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Outcome: a map of the relevant tables, evidence for their grain, and measured join behavior. Start from a connected project, or use the demo warehouse.

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Start with a question

Give your agent a purpose for the exploration:
On a large warehouse, scope the request to a business area before widening it.

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Map, then inspect

Inventory identifies the important objects; the map adds profiles and inferred relationships. Read the map’s skipped objects and elision counts before treating it as a complete inventory of the warehouse. Then profile the relations your question actually reads. On the demo:
Check whether a proposed key is unique, whether it contains NULLs, and whether the join has unmatched rows or multiplies the parent. A declared relationship states the project’s intent; verification measures the data behind that claim.

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Ask the data question

Use explore query for SQL questions so profiling, scope, cost, and PII checks apply. Read the reported truncation and warnings along with the answer.

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Keep the evidence useful

A useful exploration report names the grain, joins that were verified, findings that affect the question, and anything left unmeasured. It distinguishes what the project declares from what the data proves.
These commands include local project declarations and semantic relationships. A declaration can inform a map without proving its assertion against the data.
The result contains Mermaid text under data.mermaid. Your agent can render it. The diagram reads the cached map and does not query the warehouse. Use --full when you need more of the cached objects and columns.

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Continue

Use the evidence to transform a model. For selection rules and measurement detail, read the exploration methodology. For every option, use dex explore.