AI Builders: Building a text-to-SQL agent

2026-05-26 · Source: Weights & Biases · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Intermediate, medium

Summary

This content details the development of a text-to-SQL agent designed to democratize data analytics by allowing business users to retrieve data and build reports using natural language. The prototype, developed in a Marimo notebook, utilizes DuckDB for querying CSV files representing a fictional online pet retailer's sales data. The agent employs a system prompt to define the database schema and rules, requesting JSON output for SQL, chart type, and Plotly chart configuration. The "Pet Sales Analytics Agent" class integrates an LLM (Anthropic or OpenAI) and uses W&B Weave for observability, tracing all operations from SQL generation to data execution and chart rendering. The process moves from prototype to a production-ready web application, emphasizing continuous evaluation and optimization using Weave for tracking user and agent behavior post-deployment.

Key takeaway

For AI Engineers building data analytics interfaces, integrating text-to-SQL agents can significantly lower data access barriers. You should prioritize a robust prototype phase to refine the agent's workflow and output format, especially for visualization libraries like Plotly. Implement comprehensive observability with tools like W&B Weave from the start to track LLM calls, evaluate performance, and continuously optimize your agent post-deployment, ensuring accuracy and user satisfaction.

Key insights

Text-to-SQL agents democratize data access by translating natural language queries into structured database commands.

Principles

• Define schema and rules clearly for LLM SQL generation.
• Instrument agents for full observability and evaluation.
• Iterate prototypes into production with continuous monitoring.

Method

The workflow involves defining a system prompt with schema and rules, using an LLM to generate SQL, executing it against DuckDB, and then parsing JSON output to render Plotly charts.

In practice

• Use DuckDB for in-memory data querying from CSVs.
• Integrate W&B Weave for LLM trace logging and evaluation.
• Output structured JSON for chart configuration and data visualization.

Topics

• Text-to-SQL
• LLM Agents
• Data Democratization
• DuckDB
• W&B Weave
• Plotly

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Weights & Biases.