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.

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.