JuliaCon 2026

What if engineers could go from concept to validated simulation model through conversation alone? DyadAgent, built on Julia and Dyad, makes this possible by combining generative AI with the SciML ecosystem to construct, compile, and rigorously validate high-fidelity physical models from natural language. It handles planning models, creating them, validating them, debugging them and using them in downstream applications such as parameter estimation, model discovery and more. This workshop demonstrates how DyadAgent is reshaping the modeling workflow across engineering domains.

DyadAgent is an AI coding assistant for generating and debugging Dyad code across modelling and simulation workflows, enabling engineers to express complex model requirements in natural language. Evaluating the performance of such an agent requires verification of generated simulation results against standards of physical correctness and numerical accuracy. DyadAgentBench is an evaluation infrastructure designed to measure the agent's modelling and simulation capabilities in a systematic and reproducible manner. In this talk, we present the infrastructure and evaluation framework developed for DyadAgent, covering how agent performance is assessed and how the resulting insights are used to benchmark and guide iterative improvements.