AI agents offer remarkable productivity gains in complex chip design workflows, but their tendency to generate plausible yet incorrect artifacts makes reliability a major barrier to production adoption. In multistage engineering workflows, an undetected error early in setup can silently propagate through downstream stages and final artifact generation. Evaluating and refining these agents against verified final outputs, rather than isolated intermediate steps, is essential for building trustworthy automation.

NVIDIA NeMo Gym , an open library for evaluating and improving models and agents using environments, addresses this challenge. The NeMo Gym agentic evaluation environment is designed to diagnose failure modes and track error propagation across complex, multistage workflows.…