סוכנים מלאכותיים לביצועים: משלוח מהיר, תשלום פחות

תקציר מקורי באנגליתAn inefficient quadratic-time pattern was hiding inside a tensor merge method on a live Netflix service, quietly wasting CPU on every request - nothing in a code review caught it, but it stood out clearly in a call stack. That's usually how this kind of waste gets missed - the normal process of catching it (profiling, reading a flamegraph, tracing it through the codebase, writing a fix, validating it) takes long enough that inefficiencies sit in production for months before anyone looks at them. AI-assisted coding adds to this, since it produces more code optimized for how fast it's written, not how fast it runs. Rajat Shah's team at Netflix built an agent to handle this loop: it took a call stack, traced the issue to the exact source line, proposed a fix, and validated it against a canary