Design a conversational analytics agent that answers business questions over a data warehouse in natural language.
'What was revenue by region last quarter?' looks like text-to-SQL, yet production analytics agents break on ambiguity, wrong joins, and confidently wrong numbers. Here is the architecture that makes the answers trustworthy.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
'What was revenue by region last quarter?' looks like text-to-SQL, yet production analytics agents break on ambiguity, wrong joins, and confidently wrong numbers. Here is the architecture that makes the answers trustworthy.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
