Learning outcomes include applying agentic AI to realistic multi-step engineering workflows; designing stateful AI systems with durable context, provenance, recovery, and human approval points; evaluating continuity, reliability, completion rate, and intervention rate for non-deterministic systems; and building fluency with tool integration, workflow automation, task state management, and auditability requirements. Currently pursuing or recently completed a PhD in Machine Learning, Computer Science, Electrical or Electronic Engineering, Applied Mathematics, or a related technical field, with experience building and evaluating agent systems.