At Netflix, performance waste is everywhere- and almost no one is looking for it.
Degradation is silent. It compounds. The manual cost of closing the loop (profile, analyze, trace, fix, validate) means most inefficiencies quietly burn compute for months before anyone acts. By the time a human gets there, the damage is done.
We decided the loop should close itself. We built an autonomous agent that continuously hunts performance inefficiencies across live production services, traces them to source code, proposes fixes, and validates results through canary deployment- grounding every decision in measured production outcomes, not model confidence. We'll also discuss about how the Agent memory and knowledge graph evolves over time to become more effective and efficient over time.
In this talk, we'll share what it actually took to make an autonomous agent trustworthy enough to act in production: where it earns autonomy, where it doesn't, and a novel approach that changed how we think about agent reliability entirely. One finding the agent surfaced- caught, fixed, and canary-confirmed- with no ticket, no oncall, and no performance engineer in the loop.
This is not a demo. This is already in production at Netflix.
Speaker
Rajat Shah
Staff Software Engineer, AI Platform, Netflix
Rajat is a Staff Software Engineer at Netflix, leading the technical architecture for the global ML Model Serving Infrastructure. Over a decade at Netflix and Amazon, he has specialized in building highly available distributed systems and stable, usable ML platforms that power recommender systems, search, and payments at scale. A recipient of Amazon’s prestigious "Just Do It" Award by Jeff Bezos for his bias for action, Rajat excels at abstracting distributed computing complexities to drive developer velocity and platform reliability. He holds a Master’s in Machine Learning from North Carolina State University, blending deep theoretical knowledge with a proven track record of solving massive-scale infrastructure challenges.
