Your engineers need an AI control plane, not more tools | Guild.ai’s James Everingham

**Andrew** (0:05)
Hello to our listeners, and welcome back to Dev Interrupted. Right now, a lot of engineering leaders are stuck in the same loop. They roll out AI tools, and they see promising demos. But when they go to measure productivity, the numbers get fuzzy, and the organization quietly drifts back into business as usual. Sound familiar? James Everingham, however, has lived the opposite story. After nearly a decade at Meta, including leading Instagram engineering, he was pulled back to help run DevInfra, a thousand-person org responsible for the internal developer experience of 40,000 engineers and technologists. There he discovered that the real gains come from when you stop thinking about AI as an authoring tool and start treating it as sentient fabric across your entire SDLC. And that's what we're going to talk about today, because internally that work became DevMate, an agent platform that went viral inside of Meta and grew to the point where its agents were submitting 50% of all diffs. And now James is taking those lessons to the rest of the world as the CEO of Guild.AI, where he is building enterprise infrastructure for AI agents so that every engineering org, not just Metascale companies, can centralize and orchestrate and safely scale their agent workflows. This is something we've been talking about quite a bit on Dev Interrupted, so we're really excited to dig in. And James, welcome to Dev Interrupted.

**James Everingham** (1:31)
Thank you so much for having me on. I'm excited to be here today.

**Andrew** (1:35)
Great. Well, let's go ahead and jump into talking about that mental transition, the one I kind of opened with here, about moving from the idea of AI being an editor or a helper to being part of the fabric that you work within. You know, when you were leading Dev Infra, you realized pretty quickly that like, you know, autocomplete and just finishing your idea of what to implement is not where the biggest wins are. You went for these larger leverage opportunities within the infrastructure beyond the editor.
What was that realization like and what led you to go there?

**James Everingham** (2:07)
Yeah, sure. I think, you know, when we first started, look, you know, we were on the same path as everyone was trying to accelerate developer productivity using AI. We started with the same tactics that we saw, such as I would say the in-authoring experiences. You see the cursors and the co-pilots out there, which are auto-complete. These have evolved to much more than that, but that's where we all started. We had an advantage there where we were, we owned all of the internal tooling. We owned, we had to build our own editors. We had to build our own source control because the repository was so large. It wasn't that we were an NIH company, it's just that infrastructure is so vast and large, these tools just wouldn't work. So, the advantage of that though was that we got to measure and experiment quickly with a very closed economy of developers. So we could learn pretty quickly. We did learn that like, hey, these tools like Cursor, which we basically came pretty close to feature parity with internally with our internal product called Code Compose. You would get a certain level of productivity more with the junior engineers, and actually surprisingly some of the very senior engineers, but the bulk of the engineers in the middle weren't actually, it wasn't moving anything meaningful. And that's a whole conversation itself is like, what is moving something meaningful actually mean, which we put a lot of thought into as well. So we experimented and eventually, we started trying some different things. And one thing that we did, we had this realization that, hey, AI is a very powerful tool, but if you actually move some of this new agentic behavior closer to your source control system, as part of the infrastructure, you get a lot more impact and you can do a lot more interesting things. Source control is like the canonical source of truth, like your teams, your tools, everything connect to it. There's your code, your history. And if you can intercept some of those tool connections and invoke agents and you could start to make some super interesting things.

**Andrew** (4:19)
You mentioned this opportunity you have that because you owned all these different parts of the stack and you had everything custom built, you get these really amazing opportunities to slice and dice and examine. And that's like a privilege and an ability that like not a lot of orgs have to like the level of granularity that they get. And so I think this oftentimes muddles the process of trying to experiment and measure the impact. Like what would you say to somebody who is working within an organ, doesn't have that level of detail in slicing their whole top to bottom system? Like where should they start to try to zoom in first and look at what they can get their hands on?