Observability rests on three signals: logs (discrete events), metrics (aggregated numbers), and traces (the path of a request across services). For distributed and AI systems it increasingly also covers token usage and output quality. The principle is simple: you cannot fix what you cannot see.
Open-source observability stacks instrument applications and, more and more, the LLM calls and agents inside them — turning opaque AI behaviour into something you can measure and debug.