Can AI Agents Finally Fix Customer Support?

**Jesse Zhang** (0:00)
How do you actually build an agent? Our view is that over time, it will become more and more like natural language based because that is how agents think or how this basically what LLMs are trained on. And in the limit, right, if you have like a fully, just like super intelligent agent, it would basically be like a human where you can show it stuff, you can explain it stuff, give it feedback, and it just kind of updates in its mind. Like if you just think about having a really competent human on your team, they arrive, you teach them some stuff, they start doing work and then you just give it feedback and you can show a new things, you can show it like new documentation or show it new charts or whatever. In the limit, it kind of moves towards that, where things are a lot more conversational and things are more natural language based and people aren't just using these stop gaps of building gigantic complex decision trees that sort of capture what you want but can break apart pretty easily. We had to do that in the past because that's all we had. We didn't have LLMs, but now as the agents get better and better, the UX and the UI is going to be more conversational.

**Derrick Harris** (0:58)
Good day and welcome to the a16z AI podcast. I'm Derek Harris, and joining me for today's episode are Decagon co-founder and CEO Jesse Zhang, along with a16z partner, Kimberly Tan. Kimberly leads the discussion with Jesse, who shares his experiences so far with building Decagon as both a company and a product. If you're not familiar, Decagon is a startup supplying businesses with AI agents to assist in customer support. These are neither chatbots nor single API call LLM wrappers, but rather advanced, tunable agents personalized to a company's specific needs and able to handle complex workflows. In addition to explaining why they started Decagon and how it's architected to handle different LLMs and customer environments, Jesse also touches on the benefits of a per-conversation business model and how AI agents will change the required skill sets of the people in charge of customer support. It's also worth noting that Kimberly recently wrote a blog post titled RIP to RPA, the Rise of Intelligent Automation, which we briefly discuss in the episode. It's a great starting point to understand where and how this type of automation is taking off for business processes, and we'll post a link to that in the show notes. As a reminder, please note that the content here is for informational purposes only, should not be taken as legal, business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any a16z fund. For more details, please see a16z.com/disclosures.

**Jesse Zhang** (2:35)
So quick background on me, born and raised in Boulder, grew up doing a lot of math contests, stuff like that. Studied CS at Harvard, started a company afterwards that was also backed by a16z. We eventually got acquired by Niantic. And then here we are building Decagon. What we do, we're building AI agents for customer service. When we first got started, it was for us, we wanted to build in something that was like very, very relatable for ourselves. And so of course, no one needs to kind of be taught like what AI agents for customer service can do, right? We've all been on the phone on hold with airlines or hotels or whatever. And so that's kind of where the idea originated. And we just talked to a bunch of customers to see like specifically what we should build. I think for us in particular, the thing that stood out is that as we learned more about AI agents, we started to really think a lot about what would the future look like when there are a lot of AI agents. Like I think everyone believes that there's going to be a lot of AI agents that come up. And so for us, an interesting thing would be what would the humans that work around the AI agents do? Like what tooling would they have?
What sort of control or visibility would they have into the agents that they're working with or managing? And so that's really what we built the company around. I think that's the thing that's made us special so far, is that we have all this tooling around these AI agents for the people that we work with to build them and configure them and just make it not really a black box. So that's kind of where we've created our brand.