Agentic Kernel Optimization, visualized.

Explain Like I'm Normal

An autonomous swarm of agents spent 40 hours redesigning a model's execution graph, collapsing 331 compute nodes down to just 22. By discovering new operator fusions and custom WebGPU kernels for complex architectures like MLA and MoE, the agents boosted throughput from 65 to 406 tokens per second. This demonstrates a shift where AI doesn't just write app code, but optimizes its own low-level GPU hardware instructions.