Data agents: When enterprise analytics learns to reason
The author's data team was overwhelmed by ad hoc analytics questions across multiple datasets, and manual reporting cycles slowed decisions and overloaded the team.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User submits NL question
A user submits a natural-language question via a chat interface.
Tools used
enterprise AI platformcloud analytics connectorsLakehouse
Outcome
The team shipped a natural-language Q&A system over governed data tables with layered security and role-based access, and validated outputs against manual notebook queries across tested scenarios. Success was defined as reducing time-to-answer and increasing self-serve analytics adoption.
What failed first
During early testing, the agent returned fragmented or duplicated results caused by implicit grouping on internal attributes not requested by the user. After fixing the child agent, the parent orchestrator continued producing old grouped results and falsely claimed the issue was already resolved.
Results
Time savedreducing time-to-answer for common questions