What if your Airflow DAG could orchestrate robots, thermal chambers, and silicon tests, not just code?

Silicon validation labs rely on scarce, stateful physical resources: robotic handlers, DUT boards, thermal/power
systems, instruments, and shared hardware queues. Teams often coordinate these via spreadsheets and ad hoc
reservations, causing contention, idle gaps, conflicts, poor observability, and slow triage.

This talk presents a closed-loop orchestration model where Apache Airflow is the control plane for a software-defined
validation lab. A central DAG coordinates robotic handling, thermal/power setup, stress and performance runs, and
parametric characterization on hosts connected to silicon. It continuously ingests hardware health, measurements, and
test outcomes, then feeds results into AI-assisted analysis to choose the next physical action: refine parameters,
schedule follow-up experiments, or trigger mitigation.

Using Edge workers on dedicated lab machines, we replace manual coordination with reliable, auditable orchestration.
The same pattern extends beyond silicon to robotics labs, device farms, and other cyber-physical environments.