Agentic workflows in data analysis: Architectural approaches and how to build them

Agentic workflows in data analysis use AI agents to detect conditions, reason through multi-step investigations, and take or recommend actions, extending beyond dashboards and conversational assistants that depend more heavily on human direction. The article argues that organizations should begin with narrowly scoped, measurable use cases such as variance investigations, data-quality checks, or forecast reviews, typically using a single-agent or pipeline architecture before adopting more complex multi-agent, orchestrated, or event-driven designs. Because agent behavior is probabilistic and can create security, cost, and reliability risks, it recommends least-privilege data access, deny-by-default tool allowlists, human approval for irreversible or high-impact actions, staged autonomy, failure-focused testing, and complete audit trails. It also describes Sigma as a warehouse-native runtime platform that applies existing warehouse, user, workbook, and agent-level permissions to live data access, supports configurable agent actions and approval modes, and records agent-driven writebacks for review and compliance.

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