# Siemens Fuse EDA AI Agent

Siemens Fuse used NVIDIA NeMo Gym to build an evaluation loop for the Solido Characterization Suite agent that grades final Liberty files, raising the mean composite verification score on standard-cell characterization tasks from about 55% to about 95% while cutting agent turns from 155 to 53 and output tokens from about 81K to 29K. Previously such multistage flows could only check whether individual tool calls succeeded, and an early setup error could propagate silently downstream; now a composite reward combining coverage, validity, relative validation, integrity and the in-tolerance fraction scores the final artifact of each rollout, and those findings guide rewrites of skills and MCP tool descriptions.

Siemens Fuse used NVIDIA NeMo Gym to build an evaluation loop for the Solido Characterization Suite agent that grades final Liberty files, raising the mean composite verification score on standard-cell characterization tasks from about 55% to about 95% while cutting agent turns from 155 to 53 and output tokens from about 81K to 29K. Previously such multistage flows could only check whether individual tool calls succeeded, and an early setup error could propagate silently downstream; now a composite reward combining coverage, validity, relative validation, integrity and the in-tolerance fraction scores the final artifact of each rollout, and those findings guide rewrites of skills and MCP tool descriptions.