As frontier AI models grow in scale and complexity, developers face a common challenge across every hardware platform: how do you extract the maximum performance and efficiency from the silicon their models run on. Whether delivering real-time experiences for world models, supporting deeper reasoning in agentic workflows, or reducing inference costs at scale, the gap between what hardware can theoretically deliver and what most teams achieve remains significant. Custom kernel development has historically been the path to closing that gap, but it demands deep architectural expertise, manual profiling workflows, and iterative optimization cycles that few teams can afford.

This doesn’t need to be the case. What if every machine learning (ML) engineer could operate as a performance engineer, writing hardware-aware kernels, diagnosing bottlenecks, and shipping optimized models, without years of chip-level experience? What if developers already proficient on one architecture could ramp up on another in days instead of months?

Today, we’re announcing the Neuron Agentic Development capabilities: a collection of AI agents and skills that make this possible for developers building on AWS Trainium and AWS Inferentia. The first capabilities equip coding agents in Kiro and Claude to author, debug, and profile Neuron Kernel Interface (NKI) kernels, extending ML performance engineering to every developer on the team. Kernel developers coming from other architectures can scale quickly to Trainium, teams can shorten the time from idea to hardware-optimized implementation, and the deep architectural knowledge that once gatekept kernel development is now accessible through agentic tooling that guides developers at each step.