---
title: "Teaching Agents to Search: Building Synthetic Training Pipelines with NVIDIA Data Designer"
category: "talks"
date: "2026-06-29"
time: "11:05am-12:05pm"
track: "Workshops Day 1"
room: "Track 5"
speakers: ["Dhruv Nathawani"]
sourceLabels: ["Official conference schedule", "Public YouTube metadata"]
scheduleTrack: "Workshops Day 1"
scheduleRoom: "Track 5"
scheduleLabels: ["Workshops Day 1", "Track 5", "workshop", "confirmed"]
---
# Teaching Agents to Search: Building Synthetic Training Pipelines with NVIDIA Data Designer

## Conference Context
- Date/time: 2026-06-29 · 11:05am-12:05pm
- Track/room: Workshops Day 1 · Track 5
- Speaker(s): Dhruv Nathawani
- Session type/status: workshop · confirmed

- Track: Workshops Day 1
- Room: Track 5
- Session type: workshop
- Status: confirmed

## Session Description
Modern agentic systems often fail because the right training data simply does not exist. Search agents are a perfect example: if you want a model to browse the web effectively, you need high-quality multi-step trajectories that teach it how to search, refine queries, inspect sources, and recover from dead ends. Those datasets are rarely available off the shelf. In this hands-on workshop, we will show how NVIDIA used Data Designer to build synthetic supervised fine-tuning data for search-capable Nemotron models. Participants will learn how to translate a target capability into a scalable data generation pipeline: defining task structure, generating strong seed examples, producing realistic search trajectories, filtering low-quality generations, and converting traces into training-ready records. Using a real search-agent use case, we will walk through the design decisions behind teaching Nemotron Super to browse the web, including how to create BrowseComp-style tasks, generate tool-use rollouts, and manage the tradeoffs between diversity, correctness, and yield. We will also cover the practical realities of production synthetic data workflows, including validation, dataset curation, and where most pipelines break down. But the goal of this workshop goes beyond search. Participants will leave with a reusable framework for designing any dataset they wish they already had: starting from the behavior they want to teach, mapping that behavior into a data schema, generating examples at scale, and iterating until the dataset is useful for training. By the end of the session, attendees will not only know how to build synthetic data for search agents, but how to design custom datasets for specialized behaviors across reasoning, tool use, and domain-specific applications. Attendees will leave with a practical methodology for synthetic data design, plus hands-on familiarity with NVIDIA Data Designer as an open-source system for rapid experimentation.

## Media Evidence
No related AI Engineer channel video found yet.

## Evidence Graph
This evidence graph is generated from currently linked source material: official schedule text, related video pages, cached transcripts, visible slide text, dense/reconstructed slide pages, and AI slide-classification audits.

### Media Signals
No linked video, transcript, or slide source has been attached yet.

### Agent Reading Notes
Use these signals to refine the synopsis, topic links, people/company context, and method notes. If a source is a related external video rather than an exact official recording, keep it framed as supporting evidence.

## Transcript Status
No official session recording transcript was found by exact title match on the AI Engineer YouTube channel during this run.

## People
- [[dhruv-nathawani]]

## Notes
- Pending transcript synthesis when an official recording or confirmed matching video is available.

## Synthesis
### Synthesized Breakdown
# Teaching Agents to Search: Building Synthetic Training Pipelines with NVIDIA Data Designer ## Conference Context - Date/time: 2026-06-29 · 11:05am-12:05pm - Track/room: Workshops Day 1 · Track 5 - Speaker(s): Dhruv Nathawani - Session type/status: workshop · confirmed - Track: Workshops Day 1 - Room: Track 5 - Session type: workshop - Status: confirmed ## Session Description Modern agentic systems often fail because the right training data simply does not exist. Search agents are a perfect example: if you want a model to browse the web effectively, you need high-quality multi-step trajectories that teach it how to search, refine queries, inspect sources, and recover from dead ends. Those datasets are rarely available off the shelf. In this hands-on workshop, we will show how NVIDIA used Data Designer to build synthetic supervised fine-tuning data for search-capable Nemotron models.

### Speaker And Company Context
- [[dhruv-nathawani|Dhruv Nathawani]] — Research Scientist at [[nvidia|Nvidia]].

### Topics Covered
- [[agentic-search]]

### Derived Links And Source Material

### Novel Concepts / Clever Methods
- No highlighted novel concept has been detected yet.

### Evidence Boundary
This synthesis is based on the official schedule and linked source pages. It should be revisited when exact session recordings or transcript-backed secondary sources are available.