Observability is your profit center now | Honeycomb’s Christine Yen

**Andrew** (0:05)
Today, we have a very special guest joining us, Christine Yen, the CEO and co-founder of Honeycomb. Christine has been at the forefront of the observability movement since before it was even really called that. And drawing from her early days building analytics at Facebook and more, she spent her career figuring out how to separate signal from noise, which is the biggest battle that I think we're all fighting right now. How do we understand these deeply complex systems that we're creating? So today we're going to dive deep into the state of observability, because in an era where AI coding and AI agent is shipping code faster than humans are even really reading it and understanding it, we're in an impossible battle and traditional monitoring tools are bursting at the seams. The techniques that we used yesterday might need to be flipped on their head. So we'll explore what this means for developers and the pressing conversations the industry needs to be having right now.
But particularly we're going to explore how the worlds of observability and data science and engineering and product are all colliding into this one big new thing. Christine, welcome to the show.

**Christine Yen** (1:18)
Thank you so much for having me. I'm excited to be here.

**Andrew** (1:21)
We're real excited to have you. Honeycomb is a big, a long time friend of the Dev Interrupted show as well. Your co-founder Charity has been on here many a time.
Even predating me, she's probably been on more episodes than even I, the host, have in some regards. There was a period of time in my early days where that was certainly the case. So it's really great to have you here and get your perspective because I'm a really big fan of all of the thinking and the leadership that's coming out of Honeycomb right now. And I really want to dive into the things that you're seeing and your understanding from your role as a CEO of this company, of really looking at the landscape and how it's been changing kind of like right underneath you. You're not an AI native company. You were talking about understanding technology before all of this transformation happened. So what is highlighted right now for you in this big wild world and how it's getting shaped by agents and how you think observability is fitting into that?

**Christine Yen** (2:21)
I think one of the things that strikes me, you started this off by touching on data science.
And when I think about observability, there's many definitions out there. I just think of it as trying to make sense of what your systems are doing by using data, right? Which is a very broad definition. And when you think about what mechanically that requires, it does sound like a lot of very parallel industries, business intelligence, data science, product analytics. There's all these adjacent things where we're trying to do very similar things. And I've always been surprised, historically, how high and thick the walls between these adjacent disciplines tend to be, how little idea sharing there tends to be. And I'm seeing, and I imagine we could talk a little more about that later if you want, but I'm seeing a version of that happening with talking about agents and agentic development, whether it's, I have Claude helping me write code, or I'm trying to build my customer-facing agents, like people are really, really trying to talk about this as if it's something entirely new and entirely different, and we leave all of our traditional software things over here, and we're doing all of our AI things over there. And maybe it is because we've been doing this since before ChatGPT, but I look at it and I'm like, guys, it's software, like it's weird software, it's non-deterministic software, it's more autonomous software than we're used to. And there's a lot of the shape of how we serve, that software has changed, right? We don't have clean request response cycles anymore, where you have clients talking to servers and no state's shared in between.
The shape has changed, but it is software. And it's interesting to see different organizations sort of working through that idea and figuring out for themselves which ideas to carry over from how they serve software systems, how they think about reliability of that software, how they think about observability, and where they are like, no, truly our practices from the past do not serve us in this new world, and we need to take this sort of step change evolution in our practices.

**Andrew** (4:46)
That's definitely something we've heard a lot from leaders on the show in terms of really what AI does, is it makes more dramatic and more visible the problems in an organization that are deeply maybe like around those rituals and those silos and those things that cause the information to not flow correctly. And so really what it does is it highlights issues that were always there, makes those issues way more stressed and way more strained. And what you see in these teams are they come together and in this like, like, you know, the rubber is hitting the road with AI as fast as possible. They're building a race car, but it's falling apart on the racetrack. Why is that? And they're there for the first time, opening up the hood and fixing that one particular thing that had been like really a huge crutch that the organization had found workarounds for, it wasn't the biggest obstacle of the time, but now it was what was causing the race car to fall apart. So because of that, like, you got like people shining light on things that always should have mattered. Like what you're saying, like, the underlying discipline didn't change. Just the things that we need to carry forward might need to be in a different arrangement. You might need to repack your suitcase, but you're still taking the same thing, you know?