Photo by Patrick Tomasso on Unsplash You want your AI agent to understand places; ports, roads, buildings, construction sites. OpenStreetMap is the obvious source. But feeding raw OSM data to an agent is like giving someone a raw database dump and asking for insights. It's possible, but painful. We spent the last few weeks building a pipeline that turns OSM into clean RDF knowledge graphs. It's not a polished product; it's a working prototype that taught us a lot. This post shares the important steps, the mistakes we made, and the code that actually works. What you'll get from this post: Why bash scripts fail silently (and how to fix it) How to generate RDF from OSM without losing your mind A working Python pipeline you can adapt Real examples from maritime and construction extraction Let's dive in. The problem with OSM + bash Our first version was simple: a bash script that calls osmconvert to clip a region, then osmfilter to extract categories like transportation, buildings, and POIs.…