Ashwin Sreenivas
Showing 1–2 of 2 transcripts.
- a16z1h 20m
How Decagon Runs 90% of Its Agents on Open-Source Models
Sarah Wang, Kimberly Tan, Jesse Zhang, Ashwin Sreenivas
Decagon Labs challenges the prevailing 2026 narrative that AI agents will replace all software by establishing a dedicated "model factory" that prioritizes open-source fine-tuning over frontier models to achieve superior latency for voice interactions. The company differentiates its enterprise deployment through a "glass box" approach and its Duet Autopilot system, which automates the creation of agent procedures and reduces production timelines from years to months. Despite rapid international expansion and the lowering of technical barriers, Decagon contends that the primary constraint remains human talent acquisition while affirming that AI will drive new service demand rather than eliminating careers or rendering legacy SaaS obsolete.
- RAISE Summit18 min
AI's Dropout Problem: Why the Best Agents Never Stop Learning | Decagon | RAISE Summit 2026
Ashwin Sreenivas, Arjun Kharpal, Alan Palleringham, Seth Vargoe
Decagon delivers an outcome-based platform for regulated enterprises that enables large organizations to collaboratively build and safely deploy AI agents across voice, chat, and text channels. The company differentiates itself through a robust deployability layer featuring a testing suite, real-time production monitoring, and a learning loop that automates improvements based on escalated human interactions. By prioritizing infrastructure, compliance, and model cost-efficiency over raw capability, Decagon targets high-stakes sectors like finance and telecommunications to minimize brand risk while driving revenue growth.