AI Engineer World's Fair 2026 — unofficial import demo

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