Foundation models come with broad language and reasoning capabilities across use cases and modalities based on the training datasets used. Models understand language and can follow instructions, but specialized workflows often require context that is restricted, specialized, or proprietary. Customizing an agent solves this challenge by shaping how the agent reasons under constraints, which tools it selects, how it structures its outputs, and how reliably it executes domain workflows.
Mastering Agentic Techniques: AI Agent Customization | NVIDIA Technical BlogIn practice, the most effective agent customization combines multiple techniques in sequence. The stages of a representative pipeline are outlined below. Start with system prompts, tool and skill definitions, and retrieval to establish baseline behavior.
Mastering Agentic Techniques: AI Agent Customization | NVIDIA Technical Blog…The training framework then uses the cuStabilizer library within NVIDIA cuQuantum and PyTorch to generate synthetic training data and train a 3D CNN that optimizes decoding performance for the task. Users can…
…E AI-Generated Summary Like Dislike Simulation, especially using GPU-accelerated frameworks, has become crucial for scaling robot learning beyond the limitations and risks of real-world data collection, enabling training in…
…datasets Nemotron 3 Super is built on a fully open, end-to-end data pipeline that spans pretraining, post-training, and interactive reinforcement learning—giving developers reproducible building blocks for agentic AI…
…Run high-fidelity reactor/multiphysics simulations (ideally GPU-accelerated) to produce training data. Data preprocessing: Use PhysicsNeMo Curator to curate/transform geometry and fields into GPU-ready training datasets. Model training: Use…
…Prompt to running pipeline, from synthetic data and training to SIL and HIL evaluation, no infrastructure expertise required. How It Works Define your entire physical AI pipeline in a single YAML file…
Data Science Winning a Kaggle Competition with Generative AI–Assisted Coding Apr 23, 2026 By Chris Deotte Discuss (0) Discuss (0) L T F R E AI-Generated Summary Like Dislike In…
…NVIDIA Isaac™ Lab Train your robot policy in the Isaac Lab open source unified framework for robot learning. NVIDIA Physical AI Dataset Unblock data bottlenecks with the NVIDIA Physical AI Open Datasets…
…Integrated NVIDIA ConnectX-8 SuperNICs ensure high-throughput east-west traffic for AI training and inference, while NVIDIA BlueField DPUs streamline north-south data flow—together allowing the entire rack to operate…
…Increasingly, high-fidelity simulations are also used as engines for dataset generation, producing training data for AI models that augment traditional solvers. These AI models can act as intelligent pre-conditioners, accelerate…
…Open datasets for physical AI applications like robotics and autonomous driving. Open post-training scripts for adapting Cosmos 3 to your domain. Cosmos NIM microservices for easy, optimized deployment on NVIDIA GPUs…