The Observability Dataset: Architecture That Takes Agents From Junior to Senior

AI agents in observability fail not because of model limitations but because of poor data architecture. When telemetry is unstructured and ungoverned, even capable agents behave like junior engineers guessing blindly. The solution is logical segmentation: organizing telemetry into scoped, governed datasets (Dataspaces and Datasets in Coralogix) that give agents clean schemas, scoped context, governed access, pre-aggregations, and queryable meta-context. This architecture compounds over time — each agent investigation can write back summary datasets that make future queries faster and more precise, creating a platform that gets smarter with use rather than degrading as data grows.