Most observability platforms were designed for human querying, not AI consumption. For AI agents to effectively analyze telemetry data, four architectural capabilities are needed: an expressive query language (like Coralogix's DataPrime), a living schema that tracks field evolution over time, full-fidelity data access without indexing constraints, and governed semantic domains that scope AI investigations. Without these, AI agents hallucinate queries, miss signals in unindexed data, and produce noisy results. Coralogix frames its own architecture — in-stream processing, customer-owned storage in open Parquet format, and dataset-level governance — as purpose-built for this AI-ready data model, with its Olly agent as the practical expression of these capabilities.
Table of contents
The real AI bottleneckThe shift: from asking a question to conversing with dataWhat “AI-ready” actually means for telemetryFrom asking questions to an intelligence engineWhat this means in practiceThe compounding advantageSort: