Turning Observability into a Tunable Search Space

In the Mlops world, people have long used DAGs/graphs or at least the consensus has been that best practice was to use them. With AI and agents, the types of graphs used for orchestration or instrumentation are more varied, but they share the same core idea: capturing and tracking the artifacts produced by a pipeline, along with their parent and child relationships. The reason for this is intuitive: a common pattern across data, ML, and agent pipelines is a sequence of steps that can be represen