As product complexity rises, simulation must evolve from isolated analysis to a scalable, decision-making capability. This session explores agentic engineering workflows powered by Engineering AI and Physics AI — enabling guided, physics-grounded autonomy that scales simulation insight across NPI while preserving human oversight and engineering accountability.
Simulation has long informed critical engineering decisions, yet in many organizations it remains fragmented and expert-bound rather than embedded across the full NPI lifecycle. As development cycles compress and complexity increases, the constraint is no longer access to simulation — but coordination, confidence, and speed of decision-making.
In this session, Alex Graham outlines the rise of agentic engineering workflows: AI-driven systems that orchestrate simulation tasks, guide decision-making, and enable intelligent hand-offs across product development. Combining Engineering AI with Physics AI, these workflows determine when simulation is required, select appropriate fidelity, assess simulation quality, and surface trade offs — while keeping engineers firmly in control.
Through real-world examples, the talk explores how physics-grounded AI preserves trust, traceability, and accountability. Agentic workflows are presented not as experimental tools, but as an emerging operating model for simulation-led organizations seeking scalable, high-confidence engineering decisions.