PyTorchCon 2026 Keynote Maps Five Layers of the Agentic Stack
The bidirectional design idea turns agent evaluation data into training rewards, a concrete recipe for closing the loop between deployment and model improvement. Researchers and framework builders can experiment with reward signals derived from real agent traces rather than static benchmarks. The framing also clarifies where projects like PyTorch and the Agentic AI Foundation sit relative to each other in the stack.
