Data for Agents

Why agentic AI needs open data, and why synthetic data is how we scale it.

Image: Nemotron Post-Training v3 Prompt Atlas

Building AI agents is hard, because the real world does not behave like a benchmark.

An agent that can't recover from a broken API call, or a workflow it has never seen, is not really an agent. It is an autocompleter with tools. Getting from one to the other is a data problem: software engineering traces, tool-use failures, multi-step reasoning, retrieval, safety, user simulation, workflow execution, and eventually physical world interaction. That is where NVIDIA Nemotron's open data products live.

NVIDIA recently highlighted how open models are driving AI research and showing up across the popular International Conference on Machine Learning (ICML), with nearly 145 papers citing Nemotron models and datasets. Synthetic data plays an important role across that ecosystem:

Part of why NVIDIA releases open datasets is to learn with the community to expand upon these various applications.

Open weights matter. But for agents, weights are only part of the story. Reproducibility also depends on the datasets, curation choices, training recipes, and evaluation methods behind the model.

Agent behavior needs to be inspectable. If a model calls tools, executes workflows, retrieves information, and acts across systems, developers need to understand the data that shaped those behaviors. Open data makes agent behavior inspectable and explainable. Synthetic data is a key piece of the puzzle to making that possible.

NVIDIA's VP of Applied Deep Learning Research Bryan Catanzaro recently noted: "every company is built around a secret" — a workflow, corpus, or customer pattern competitors don't have. Those secrets make AI useful, but companies shouldn't casually expose them. Synthetic data gives teams a way to preserve useful signals without exposing the underlying sources.

Bryan also talks about cultivating a diverse and participatory AI ecosystem where many kinds of companies, researchers, governments, and communities can contribute. That is not just a value claim. It is a data claim.

If every model learns from the same narrow pool of data, we should not be surprised when the models start to feel the same. The hard part is that the most useful data often sits inside organizations that cannot or will not publish it directly. Everyone benefits from a richer shared data layer. No one wants to be the first to give away the thing that makes them special.