Guides
SQL AI for Agents: Named Views, Not One Query
SQL AI for agents is named intermediate views you can audit—not one giant query. Load tables, step the plan, and reopen the trail before you trust a number.
Read articleName each intermediate SQL view an agent can replay—instead of one giant generated query.
SQL AI for agents is named intermediate views you can audit—not one giant query. Load tables, step the plan, and reopen the trail before you trust a number.
Read articleA SQL statement builder for agents names each intermediate view so a reviewer can point at the broken step. Build one statement, replay it, then take the next join.
Read articleSQL tools for agents should load files, JDBC, and lake objects as tables first. Do not let the model invent a frame in memory and lose the trail.
Read articleInfiniSQL is the agent-native analysis language: load, named views, and single-step review. It is not a dialect tutorial and not a one-shot text-to-SQL translator.
Read articlePandas vs SQL for agents is not a taste debate. Keep joins and grains in SQL so a reviewer can reopen the step. Download a frame only after the table is named.
Read articleSQL scripting for agents is a chain of named views, not a 200-line stored procedure dump. Script three steps, fail one on purpose, and point at the broken view.
Read articlePandas SQL work still belongs in named tables. Letting the model write Python first burns attention on syntax and hides the grain. Keep compute in SQL until export.
Read articleAn AI query for agents is one named step you can replay—not a chat dump. Ask the filter, name the view, then take the next join instead of a one-shot blob.
Read articleSQL GPT writes a query from a prompt. An agent analysis language names each view so a reviewer can point. Do not treat a fluent SELECT as a finished analysis.
Read articleA SQL helper for agents reopens one named view so a reviewer can point. Autocomplete that pastes a CTE stack is not help. Keep the helper on the trail.
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