AI Database Q&A Analyst Agent Template

• What it does: Translates business questions into bounded read-only SQL and returns validated answers, tables, and charts.
• Best for: analysts, product teams, startups, operations teams, and non-technical business users.
• Apps used: Supabase, PostgreSQL.
• Setup time: 10 to 15 minutes after connecting Supabase or PostgreSQL.

Last verified from workflow config on 2026-08-20. Includes exact connector scopes, common failure modes, and fixes.

The AI Database Q&A Analyst turns plain-language business questions into safe, read-only SQL, validated answers, tables, and charts. This template acts as a starting point: open any agent and use Use AI to help draft or update its instructions—adding new agents or connecting new tools is done manually in the canvas.

The problem this template solves

• Business users wait for: simple answers because every question must be translated into SQL manually.
• Generated queries run before: the schema, grain, joins, tenant boundary, or sensitive fields are checked.
• Charts and conclusions get: produced without validating nulls, duplicates, totals, or join inflation.

How do you build a natural language to SQL agent yourself?

A reliable natural language to SQL agent needs schema inspection, a business glossary, text-to-SQL translation, a read-only policy, bounded query execution, result validation, and chart generation. The method must keep source evidence, calculations, recommendations, and external actions distinct so a user can review what happened.

You can build this with Claude Code, Codex, or a custom stack. You would own authentication, schemas, retries, storage, approval state, testing, and ongoing connector maintenance. This template starts with those workflow decisions already represented in an editable Agent Build.

If the adjacent job is a better fit, the Product Analytics and Reporting Analyst template provides a related starting point without forcing this agent beyond its actual scope.

Setup guide

Step 1: Clone the template into the right Project

Open the template gallery from Agent Space, choose AI Database Q&A Analyst, and clone it into the Project that holds the relevant customer context. The workflow opens as an editable draft with its agents, notes, tools, and installed skills.

Step 2: Review Project context and complete first-run setup

Open Run and start a new conversation with Database Q&A Analyst. The agent checks read-only Project files and its reusable setup record before asking only for unresolved choices from the installed Answer Database Questions with Safe SQL method.

Step 3: Connect only the apps this workflow needs

Open Agent Build, inspect each tool node, and connect the required accounts for Supabase, PostgreSQL. Review the allowlisted actions shown in the Apps used table below, and avoid granting broader write access than the template needs.

Step 4: Confirm goals, limits, and approval policy

Review the saved setup fields, reporting window, output preferences, cost limits, and any external-write policy. Keep analysis and draft work available by default, and require explicit approval for any live side effect described in the workflow guardrails.

Step 5: Test a normal prompt and an edge case

Run Which plans had the highest churn last quarter, and how did you define churn? Then open a clean conversation and test Explain the ambiguity in tenant and churn definitions before running any query. Inspect tool calls, files, human-input pauses, and the final answer in the trace before changing instructions.

Step 6: Publish the tested draft

Return to Build, save the final configuration, then publish it from Deploy after the draft test behaves as expected. Use Playground for ongoing work and Agent Space to watch live status or requests that need human input.

Common issues and fixes

• A required connector is disconnected: Open Tools and Skills, connect the account in the same Project, and confirm the intended actions remain allowlisted.
• Project context is incomplete: Add or update the relevant brand, goal, policy, glossary, or source file at Project scope, then ask the agent to refresh setup instead of guessing.
• The requested action is unsupported: Keep the local analysis or draft, state the connector gap, and do not claim a live write without a returned identifier.
• The result uses the wrong window or target: Confirm the saved setup, source period, locale, identity, and output goal before rerunning a cost-bearing or external step.

Customization knobs

• selected connector and database/project: Review and update this value through the first-run setup flow when the Project changes.
• business glossary and metric definitions: Review and update this value through the first-run setup flow when the Project changes.
• allowed schemas, tables, and columns: Review and update this value through the first-run setup flow when the Project changes.
• join keys and timezone conventions: Review and update this value through the first-run setup flow when the Project changes.
• sensitive/regulated fields to exclude: Review and update this value through the first-run setup flow when the Project changes.
• default row and execution limits: Review and update this value through the first-run setup flow when the Project changes.

Who this template is for

• Business operations teams: Ranked from natural language to sql for business (0 US searches/month in the evaluated dataset). The workflow fits because it can translate business questions into safe, explainable database analysis across Supabase or PostgreSQL and return validated answers and charts.
• Data analysts: Ranked from ai sql for analysts (0 US searches/month in the evaluated dataset). The workflow fits because it can translate business questions into safe, explainable database analysis across Supabase or PostgreSQL and return validated answers and charts.
• Startups: Ranked from ai sql for startups (0 US searches/month in the evaluated dataset). The workflow fits because it can translate business questions into safe, explainable database analysis across Supabase or PostgreSQL and return validated answers and charts.
• Teams evaluating text-to-SQL tools: Ranked from text to sql tool (0 US searches/month in the evaluated dataset). The workflow fits because it can translate business questions into safe, explainable database analysis across Supabase or PostgreSQL and return validated answers and charts.

What the agent does

This template can:

• Load setup, glossary, allowed: schema scope, and prior validated query patterns.
• Inspect available table/schema metadata: before writing SQL.
• Restate the business question: as definitions, grain, filters, joins, and validation checks.
• Write a bounded SELECT/CTE: query with explicit columns, date filters, and LIMIT where appropriate.
• Use the Supabase read-only: query action when selected; for PostgreSQL use find/query actions and enforce read-only SQL in policy.
• Inspect row counts, nulls: duplicates, totals, and obvious grain/join errors before interpreting results.
• Use code interpreter for: calculations and charts after data is returned; never expect it to connect to the database.
• Save the question, SQL: connector result metadata, validation, chart, and plain-language answer.

How the workflow works

1. Load setup, glossary, allowed schema scope, and prior validated query patterns.
2. Inspect available table/schema metadata before writing SQL.
3. Restate the business question as definitions, grain, filters, joins, and validation checks.
4. Write a bounded SELECT/CTE query with explicit columns, date filters, and LIMIT where appropriate.
5. Use the Supabase read-only query action when selected; for PostgreSQL use find/query actions and enforce read-only SQL in policy.
6. Inspect row counts, nulls, duplicates, totals, and obvious grain/join errors before interpreting results.
7. Save the evidence, approvals, files, and final result for review.

This is a single-agent workflow. The main agent owns research, planning, tool use, approvals, files, and the final response without passing routine stages between specialists.

Requirements and setup inputs

Use the template in a Project that contains the relevant business context. Connect only the accounts required for Supabase, PostgreSQL. The agent reads Project files before asking questions and stores confirmed reusable setup at agent scope.

• selected connector and database/project: Confirm this only when it is not already available from Project context, public evidence, or a saved setup record.
• business glossary and metric definitions: Confirm this only when it is not already available from Project context, public evidence, or a saved setup record.
• allowed schemas, tables, and columns: Confirm this only when it is not already available from Project context, public evidence, or a saved setup record.
• join keys and timezone conventions: Confirm this only when it is not already available from Project context, public evidence, or a saved setup record.
• sensitive/regulated fields to exclude: Confirm this only when it is not already available from Project context, public evidence, or a saved setup record.
• default row and execution limits: Confirm this only when it is not already available from Project context, public evidence, or a saved setup record.
• approved query and export policy: Confirm this only when it is not already available from Project context, public evidence, or a saved setup record.

No template-wide sample-data artifact is installed. The template installs its reusable methods and setup.md through skills, then creates customer-specific profiles, reports, charts, exports, and receipts from authorized evidence. Generated files can be inspected in Playground.

Apps and tools used

Connector actions are scoped to this Agent Build. A Project connection does not automatically grant every agent or every action access.

Practical use cases

First setup and baseline

Business operations teams can use the template to translate business questions into safe, explainable database analysis across Supabase or PostgreSQL and return validated answers and charts. The saved setup and evidence files reduce repeated onboarding while keeping unknowns visible.

Best for: business operations teams.

Focused analysis or production run

Data analysts can use the template to translate business questions into safe, explainable database analysis across Supabase or PostgreSQL and return validated answers and charts. The saved setup and evidence files reduce repeated onboarding while keeping unknowns visible.

Best for: data analysts.

Ongoing review and iteration

Startups can use the template to translate business questions into safe, explainable database analysis across Supabase or PostgreSQL and return validated answers and charts. The saved setup and evidence files reduce repeated onboarding while keeping unknowns visible.

Best for: startups.

Safe edge-case handling

Teams evaluating text-to-SQL tools can use the template to translate business questions into safe, explainable database analysis across Supabase or PostgreSQL and return validated answers and charts. The saved setup and evidence files reduce repeated onboarding while keeping unknowns visible.

Best for: teams evaluating text-to-sql tools.

Example prompts and outputs

Example prompt: core workflow

Which plans had the highest churn last quarter, and how did you define churn?

Example output: The executed SQL, definitions, validation checks, result limitations, plain-language answer, and optional chart. The actual numbers, sources, files, and connector receipts depend on the user's authorized data.

Example prompt: safety or limitation check

Explain the ambiguity in tenant and churn definitions before running any query.

Expected behavior: the agent follows this boundary, preserves useful local work, and does not claim an unsupported or unapproved external action.

Why use this template

• Research happens before questions: the agent inspects Project context, saved setup, and available evidence before requesting unresolved choices.
• The method stays maintainable: detailed repeatable steps live in human-readable skills instead of one oversized agent prompt.
• External actions stay reviewable: connector scopes, approval boundaries, and returned identifiers are explicit.
• Files carry the detail: reports, evidence, drafts, charts, and receipts remain inspectable instead of being lost in a long chat response.
• The workflow remains editable: teams can refine one agent, skill, tool scope, or handoff at a time and retest it.

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