Kavak deploys an agent per customer with its own virtual machine, enabling each agent to remember years of interaction history and optimize long-term customer value.
Instead of building agent workflows, Kavak instantiates 100,000-200,000 agents daily, each dedicated to one customer with full context and long-term goals. ✦ AI generated
Ali Massa · a16z Podcast · 2026-08-10 · original ↗
starts at this moment · 3:46
“I want to sort of start with the framing of what the architecture looks like. So, a consumer comes in and says, 'I want to sell my car.' Like how many agents do they touch? Like what's the harness look like? Like ground us in how you design this.”
So, when a customer comes in right now, um agent will get spawned specifically for this customer with its own virtual machine. It'll remember years of interaction of this customers with with Cabal, what they visited in the web page or call they had 2 years ago. Remember everything like in its memory, come up with a strategy and set a long-term goal to maximize the lifetime value of this customer and do whatever it takes to make the customer happy and convert them into like all our different products like across time. And this is a completely new and groundbreaking architecture at scale, I think, because like people are still building multi-agent system with with experts and and we realized too bad that long-running agents with hard goals, not just workflows, could could maximize our our customers satisfaction and obviously their their lifetime value.
verbatim transcript · starts at 3:46
3:46um agent will get spawned specifically for this customer with its own virtual machine. It'll remember years of interaction of this customers with with Cabal, what they visited in the web page or call they had 2 years ago. Remember everything like in its memory, come up with a strategy and set a long-term goal to maximize the lifetime value of this customer and do whatever it takes to make the customer happy and
4:14convert them into like all our different products like across time. And this is a completely new and groundbreaking architecture at scale, I think, because like people are still building multi-agent system with with experts and and we realized too bad that long-running agents with hard goals, not just workflows, could could maximize our our customers satisfaction and obviously their their lifetime value. >> Awesome. Okay, so we're going to jump to
4:46the nuances that but maybe versus many companies that say, "Hey, we want to be agentic." And they try some workflows. >> Yes. >> You guys took the just rip like we had to make this work. You had to downsize dramatically. It didn't work for a year. >> Right. >> So, do you want to talk through obviously you had to tune a lot of things to make that work.
5:07Like describe the harness at that time and like what models you were using and sort of specifically. Yeah. >> So so there there were like three main decisions that that we had to make. The first and this is what where I think many companies are stuck right now is the first instinct is, "Okay, let's adopt AI." And you you basically leave your structure as it is and just give
- ·One agent spawned per customer with its own virtual machine
- ·Agent remembers years of customer interaction history
- ·Sets long-term goals to maximize customer lifetime value
- ·100k-200k agents instantiated daily at scale
- ·Agents track interactions across channels and years
- ·Full context includes web visits and past calls
- ·Develops strategies to convert across all products
- ·Purpose-built for maximizing lifetime value, not just tasks
- ·Traditional: expert-based multi-agent workflows
- ·Kavak: single agent with hard goals per customer
- ·Agents optimize across products over time