François Scharffe: An Agentic Harness for Knowledge Engineers – Episode 58

François Scharffe

As AI unleashes a host of new tools and agents to help build knowledge graphs, engineers like François Scharffe are tasked with harnessing these new capabilities and deploying them effectively.

François brings decades of AI experience to the goal he set for himself earlier this year: to automate his own job. He's making good progress, but he's also finding that there's still plenty of need for him and other humans in the workflows he's designing.

We talked about:

the upcoming 2027 edition of the Knowledge Graph Conference

his long history of working with various kinds of AI

the distinctions between knowledge engineering and software engineering

the rationales behind his choice of Pi as his coding harness

an overview of his harness set-up and knowledge engineering workflow

the benefits to knowledge graph building that AI brings

the variety of tools and agents he has built for tasks like requirements specification, data sourcing, ontology design, term extraction and re-use, entity resolution, data mappings, ETL pipelines, etc.

the evaluation processes he's using to assess the benefits of his new workflows