Custom AI Agents for Data Analytics: What It Takes to Build One That Works

Custom AI agents for data analytics are presented as a way to make AI-driven analysis more reliable, repeatable, and governed than general-purpose assistants, which can produce inconsistent results, struggle with complex enterprise warehouses, and require users to expose schema or sensitive business context outside established security controls. Effective analytics agents require carefully scoped access to governed data sources, semantic definitions and metric context, orchestration for multi-step querying and error correction, explicit limits on read and write actions, human approvals for consequential changes, output validation, and detailed audit logs. The article argues that agents should operate under existing warehouse permissions, use narrowly defined tools, apply retry limits and checkpoints, and validate SQL and results before business users rely on them. It describes Sigma as a platform intended to provide much of this infrastructure through warehouse-native governance, workbook-based workflow configuration, scoped actions, validation, and auditable writebacks to Input Tables, while supporting data warehouses such as Snowflake, Databricks, BigQuery, and Redshift and configurable AI model providers.

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