GitHub のエージェント計画に関する Kyle Daigle とのインタビュー

I’m excited to work with Microsoft once again as the presenting sponsors of the AI Engineer World’s Fair! We’ll streaming live from MS Build today for a special crossover pod with our friends at No Priors and the one and only Satya Nadella. However we did not hold back with this interview - we asked all the burning questions about uptime and Copilot that we know you have in your minds. Lets go!

For almost two decades, GitHub has been the home of software, where both open source and closed flow, through commits, pull requests, reviews, actions, etc.

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This ecosystem flourished as open-source maintainers and contributors would continue shipping code for the benefit of the community. However as coding agents began to ship mass quantities of code - growing 1400% in 2026, it marked a new era that was both extremely exciting and challenging for GitHub.

While these agents help more people ship more projects, they also significantly increase the floor of how much code is shipped, how often it is shipped, how many people commit code, and basically orders of magnitude multiples in every dimension of GitHub infrastructure:

our valuemule pod

Now GitHub inevitably experiences more pressure on their infrastructure which was originally designed around human developers moving at human speed. This has resulted in a very publicly notable uptime story:

So it begs the question of whether current systems around code can absorb what AI produces. Can CI/CD keep up when every idea becomes a build? Can open source maintainers survive floods of AI-generated slop contributions? Can GitHub preserve the human social contract of software while becoming the operating layer for agents?

Which brings us to the perfect person to answer these questions: GitHub COO Kyle Daigle. In this episode, he joins swyx to unpack what happens when AI doesn’t just autocomplete code, but starts changing how companies operate, how open source works, how pull requests get reviewed, and how GitHub itself has to scale.

We go deep on GitHub’s internal AI workflows: micro-skills, WorkIQ, MCP, Slack, Teams, email, Copilot workflows, the new Copilot desktop app, CLI, cloud agents, and how Kyle uses agents to look backwards across company context before deciding what to do next. Kyle also reflects on GitHub’s history building webhooks, APIs, Actions, npm, Dependabot, and Semmle, why the AI era is breaking GitHub in new ways, how Actions became a general-purpose compute layer, and what Copilot becomes after code completion.

Full Video Pod

We discuss:

Kyle’s expanded role across GitHub

How AI got Kyle coding again after years in leadership

Why GitHub rolls out AI through existing workflows instead of forcing new tools

WorkIQ, MCP, Slack, Teams, email, and GitHub as company context

Why massive “mega-skills” are giving way to small, atomic micro-skills

How AI changes summarization, communications, marketing, and analyst work

Why former developers in leadership may have a unique advantage in the AI era

Kyle’s “15 agents on Saturday” workflow

How Kyle built an AI-generated executive presentation for CRO/CFO teams

Why AI changes the chief of staff role without removing the human work

GitHub Actions, webhooks, arbitrary code execution, and secure agent compute

The npm acquisition, supply-chain security, 2FA, and token invalidation

Slop forks, vendoring, and whether AI agents change dependency management

What pull requests become when most PRs come from agents

Prompt requests, vouching, AI review, and trust in open source

What counts as a “developer” when AI lowers the barrier to building

GitHub Spark, low-code, and why GitHub refuses to hide the code

14x commit growth, Actions load, databases, monorepos, and availability

Copilot’s evolution from completion to CLI, desktop app, cloud agents, and SDK

Context, memory, rules, and making GitHub “act like Kyle wants it to act”

Ambient AI, OpenClaw, enterprise security, and the new operating system for agents

What swyx should ask Satya Nadella about Microsoft’s AI future

Kyle Daigle

LinkedIn: https://www.linkedin.com/in/kyledaigle

X: https://x.com/kdaigle

Timestamps

00:00:00 Introduction

00:03:36 Why AI Got Kyle Coding Again

00:07:04 Running GitHub with AI: WorkIQ, MCP, Slack, Teams, and Skills

00:15:39 The Golden Age for Former Developers in Leadership

00:17:31 15 Agents on Saturday and AI-Generated Executive Work

00:20:20 How AI Changes the Chief of Staff Role

00:21:45 GitHub’s History: Actions, npm, Webhooks, and Open Source

00:28:45 Slop Forks, Vendoring, and AI Dependency Management

00:33:57 Pull Requests, Prompt Requests, and Trust in Agent-Generated Code

00:41:21 GitHub Stars, 200M+ Developers, and the New AI Builder Wave

00:45:15 GitHub Spark, Low-Code, and Why GitHub Still Shows the Code

00:47:38 GitHub’s Hardest Era: 14x Growth, Reliability, and Scale

00:59:21 Actions as the Compute Layer for CI/CD and Automation

01:02:04 The State and Future of GitHub Copilot

01:08:24 Ambient AI, Background Agents, and the Future of the SDLC

01:13:09 OpenClaw, Enterprise Security, and the New OS for Agents

01:18:03 Build Announcements, WorkIQ, FoundryIQ, and Microsoft Context

01:21:41 What Should swyx Ask Satya?

Transcript

Introduction: Kyle Daigle’s Expanded Role at GitHub and Microsoft

Swyx [00:00:00]: We’re here with Kyle Daigle, COO of GitHub. Welcome.

Kyle [00:00:07]: Hey, thanks for having me.

Swyx [00:00:08]: You’re not just CEO of GitHub. People know you as that. You have a new role.

Kyle [00:00:11]: So I have an expanded role now. I’ve been working at GitHub for thirteen years and doing all things developer. Joined as a developer myself. And now, I’m also responsible as the CMO of Developer for Microsoft. And so all the kind of learnings and passion for developers and how we work with them and how we communicate and how we bring our products to market, we’re also bringing that expertise to the broader Microsoft ecosystem and helping every developer that uses a Microsoft product or would like to have a sort of similar experience that they’ve had with GitHub over the years. So it’s a different role in some ways, but it’s also just building on the experience that I’ve had at GitHub of just sort of tell the truth, be authentic, show people how to use it and then let the products speak for themselves. Now just doing that with, all of Microsoft.

Swyx [00:01:09]: We’ll be releasing this in conjunction with Build. You got lots of stuff planned, and we can sort of touch on that whenever it’s appropriate. I think one of the interesting things is I rarely meet a COO who’s also a CMO. I think you’re a very outward facing and you’re very confident publicly. That’s rare. Do you actually view yourself as COO? What’s What is your thing?

From GitHub Developer to COO/CMO: Building the Platform and Operating GitHub

Kyle [00:01:33]: I think for me, it’s been funny. The titles have always been, a— have always felt a little strange to me. I joined GitHub as a developer? I wrote so much of the

Swyx [00:01:46]: Let’s bring that up. You wrote the back ends?

Kyle [00:01:48]: I was going through, I was going through, some old photos, when folks were talking about how things were being built or how there was a build GitHub. I built, webhooks and worked with teams building the API, built the platform layer. Anything that integrated with GitHub, up until really twenty eighteen, I built or ran the engineering teams. And that’s kind of where my the beginning of my passion always was helping people build things, deliver them to, their customers. And so being a developer, building for developers was always super unique. In a— I think as my role expanded, it became my ability to talk to not just developers, but also enterprise customers or business leaders and have this translation layer. And then through all those years, GitHub has always operated pretty uniquely. Post-pandemic, working remotely was not as novel as it was when GitHub started in two thousand and eight. But all that expertise of running remote teams, doing it well, became this sort of bigger role, ultimately turning into the COO role of how do we operate GitHub in the way that GitHub’s always operated after the Microsoft acquisition. And kind of so on from there. So like for me, I think the— I’ve, I still code. I love coding but the problem has always been, people. It’s a much harder problem to both support our own employees, a harder problem to communicate to developers and enterprise buyers what we’re building why it matters, ‘cause those are two very different messages. And so getting to work in the mix of COO, CMO, also just being a dev, I think is what’s kept me at GitHub for so long.

AI Workflows for Leadership: Commits, Retrospectives, and Context

Swyx [00:03:40]: Apparently, you have— your commits have gone up. What’s this? What’s going on?

Kyle [00:03:45]: Rui’s called me out pretty aggressively. So I think— as you can imagine, right, you can see my normal era of being a dev In the twenty thirteen, twenty fourteen era, and then moving into management, and then ultimately the COO role. I think what you see there is me, really getting back to coding thanks to AI. I— similar to, attaching problems between how to market and how to operate a business and how to code, I find, building agents and workflows that are connecting very disparate problems to be what’s driving this. So that’s, some of it’s writing software. A lot of it is, connecting a ton of a different data sources to, help me out. But that is completely me really diving in on the AI side in trying out our tools, trying out everyone’s tools, But building for me, building for the non-technical leader, though I’m technical and how we’re, able to use these tools more than just the simple, call and response that I think a lot of the non-technical, your employers, you have to get— you have to use AI, and so everyone uses, ChatGPT or Copilot or Claude or whatever. To really get into, how is this going to help me out, it— I find that it’s not the I need to write a blog post, I need to those simple examples. Helping people find the workflows of, “Okay, I need you to go through all the PRs today. I need you to go through everything that we’ve posted online. I need you to go through what we did the last three months. Go through all of my Obsidian notes for any mentions of this then go through my transcripts at work.” We use, Teams, so, using WorkIQ, go call that MCP server, grab all the transcripts, go through all the Slack, and then build me out the plan of, what this week’s messaging actually was. That’s something that was, impossible because for me, I find AI in a what most of this launch here is actually, less building forward. It’s actually, a recursive loop backwards. I’m always looking at what had happened first. Go back through the week and tell me what we did, what worked, what didn’t work? And then tell me in the next three or four days-What would you tweak based on this sort of like looking backwards and then looking ahead a little bit? I find that to be so much more valuable, especially for like non-technical, because that retrospection is actually LLMs are very good at that. Like finding all the patterns, pulling them out, and then applying that retrospection to just a couple of days or just like a short period of time. Is all a bunch of apps that I’ve built and launched a bunch of, internal tools. I use the new, GitHub Copilot app, the desktop app with workflows. Every time I crack open my laptop, it’s running workflows for me. It’s just a ton of different stuff and of course, it all ends up on, it all ends up on GitHub.

Swyx [00:06:47]: Of course. That’s where, that’s where, stuff is hosted. Man, there’s so much to ask you. I was going to leave the how do you run a company with AI thing at the end. I have to ask one— double click one thing. You said, you are looking back at the week. You’re, you’re understanding what happens. When you say we That’s three thousand people. How?

Rolling Out AI Internally: Skills, CLIs, and Company Context

Kyle [00:07:09]: I think when we started rolling out AI internally beyond engineering, right? One of the things that I was really, passionate about is like we have to do this in a way where no one has to change how they work. I don’t want to have to teach you a tool. I don’t want to have to teach you something new. And so for us, we tried out a few tools. Most of them don’t work because I got to get you on board? I got to teach you how to use it. What we’ve actually ended up doing is we’ve built like a set of skills internally. We have we each have our set of skills, and we’ve just been distributing even to the non-technical folks, the CLI. And then effectively, we’re just giving it access to like read about everything that we’re writing. So that’s for us, that’s usually GitHub, Teams, Email, and Slack. So Teams for, video chat, generally speaking.

Swyx [00:08:03]: Teams and Slack?

Kyle [00:08:04]: so we use Teams for video communication, but we don’t use it for chat. W-we— GitHub for a long history, right? We’re always

Swyx [00:08:13]: Also Slack

Kyle [00:08:14]: Talking about ChatOps and like everything is built into Slack. Like every command, every flow.

Swyx [00:08:18]: So even though you have been acquired for I don’t know, eight years now

Kyle [00:08:22]: we still

Swyx [00:08:23]: You still use Slack?

Kyle [00:08:23]: it’s a purpose-built tool for us, and I think the reality is that moving off of it would be so bluntly expensive? Simply because all the tooling is, baked in with that paradigm. And they both have their pros and cons but they don’t work the same way at all. We still use a bunch of different tools Because it’s the purpose-built tools that We need. And then

Swyx [00:08:47]: Well, the same doesn’t go for the rest of Microsoft, presumably.

Kyle [00:08:50]: like the like various teams like operate

Swyx [00:08:53]: They make their own decisions

Kyle [00:08:54]: Various ways. I think it just matters what you’re trying to what you’re trying to do. But we do we do work across kind of every tool that we use, and then by giving everyone access to all of that context and the new WorkIQ MCP server, which is quite cool if you do live in the M365 like world. I can ask it all these backwards-facing questions, and it’s incredibly important for our teams that are working remotely. There’s a lot of stuff you miss when you’re not in an office, and we are spread out all over the world. So most of that is looking back. And then we post, we post either auto-automatically into GitHub issues or discussions, these sorts of like findings or like our industry reports. Like what’s happening this morning, today, yesterday. A little automation gets run. We’ll use the app. We might use GitHub Actions like with, our agentic workflows just to go do that run, and then we push it into GitHub, and w-we keep having a conversation. So usually for us, it’s about that sort of like looking back, looking forward on the non-technical side. And then of course for a lot of those folks, it’s also building an app, pushing it to GitHub pages or pushing it somewhere to host it et cetera. But it’s just like enabling everyone with that power of it’s going to take me a week to figure this out. Instead, we’re going “Okay I built a skill. Let’s put it into a repo. We’ll all share that skill together, and then we’ll use the CLI or now the app-” “just to run it.”

Micro Skills vs. Mega Skills: How GitHub Uses AI at Work

Swyx [00:10:26]: All right. I think, I think we’re going straight into like the team management and productivity thing. I think a lot of people are getting various levels of LLM psychosis. How do you manage the bloat of skills? Like everyone Has their thing, and they’re Like trying to promote it to the rest of their peers in their org, right? And obviously, whoever becomes a skill influencer internally becomes like an AI leader, right? Of sorts. I assume you have those.

Kyle [00:10:50]: like I think we have

Swyx [00:10:52]: And I assume it’s a mess a Yeah.

Kyle [00:10:54]: there’s like I— like I think the reality is there’s two pieces. Like first is I think that we’re ending the era of these like massive, beautiful, perfect skills that are just like not any of those things. ‘cause for a while, right every tweet every day is like go download the skills, the perfectly managed thing to do this entire workflow. And I think that like what we’ve found and what— I was just with my team, this week, and we were talking about the skill side, and we’re really talking about these like incredibly micro skills that are just doing one thing for us very well Versus a skill that’s going to do I said, that full report. That doesn’t really exist on our side anymore. It’s usually how do— like a single skill that’s going to identify the most important marketing information given any MCP server. Like this is the most important thing. Less about stitch a bunch of tools together and have it produce this mega output because then weeks go by, months go by, things change, and you want to tweak

Swyx [00:11:58]: It’s brittle

Kyle [00:11:58]: Your mega skill and you’re screwed? You can’t do that. And so now we’re really just talking about the Legos we’re using and just letting the instruction book be something we’re all putting together. Whereas I think a lot of AI skills for a while have been that mega instruction book style.

Swyx [00:12:15]: I’ve, thought a lot about Postel’s law. I don’t know if that’s a term that is, means things to folks. It’s the idea that you should be liberal in what you accept and strict in what you output, right? And I think that’s like a good framing principle for skills. This is my skills, obviously on GitHub. I feel like everyone should have like how like some repos In GitHub are special repos? I feel like we should sort of reify the slash skills and everyone like give it some kind of special presentation. Anyway, so, yeah, this is one of those like download Download anything, transcribe anything, and then you can string together the atomic skills that do one thing well Into like some kind of orchestration skill that calls other skills. I assume, does that match?

Kyle [00:12:56]: I like I think so. I think that the

Swyx [00:13:00]: Summarize anything.

Kyle [00:13:01]: Like I think the- For me, summarizing something for I do communications and PR and analyst relations and marketing and customer activities, and so my summarize everything is very different for each one of those like Contexts. What ‘Cause if I’m summarizing something for an analyst, that’s a very different thing than, probably how I’m going to summarize something for like a customer meeting or an engagement. So that’s I think like the difference when we’re talking about the like the tools I might use on Saturday or the skills I might use on a Saturday when it’s just for Kyle. Yeah, those are kind of like they have an atomic actual tool underneath or maybe skill, and then Kyle cares about X. But I think when we’re talking about work and enabling the the marketers, communicators there, it’s the atomic, this is what good summarization is, and then this is