From Observation to Action: Structured Engineering Inference for Agent-Driven Design on Real CAD/CAE Infrastructure

Engineering design optimization requires iterative simulation and modification, where each change must be grounded in reasoning about the simulation behavior it addresses. That cycle is exposed to software through computer-aided design and computer-aided engineering (CAD/CAE) tools, which recent tool-augmented agents can now operate end-to-end. However, operating tools is not the same as making informed design decisions. We present an engineering inference framework that structures the agent's reasoning from simulation observation to design action, with a 157-category controlled vocabulary of engineering failure modes, design modifications, and formal validation. We evaluate both the observation and the action step on real infrastructure. On observation, we extend prior benchmarks to tool-augmented and multi-view settings across 560 Finite Element Analysis questions, finding that interpretation accuracy ranges from 38-65% regardless of model tier, tool access, or viewpoints. On action, we compare an unstructured Baseline agent against an Ontology-guided agent on three self-terminating design tasks with live CAD/CAE solvers, spanning single-objective and coupled multi-physics settings. On the single-objective design, the ontology agent removes 9.32% of the starting mass compared with 3.72% for the baseline agent, with all stress and displacement limits satisfied. On the coupled multi-physics case, it reaches its best design at 91% of the starting mass, while the baseline finishes 23% heavier than the part it began with. The two agents differ in strategy rather than in effort: the Ontology-guided agent adds structure through ribs, while the Baseline agent adds bulk. Together, these experiments establish where in the inference loop current frontier models are effective, and where an explicit engineering vocabulary changes what the agent searches for.