Agents· 🧪 Worth Testing
Siemens uses NVIDIA NeMo Gym to make chip-design agents faster and more accurate
NVIDIA published a case study showing how Siemens EDA applied its open NeMo Gym evaluation library to tighten up an agentic tool used for chip characterization. Instead of checking individual tool calls, the team scored complete final output files with a domain-specific verifier, then used the failure patterns to rewrite the agent's skill instructions and MCP tool descriptions. After several rounds of this loop, a composite accuracy benchmark climbed from roughly 55% to 95%, while the agent needed far fewer steps and far less output to get there. The experiments relied on about 25 evaluation rollouts running Claude Opus 4.8 as the agent model inside Claude Code.