Airbnb Is Building a ‘Company Brain’ for AI Agents with Ahmad Al-Dahle

Sam Charrington (0:00)

Your company knows a lot more than what's written down. Experienced employees know why a system works a certain way, which decisions apply, and how to ask when the documentation falls short. Giving AI agents access to that knowledge is a much harder problem than just connecting them to a code base. Ahmad Al-Dahle helped build the original iPhone at Apple and led the team behind Llama at Meta. Now, as Airbnb's CTO, he's tackling that problem by building what he calls a company brain. The system, Everest, captures institutional knowledge from employees' workflows and makes it available to agents. But as that shared knowledge grows, how do you keep agents from learning the wrong lessons?

Ahmad Al-Dahle (0:41)

I think if you aren't careful, markdown files could be exactly the kind of problem that I was referring to where you have like, it's difficult to find and there's a lot of like context poisoning that's happening. One person uses it, we extract a thing, we put it down a markdown file, and then the model's like very good at finding the thing that's old or wrong. Because the biggest mistake you can make is you reveal too much context too quickly to the model, versus you try to give it enough context to move one step forward to then ask for more context.

Sam Charrington (1:10)

I'm Sam Charrington, and this is The TWIML AI Podcast. For over a decade, I've been exploring the ideas and innovations shaping the future of AI through conversations like this one that help you understand what's real, what's next, and what matters. Let's jump in.

When you arrived at Airbnb, talk a little bit about the AI landscape that you encountered and how you've seen that evolve since you joined.

Ahmad Al-Dahle (1:47)

The good news is there was a lot of momentum already. There was a pretty decent foundation, I would say. And I think it's both the timing that I joined, because really, agentic coding had just begun to become something that people could really leverage. So that the moment and timing of the capability was right. But also, I think the Airbnb culture, and Airbnb has had a long lineage of working on AI, and ML Ops, and all sorts of things. So it had a strong infrastructure investment. So what I've been able to help the company do is to really accelerate the adoption of AI across maybe three frontiers or three boundaries. The first one is AI for productivity. How does the average engineer use AI to move faster? That turns out to be you have to engineer your platform to make it easy to facilitate AI agents in your infrastructure. And then the second one is how do we deploy AI agents to the marketplace? These are things that maybe the user doesn't see, that really add a lot of accretive value to our platform. It either builds more trust, more reliability, quality, et cetera. These are things that you do in an offline fashion.

And then how do we deploy AI agents to improve both the guest experience or the host experience? So these are kind of like the three frontiers we pushed on.

And each one's made like a tremendous amount of progress in a really short amount of time.

Sam Charrington (3:19)

When you think about that first pillar in AI for engineering productivity, you mentioned you kind of came on just as agentic coding was taking place.

When you think about how to help engineers be productive today with AI coding tools, there are lots of different ways that you could come about that. I see lots of different philosophies about AI coding in the marketplace today. I'm not sure we've fully nailed this is the right way to do it or the one way to do it. How do you describe the Airbnb way to do it? Or even is there an Airbnb way to do it or an Ahmad way to do it, or is it team by team?

Ahmad Al-Dahle (4:00)

Well, every company has a culture set of principles that we've been here to.

So some companies will say like move fast. That's the mantra. So what you'll find is the way that AI is being deployed for activities really about velocity. I think Airbnb really focuses on craft, which I think is actually really, really good. It's aligned to how I think about things. And so because we're very focused on craft, it's not just a velocity or speed question. Velocity actually has a heading, which is good. It's really also about quality. So we have to balance the equities of both. And so when we look at productivity, we don't just look at PR throughputs or token rates or things like that. We also look at defect ratios, reliability metrics. So we try to guardrail the velocity with craft and judgment to make sure that we are sort of continuing to stay on track. That way, when you get exponential use of AI in your system, you're also pretty much reinforcing a culture that you kind of need to maintain the quality of your system as well. So I think that's one of the very high level things that stays true everywhere. But I think there's some common truths in how to deploy AI agents for your own productivity that are consistent at every company. So how do you basically MCP your entire company so that an agent can access what it needs based on any generic task? How do you construct context in your code base or documentation within your code base so that agents are able to specialize on the work that they need to do? So if I am a product engineer and I need to work on a PR that lands in the payment stack, which is complicated and regulated and gnarly, is there enough documentation to inform the agent so that your PR is actually reasonable for the payment team to review?