Netflix built an autonomous agent that hunts performance inefficiencies in live production services, traces them to source, proposes fixes, and validates results through canary deployment—grounded in measured outcomes, not model confidence. This talk covers what it took to make the agent trustworthy enough to act unsupervised, including a real fix it caught and confirmed with no ticket, oncall, or engineer in the loop.
Rajat is a Staff Software Engineer at Netflix, leading the technical architecture for the global ML Model Serving Infrastructure. With nearly a decade at Netflix and Amazon, he has specialized in building highly available distributed systems and stable, usable ML platforms that power personalization, search, and commerce at scale. A recipient of Amazon’s prestigious "Just Do It" Award from Jeff Bezos for his bias for action, Rajat excels at abstracting the complexities of distributed computing to drive developer velocity and platform reliability. He holds a Master's in Computer Science from North Carolina State University, and has spent his career applying that foundation to massive-scale infrastructure challenges.
