In the emerging agentic analytics, the fundamental unit of computation is no longer a SQL query but an agentic workflow: a DAG of agent operations that compose SQL queries, object fetches, LLM\/VLM invocations, and multi-round subagent interactions. These workflows are computationally explosive\u2014a single seven-node financial-analysis template applied to 160 stock symbols generates over 800 GPU-resident LLM\/VLM calls with overlapping prefixes\u2014yet existing systems treat each call in isolation. We present Lumilake, an agentic analytics engine that models workflows as first-class, introspectable DAGs and addresses three challenges: (i) LLM-aware query optimization that consolidates shared prefixes to maximize KV-cache reuse, (ii) a multi-tenant runtime that dispatches heterogeneous operators across mixed hardware with cross-tenant deduplication, and (iii) governance by design with semantic lineage at every agent step. Deployed at the National University of Singapore, Lumilake achieves up to 3.8\u00d7 speedup over Ray on six real workflows from clinical decision support, materials spectroscopy, and financial analysis on commodity GPUs. Our engine is open-sourced at https:\/\/github.com\/mlsys-io\/lumilake.<\/p>\n","protected":false},"excerpt":{"rendered":"
In the emerging agentic analytics, the fundamental unit of computation is no longer a SQL query but an agentic workflow: a DAG of agent operations that compose SQL queries, object fetches, LLM\/VLM invocations, and multi-round subagent interactions. 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