# Agentic AI and Next-Gen Intelligence at PyTorch Conference 2026 Key sessions focusing on training and deploying AI agents in production. - **Source:** [PyTorch Blog](https://pytorch.org/blog) - **Published:** 2026-09-02 - **Topic:** [Data & AI](https://brifing.si/c/data-ai) - **Original article:** - **On Brifing:** https://brifing.si/s/613e576e-0e23-4dec-95df-51e0090de0da ## Conference Main Theme PyTorch Conference North America 2026 focuses on Agentic AI and next-gen intelligence systems. Topics include agents writing core software, managing CI processes, and optimizing workloads across chips. The question is no longer whether models can do these tasks, but how to train, serve, and govern them. ## Keynote Speeches Three major keynotes set the conference's framework. Sara Hooker discusses systems enabling continuous post-deployment learning in 'Beyond Brute Force: The Era of Adaptive Intelligence.' Bill Jia from Google Cloud covers TorchTPU going open source and agent workload migration from GPUs to TPUs. ## Agentic AI Foundation Mazin Gilbert, Executive Director of the Agentic AI Foundation, explains how neutral governance structures support open-source AI deployment. With 25 years of experience and 260+ patents, he sheds light on the future of agent infrastructure. ## RL Training Agent training has evolved from research curiosity to production necessity. Yichuan Wang and Shuhua Yu from Meta detail the RL training loop, while Ben Burtenshaw from Hugging Face demonstrates open-source RL environments in action. ## Open Source RL OpenEnv, developed by Hugging Face, Meta, and others, serves as an RL environment layer. While frontier labs train models in proprietary environments, the open ecosystem separates models, environments, and training. This session shows how to bridge that gap. ## Practical SFT and GRPO Sergio Paniego Blanco from Hugging Face explains practical SFT and GRPO techniques in agent environments. He demonstrates how to build systems where the environment manages its own inner loop. ## Torchtitan RL Framework Felipe Mello and Jiani Wang from Meta show how to use a single model definition for both training and generation. Their bitwise-reproducible mode prevents system artifacts from interfering with reward design. ## Miles Framework Mao Cheng from RadixArk demonstrates scaling training using unified low-precision training, stable MoE RL, and accelerated speculative rollouts. ## Training-Inference Mismatch Neiwen Ling and Tianle Zhong explain why minor numerical discrepancies between training and inference aren't benign and how they can silently reshape PPO/GRPO objectives. ## Agent Serving in Production Agent workloads break traditional inference stack assumptions. Sessions may idle for hours, contexts grow long and multi-turn, and CPU work like orchestration becomes significant. ## Enterprise Agent Inference Joseph Groenenboom and Tyler Michael Smith from Red Hat detail what enterprise readiness entails, sharing practical insights on tool calling and long-context multi-turn chat. ## Intel's Approach Intel adopts an upstream-first strategy for heterogeneous agent AI workloads, leveraging Xeon processors, Intel GPUs, and open software stacks to deliver standard PyTorch workflows with deep optimizations. ## Sources covering this story - https://pytorch.org/blog/agentic-ai-and-next-gen-intelligence-sessions-at-pytorch-conference-north-america-2026/ — --- _Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._