Decagon’s Playbook for Building Enterprise AI Applications
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An AI agent should just be the front door of your business, and every interaction, whether it's reactive or proactive with a customer, should be handled by AI.
This narrative dominated the first half of twenty twenty six, which is that Anthropic, OpenAI, they're the last startups. They're gonna 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 task 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. Well, instead of us having to write these AOPs, 200 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 ten 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 Kimberly 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.
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