How Enterprise AI Really Gets Deployed
episodePreviously titled “Decagon’s Playbook for Building Enterprise AI Applications” — renamed by the publisher on Aug 4, 2026
Transcript
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What is the main topic discussed in this episode?
An AI agent should just be the front door of your business. And every interaction, whether it's like reactive or proactive with a customer, should be handled by AI.
This narrative dominated the first half of 2026, which is that Anthropic, OpenAI, they're the last startups. They're going to take over everything.
Even once you have AGI, agents are going to need somewhere to store work and pull information from and reason about things. I don't think software as a whole in any meaningful way is going away.
Unfortunately, the Frontier labs, they do have small models, but you can't really control them in the way that you want. So today, 90% of our workflow is on open source.
On the specific tasks we want them to do, they actually outperform the large, smart, state-of-the-art model. The thing that we built was not an agent that does customer support well, but rather an agent that follows business process well.
Instead of us having to write these AOPs, DoI just does all of that.
Let's say like we hit AGI and the models can do all sorts of things we can't even imagine today. What's Decagon's moat? And like, why does Decagon like 10 years from now still have a right to exist? The biggest breakthroughs in enterprise AI aren't just happening inside foundation models. They're happening in the products built around them. In this episode, Sarah Wang and Kimberley Tan sit down with Decagon co-founders Jesse Zhang and Ashwin Srinivas to discuss why they shifted most of their AI stack to open source models, how they think about deploying AI inside large enterprises, and why the future of enterprise software will be shaped by AI agents, not just better models.
Hey guys, welcome back to the studio.
Thanks for having us. Yeah, good to see you.
Thank you for being here. Before we get into customer support, I actually wanted to widen out a bit. And Jesse, I'm going to actually mention a piece that you wrote recently that went pretty viral because it's right in the middle of the zeitgeist of conversation right now on open source versus closed source models. And then thinking machines, Kimmy K3, some very interesting open source models came out sort of right after. And there's this really interesting debate going on around what does it mean to own your destiny when it comes to AI, especially in the enterprise? And what does that evolution look like by use case? So actually, since that's a pretty live topic right now, why don't we start there?
Sounds good.
So I can talk about our journey first, just to make it very concrete for people. So when we started the company, the goal was just to get something working, right? The goal is to get something working. Of course, you're just going to use the frontier models because you want to get something out there and have it actually deliver value. And so we were using OpenAI Anthropic. At that time, they were kind of like one-upping each other in terms of how the models performed. And then at some point, as we got to larger scale, we started working with larger and larger companies, and they had millions of customers. And then we also launched our voice agent, right? So a big factor became latency. So it wasn't just like, can you deliver good responses?
You have to deliver them really fast. And the only way to get latency down, but also kind of make our agent operate the way we want it to, is to use smaller models. And when you want to go to smaller models, unfortunately, the Frontier labs, they do have small models, but you can't really control them the way that you want. And most small models out of the box are not going to be good enough. That's the task that we want them to do. So you have to fine-tune them, you have to change them. And so that's when we started looking at open source. So this was about a year plus ago. And it worked really well because if you think about it, in the agent, right, so in our agent's job is to have conversations.
So it needs to do a lot of things at once, right? Like the first step it might do is, hmm, what topic is this person talking about?
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Chapters
5 chapters
1
What is the main topic discussed in this episode?
0:00–10:13
2
Why did Decagon shift most of its inference to open-source models?
10:13–20:24
3
How does Decagon balance latency, cost, and intelligence when choosing models?
20:24–35:33
4
What infrastructure and team capabilities are required to fine-tune open-source models at scale?
35:33–54:22
5
How does Decagon decide what to build in-house versus buy from vendors?
54:22–1:20:23
Speakers
3 identifiedMore from The a16z Show
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