  Communication networks are becoming increasingly complex towards 6G. Manual
management is no longer an option for network operators. Network automation has
been widely discussed in the networking community, and it is a sensible means
to manage the complex communication network. Deep learning models developed to
enable network automation for given operation practices have the limitations of
1) lack of explainability and 2) inapplicable across different networks and/or
network settings. To tackle the above issues, in this article we propose a new
knowledge-powered framework that provides a human-understandable explainable
artificial intelligence (XAI) agent for network automation. A case study of
path selection is developed to demonstrate the feasibility of the proposed
framework. Research on network automation is still in its infancy. Therefore,
at the end of this article, we provide a list of challenges and open issues
that can guide further research in this important area.
