Markdown source

Simulation-Maxxing: How Nubank ships agents 20× faster with simulations

Conference Context

Session Description

You know how to build an agent - write a prompt, spec out some tools and call an LLM (or gateway). At this point, you probably also know how to build an agent that “actually works” using some combination of agent frameworks, eval tools and looking at your data. This talk is about building an agent much, much faster using simulations to hill-climb your agent configuration instead of grinding on real data. We’ll dive deep into a case study of how a top-5 fintech made their agent dev cycle 20x faster using simulation-driven optimization. We’ll cover: - When to use real data vs. simulations in agent building - How to design simulation environments tailored to your agent - How to automate the optimization loop so you’re hill climbing agent configurations without manual tuning

Media Evidence

Trust, but Verify: Shreya Rajpal (speaker-match related prior/adjacent AI Engineer video; captions: English auto-captions).

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

Agent Reading Notes

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Transcript Status

Related video transcript availability: English auto-captions. Treat this as supporting context, not a recording of this exact scheduled session unless later confirmed. Not fetched yet.

People

Supporting Slides

Slide Evidence

Synthesis

Synthesized Breakdown

Simulation-Maxxing: How Nubank ships agents 20× faster with simulations ## Conference Context - Date/time: 2026-07-01 · 2:50pm-3:10pm - Track/room: AI in Finance · Track 3 - Speaker(s): Shreya Rajpal, Aman Gupta - Session type/status: session · confirmed - Track: AI in Finance - Room: Track 3 - Session type: session - Status: confirmed ## Session Description You know how to build an agent - write a prompt, spec out some tools and call an LLM (or gateway). At this point, you probably also know how to build an agent that “actually works” using some combination of agent frameworks, eval tools and looking at your data. This talk is about building an agent much, much faster using simulations to hill-climb your agent configuration instead of grinding on real data. We’ll dive deep into a case study of how a top-5 fintech made their agent dev cycle 20x faster using simulation-driven optimization.

Speaker And Company Context

Topics Covered

Derived Links And Source Material

Novel Concepts / Clever Methods

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.