The document discusses using systems intelligence and artificial intelligence/neural networks to enhance semiconductor electronic design automation (EDA) workflows by collecting telemetry data from EDA jobs and infrastructure and analyzing it using complex event processing, machine learning models, and messaging substrates to provide insights that could optimize EDA pipelines and infrastructure. The approach aims to allow both internal and external augmentation of EDA processes and environments through unsupervised and incremental learning.