Sreenivas: Improved model instruction-following unlocks open-ended workflows like sales discovery
Ashwin Sreenivas · How Decagon Runs 90% of Its Agents on Open-Source Models · Jul 30, 2026 · at 50:02
Ashwin Sreenivas, co-founder of Decagon, explains why early AI agents were limited to rigid customer support paths and how model progress unlocked dynamic sales qualification.
0:00 / 0:42exact quote · 42.1s
▶ Watch the full episode on YouTube → 720p mp4 · rendered on demand · StarZero watermark
“When you had models, you know, let's say a few years ago, you'd have to give it very, very tight guidance, very specific instructions that you didn't want it to deviate from, and as the models got smarter, you could kind of give it broader and broader guidance, bigger and bigger instructions, and just trust that the models have good enough sense to interpret it like a human would and kind of fill in any missing gaps, right? Because for, again, for customer support, you can have a very tight path that a model should follow, and that's all you really need. Whereas for sales qualifications, you kind of want to ask open-ended discovery questions. The conversation is going to kind of bob and weave, and so you need the model to kind of fill in with reasonable things.”
quote is from the automated transcript, cleaned for reading: filler sounds and stutters are removed, nothing is rephrased. names can be misheard (the analysis reads context, assessments check outside sources). how →
More from Ashwin Sreenivas
Sreenivas: AI startups relying permanently on FDEs become glorified consultancies
“Once you know what the workflow is, you should not be relying on forward deployed engineers anymore, because once you know what the workflow is, If you can productize it, you should productize it and then become, you know, typical company with these scaling pr…”
Sreenivas: Deploying custom open-source models eliminates major AI cost pressures
“If we ran our entire business exclusively on frontier models, I would care a lot about costs, and I would think a lot about that, but once you're already, once you've already made the jump to saying, okay, now, now we know how to think about open source models…”
Sreenivas: AGI will not replace software because agents still need infrastructure
“I think even once you have AGI, all our AGI agents are going to need somewhere to store work and pull information from and reason about things. So I think, you know, a certain class of SaaS companies that were solely built for people to do work might face a bi…”
Sreenivas: Early-stage AI startups need forward-deployed engineers for unproven workflows
“Forward deployed engineers are necessary or newly necessary for early stage AI companies because the workflows are new. Right? If you're building a SaaS company five years ago, most SaaS products are pretty well explored, right? Like you roughly know what the …”
Sreenivas: AI productivity gains will drive companies to build more, not fire
“Everybody has access to these tools, and so if our competitors are going to use them and build more things, we need to build more things, right? If somebody else said, oh, here's our roadmap, and now we can get through it in, you know, a third of the time, and…”
Sreenivas: Optimizing AI agents breaks the cost, latency, and performance tradeoff
“Performance latency and accuracy is definitely the driving factor for most of this, right? Cost is a nice benefit in that, you know surprisingly, this is one of the few like tasks where you kind of get all the things for free, right? Like we don't actually hav…”