Agents don't query like your detections do

The post argues that AI agents are changing security information and event management workloads by generating large volumes of unpredictable, chained queries that differ from the fixed, scheduled searches for which many SIEM platforms were optimized. It contends that long retention periods, inaccessible archived data, slow searches, finite AI context windows, and always-on compute costs can cause agents to return incomplete conclusions or make broad investigations expensive. The author contrasts Splunk approaches such as SmartStore and federated S3 search with Imply Lumi, a product built on Apache Druid, claiming that its concurrent-query architecture, schema discovery, indexed storage, elastic compute pools, and support for SPL and SQL better suit agent-driven investigations. The post also says Lumi can preserve existing Splunk consoles, searches, and dashboards while lowering storage and search costs, citing BTG Pactual as an example, and concludes that organizations should prioritize response time across their complete retention window rather than only their hot data tier.

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