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…While at the startup, Alex led a team of over 50 scientists and pioneers who were pioneering synthetic data-generation techniques for AI model training and differential privacy. He previously founded harvest…
Simulation / Modeling / Design Faster Chemistry and Materials Discovery with AI-Powered Simulations Using NVIDIA ALCHEMI Nov 18, 2025 By Wen Jie Ong , Justin S. Smith , Kibibi Moseley , Roman Zubatyuk and Nikita Fedik…
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Agentic AI / Generative AI Accelerating Long-Context Inference with Skip Softmax in NVIDIA TensorRT LLM Dec 16, 2025 By Laikh Tewari , Kai Xu , Bo Li and Fred Oh Discuss (0) Discuss (0…
…Computational biologist scaling structure prediction pipelines AI researcher training generative protein models HPC engineer optimizing GPU workloads Bioinformatician team building structural resources You will learn how to: Design a proteome-scale complex…