Cribl introduces new Cribl Search with agentic telemetry

Cribl introduced a new generation of Cribl Search, an AI-native log intelligence solution built on an agentic telemetry architecture to support security and IT operations for AI-driven workloads.

The company said the new Search experience helped organizations cut log management costs, resolve security and IT incidents faster, and streamline tooling by unifying human-generated context with log ingest, storage, and analysis across both Cribl-managed and external data stores for AI-speed query volumes without compromising performance or cost.

Cribl described agentic telemetry as a design that combined AI-ready infrastructure, agentic Artificial Intelligence (AI), machine telemetry, and human-generated data. The product used AI-powered parsing to normalize data at ingest and to fuse logs, metrics, and traces with tickets, runbooks, pull requests, Slack, and other collaboration and change systems, and it supported federated analysis across local datasets and external stores.

The company said Search remained schema-agnostic and could operate across Open Cybersecurity Schema Framework (OCSF), OTLP, Elastic Compute Service (ECS), and custom schemas, and that teams could power Search with their own approved AI model instead of a Cribl-managed backend. Cribl also said it had introduced enhancements to continuously improve performance since Search was initially introduced.

“The age of agentic AI is here, driving expectations for productivity sky-high, but legacy log platforms are simply not equipped to handle the exponential data and query growth from AI agents.” said Clint Sharp, co-founder & CEO of Cribl.

“The growth of telemetry data has outpaced company resources and legacy SIEMS have not kept up with AI because the cost of normalizing data ahead of time is prohibitive,” said Francis Odum, cybersecurity researcher at Software Analyst Cyber Research.