AI Agents for Performance: Ship Faster, Pay Less — Rajat Shah, Netflix
Jul 28, 2026 · 33:39
Rajat Shah from Netflix explains how an AI agent can replace the manual performance engineering loop by reading profiling data, identifying inefficient code patterns like an O(N²) tensor merge method, and producing a validated fix — all within five minutes. The agent traced a call stack to the exact source line, proposed an optimized implementation, and ran a canary deployment to confirm CPU savings without regression. Shah emphasizes building a shared catalog of anti-patterns (starting as markdown files in a Git repo) so future agents can catch similar issues earlier, even during code authoring. He advocates shifting left from reactive profiling to proactive prevention, using unit tests, canary automation, and human approval as guardrails. The playbook aims to help teams adopt the same loop to reduce infrastructure cost and ship faster.
