The Ledger
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Zhang: Most AI agent use cases lack commercial viability with current models
“I think our view is that for the vast majority of use cases right now, it is still, like there's not going to be real commercial adoption with the state of the current models because of a bunch of things.”
Liu: Point-solution AI touches only a fraction of enterprise workflows
“I think that, that, you know, you'll have some use cases addressed through solution companies that can be very big you know each individually. And yet, you know, I still think even if you added all of them up, they're still tapping into such a small fraction o…”
Zhang: Decagon prioritizes raw intelligence over direct experience across all roles
“We're generally just selecting for very smart people. First of all, I think we care more about that than like, you know, direct experience and so on.”
Zhang: AI agent industry will shift toward output-based pricing
“And we'll probably start seeing that more and more in the AI agent space where you generally price per like the output that that's doing. I think that that works. I think that's just very clearly the right pricing model for our space”
Zhang: Decagon mandates five days in-office, with many working weekends voluntarily
“We're five days and then a lot of folks come in on the weekends, but it's not like a requirement.”
Zhang: Consumers Will Interact With Agents Over Apps and Websites
“And eventually, if it's good enough, most consumers will just interact with the agent instead of even logging into the mobile app or the website.”
Zhang: Decagon cuts enterprise contact center costs by 60% to 70%
“And we've done case studies now where, you know, folks have been able to cut that down by, you know, 60, 70%.”
Gil: AI customer success is consolidating around Sierra and Decagon
“In customer success, it seems like things are kind of consolidating against Sierra and Decagon.”
Zhang: Instruction following matters more than reasoning for customer service AI
“And for us, actually those things help, but they're actually not the biggest difference maker. So in our use case, the type of intelligence that matters the most We would probably describe it as instruction following.”
Decagon AI saved Bilt Rewards 65 support agent headcounts in one year
“Now basically we're almost, almost a year in at this point, they've been able to really restructure their customer support team. And again, we published a case study on this where they were able to quantify like, okay, what are the savings? Right. And so, so f…”
Gil: Decagon and Sierra Represent a Shift Toward Utilization-Based Support Agents
“Ultimately, I think Decagon and Sierra are examples of companies where you're moving from proceed software to basically utilization based customer support related agents, right? That is a real shift. That may impact some of the prior wave of sort of perceived …”
Zhang: Customer Support AI Will Expand into Upselling and Proactive Outreach
“Right now a lot of the conversations are more reactive support. It's like, hey, I have an issue. Can you fix it? But over time, it'll be more and more kind of broader, right, in terms of, like, being able to do purchasing decisions, being able to upsell folks,…”
Zhang: Pricing AI agents per minute incentivizes needlessly long calls
“You also don't want to price per like, you know, minutes of the call either like that. That's just kind of weird. And also incentivizes the agents to just like have really long calls. So you price basically the number of conversations that it can have.”
Zhang: Engineering teams want to offload AI agent logic to business users
“What we found is even when the engineering teams are very much involved, they don't necessarily want to be on the hook for every little change, and so in that case, we can work very well with them, and you have them still owning how does the AI agent interact …”
Zhang: Near-Term Agent-to-Agent Communication Will Remain in Natural Language
“I think in the near term, they'll still communicate in natural language, just because, like, each agent also needs to be compatible with humans, right? So if they talk to a human agent, a human support agent, or if we talk to a human customer, of course it has…”
Zhang: Most AI application alpha lies in orchestration and surrounding software
“Most of the sort of alpha or most of the specials stuff that you build is on top of models. It's either the orchestration layer or the software around it.”
Zhang: Customer service is the golden use case for AI agents
“Our current use case as maybe what we think is like the golden use case for these AI agents, which is customer interactions, customer service. The use case is very tailor made for what LLMs are good at.”
Zhang reveals reciprocal angel investing among Math Olympiad AI startup founders
“I angel invested in a lot of the companies you just listed. A lot of their founders are angel investors in our company.”
Zhang: Customer service AI drove six-figure demand at zero ARR
“The real answer is we just saw a lot of folks that were willing to pay us like, you know, six figure contracts, which at the time when you're at zero ARR, it's like,
oh wow, that's huge.
And a lot of folks that were willing to, you know, do the same thing.
And…”
Zhang: Consumer AI Agents Contacting Enterprise Support Not Yet Happening at Scale
“It's not something we're seeing at scale now where you have agents writing in for you.”
Zhang: Decagon is approaching 200 employees
“Never really needed that, but, you know, we're approaching 200 people.”
Zhang: Decagon is evolving into a conversational UI concierge for brands
“And as we, as we've grown, it's kind of becoming more and more of you gotta think of like a conversational UI for the brand where it's how every user can interact with it. And we often use the term like concierge to describe this, but that's what we do.”