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…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…
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…At scale, these checkpoints become massive (782 GB for a 70B model) and frequent (every 15-30 minutes), generating one of the largest line items in a training budget. Most AI teams…
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…View Performance Data For: Latest NVIDIA Data Center Products Training to Convergence Deploying AI in real-world applications requires training networks to convergence at a specified accuracy. This is the best methodology…
…reinforcement learning (RL) for training models with new specialized skills such as coding or engineering, and agentic actions , which enable AI agents to use tools like web browsers, databases, code interpreters, and…