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:

Nemotron-CCused synthetics to enhance the popular Common Crawl dataset for pretraining.Nemotron-CC-MATHleverages synthetic math questions to improve reasoning.Nemotron Pretrainingis a broad collection spanning general, code, math, and synthetic data across trillions of tokens.

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.

Synthetic data, released openly, is one way to change that math.

As part of Nemotron open data, we've released over 10 trillion pre-training tokens and millions of post-training samples spanning many domains and data shapes. That's a lot to make sense of — and raw dataset tables don't help much.

To make it easier to explore what's actually in Nemotron post-training data, we built the Nemotron Post-Training v3 Prompt Atlas: an interactive visual map where each point is a prompt sample, drawn from the Nemotron v3 post-training collection and volume-sampled to reflect the honest proportions of the data mixture.

Color overlays and filters let you reorganize the map by dataset, pipeline stage, domain, or tool use. Since semantically similar prompts cluster together, you can zoom into a region — coding algorithms, safety, math, agentic behavior — inspect representative examples, and use that signal to curate data, build evals, or understand why a model behaves the way it does.

Agents also need to understand people they are built to support, and this is where “data quality” becomes local, not universal. A toxicity classifier trained on English internet data can miss hostile messages in Korean or Japanese, where aggression is often encoded in politeness levels rather than obvious vocabulary. Same signal, different context. Teams are already grounding agents this way.

Nemotron-Personas is one attempt at addressing that: locally grounded synthetic personas capturing the diversity and complexity of populations. Built using NeMo Data Designer, NVIDIA’s state-of-the-art compound-AI tooling for synthetic data generation, Nemotron-Personas mirrors official regional demographic and geographic statistics The goal is not to recreate real people. In a way, it’s to help developers test whether their systems reflect the users, languages, regions, and occupations they claim to serve. Last month at VivaTech in Paris, we launched our tenth country in the collection, which now represents more than 2.4B people.

When quality is local, only people who know that locality can build it — regional researchers, native speakers, subject-matter experts, stakeholders who can inspect and correct alongside you. That's learning in public: not releasing data in isolation, but building it collaboratively.

Synthetic data needs to be integrated as part of a system of data sources. There are tradeoffs. It can reduce risk, but it does not remove the need for grounding, lineage, curation, evaluation, and human judgment.

One useful way to think about this is with "synthetic thresholds": points where data can no longer be treated as purely real. That line is not always obvious. Real workflows, human feedback, model-generated traces, simulated users, and synthetic labels can all become intertwined. The answer is not to pretend synthetic data is fake or harmless. It is to document what was generated, what was grounded, what was reviewed, and what the data is meant to test. As more AI systems are trained on artificial information, we need better shared habits for inspecting it, documenting it, and debating these technologies in public.

Quality also means different things in different contexts. Reasoning data needs harder problems and cleaner traces. Persona data needs distributional fidelity and local review. Agentic workflows need task diversity, failure coverage, and recovery paths. The field is still more craft than formula.

That is why open methods matter. Synthetic data is not just about generating more examples. It is about asking better questions, and making it possible for parties who otherwise could not sit at the same table: companies without giving away their secrets, governments without compromising privacy, and researchers without waiting for permission that may never come.

The scarce resource in AI is not tokens. It is trust between organizations. Synthetic data is one of the few tools we have for building it.