or intimidating or whatever it may be. And whether you just want like a proof of concept or you're going to make a docs change or whatever it may be, like you can probably write out in plain text the thing you want to occur. Right? You might be writing the product brief or you might be giving design feedback. Like you're in effect just writing a prompt at some point. So being able to just click an emoji

• 24:43Speaker 00

  or tag the robot to send a minion. We're trying to see more non-engineer usage there. Yeah.

• 24:51Claire Vo?

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

• 25:02Speaker 00

  So at Stripe, 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 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 L M 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 to the demo, just the thing I'll illustrate is like.

• 25:47Speaker 00

  You know, often you give a prompt to Claude 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 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 a, an example.

• 26:31Speaker 00

  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 going to ask Claude to help plan it. And along the way, it's going to interact with a bunch of different, real third-party services that are really going to accept money over a payment protocol. We're calling them machine payment protocol, which we've co-designed with Tempo. And we'll see some real transactions along the way. So I have a sort of 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

• 27:12Speaker 00

  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 still getting the environment set up, just setting up our ability to pay Tempo. The first thing we're going to do, and 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

• 27:57Speaker 00

  to that BrowserBase session. It got to her website, right? So Jen likes, 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. We can see the net cost is just a fraction of a cent. And again, we 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 going to search online using parallel AI to find relevant venues in New York that we could host this party, you know, something that matches, matches her matcha interest.

• 28:38Claire Vo?

  I'm going to just do again, a side quest, a call back to our episode with Andrew Nabeel, who, who used AI to set up a 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 two 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 M and then insert Jen's name.

• 29:15Speaker 00

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

• 29:21Claire Vo?

  Exactly. It's just like so funny.

• 29:23Speaker 00

  Yeah, so I found this matcha cafe in New York on Bowery. That's, I think is a perfect fit for a matcha interest, which is great. Now we should, you know, send an invite in the mail. We're taking it offline. So now we're interacting with this service called Postal Form. Postal Form 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 service for. Obviously, the robot can't send mail and I think if the robot could send a mail, that

• 30:07Speaker 00

  would be kind of concerning. So now it's trying to fix a couple of things with the PDF. I'm sure the invite looks, it'll be very interesting to see what the invite looks like.

• 30:17Claire Vo?

  It looks machine-generated?

• 30:18Speaker 00

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

• 30:22Claire Vo?

  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 postal form, 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.

• 30:58Speaker 00

  Yeah, we've been talking to, you know, I think including postal form, 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 for 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 two pages back. And the engineer over there had used 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 happened once, I thought, okay, like that's, that's funny. And it happened like four or five times that week and it was just extremely jarring. And it added the sort of physicality to who the new user is here, right?

• 31:43Speaker 00

  That like the, we'd have to hear from the agent directly. All right, we're just going to check in quickly. We, we sent it in the mail and then, you know, we, we burned, we burned some tokens along the way. So we actually made a dollar and 65 cent donation or a contribution to Stripe Climate to erase 4.4 kilograms of carbon based off of our 70 K 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 gonna get an invite to a party in the mail.

• 32:16Claire Vo?

  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."

• 32:27Speaker 00

  Yep.

• 32:27Claire Vo?

  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, login, drop a credit card, buy a plan, there was a machine-to-machine transaction that happened that gave micro-access to the tool for the capacity the agent needed to do the job at hand. And we see it use browser base and parallel and postal form. And it issued those payments programmatically, accessed just what it needed, did a little offset Stripe climate purchase, and then got your

• 33:10Claire Vo?

  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?

• 33:40Speaker 00

  Yeah, I think I'm on a Stripe plan here, but you know, in general, like, you know, people have a subscription relationship to these providers 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 a 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 like they feel closer than

• 34:22Speaker 00

  ever. And you know, whether I'm spending a penny or a dollar on a third-party service, or I'm spending tens or hundred thousands of tokens with LM, 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, even just this view feels very provocative, and it feels early, but I think it's going to feel very natural over time to see the token and the dollar side by side. And you know, for me, it's like, you know, I planned a birthday party for, I mean, I don't know if it's any good, but I planned a birthday party for $5.47. That doesn't seem too bad.

• 35:07Claire Vo?

  Again, we're doing this episode in the year of our Claude 2026. Like, we're going to show 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, but yeah, like, I'm not going to plan my birthday party in the terminal. But let's just pull that thread, six 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 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 mindset.

• 35:48Speaker 00

  Yeah, I think it'd 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 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.

• 36:28Claire Vo?

  I completely agree. And then we're going to have agents identify what those businesses are, build them, transact with other agent customers, agents all the way down. Well, Steve-

• 36:37Speaker 00

  All the way down.

• 36:38Claire Vo?

  Awesome. 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, VPs of engineering, just like carve off a DevX 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 guess, got to zoom in, a matcha cheesecake birthday party in New York.

• 37:21Speaker 00

  Jen Lee's Matcha Party, April 19th, apparently.

• 37:24Claire Vo?

  All things matcha.

• 37:25Speaker 00

  I guess I didn't pick the date. So the robot has decided that will be a good birthday.

• 37:29Claire Vo?

  Saturday, April 19th, 3 to 6 p.m. Sounds perfect. We plan a birthday party for $6 carbon neutral. This is awesome. Before I send you off, couple lightning round questions. One, you know, we showed kind of a contrived personal use case, but what are your personal workflows for AI?

• 37:48Speaker 00

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

• 38:18Claire Vo?

  Yeah, well, if folks want to create an app like that, we just did an episode with Jesse Janey, 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. So very similar to your music example. Okay. And then my last question, which had a sneak preview of a little up on this Claude example, but When AI is 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 bribe it?

• 39:02Claire Vo?

  Do you offer it 15 cent carbon neutral? Like, what do you do?

• 39:06Speaker 00

  This sounds crazy, but like, I have made a concerted effort to always be polite. And

• 39:13Claire Vo?

  Same.

• 39:15Speaker 00

  And I don't, I mean, like, I like sci-fi, I like alien stuff, all like, there's this sort of like, who knows if that's gonna 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 wanna, like, I'd rather have to do a little bit of extra work than have it on the record that I was mean. Because you never know.

• 39:39Claire Vo?

  You never know.

• 39:40Speaker 00

  But the more serious answer is,

• 39:44Speaker 00

  one, asking it to explain or justify itself has helped quite a bit. And then I think in other cases, I've, I've tried, like, in other cases where I know the right direction to go, I will, I will start going in the right direction and then I will ask it to look at, sort of like, the get 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 that I'm gonna do again, I try to keep that in, in some skill or prompter, otherwise that I can inject back in later.

• 40:23Claire Vo?

  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, you're like,

• 40:31Speaker 00

  It didn't really hit me until you said that, but 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.

• 40:47Claire Vo?

  Yeah, I said something like, it's really interesting to be raising kids and literally writing like soul.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?

• 41:01Speaker 00

  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 some 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 @SteveKaliski. So those three.

• 41:21Claire Vo?

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

• 41:24Speaker 00

  Awesome. Thank you so much for having me.

• 41:27Claire Vo?

  Thanks so much for watching. If you enjoyed this show, please like and subscribe here on YouTube or even better, leave us a comment with your thoughts. You can also find this podcast on Apple Podcasts, Spotify, or your favorite podcast app. Please consider leaving us a rating and review, which will help others find the show. You can see all our episodes and learn more about the show at howiaipod.com. See you next time.