The problem with "classic" Text-to-SQL
The simplest way to connect an LLM to a database is a single-pass pipeline: the user writes a question in natural language, the model generates a SQL query, and that query is executed directly against the database. This is, for example, the approach followed in freeCodeCamp's tutorial "How to Talk to Any Database Using AI", where a Python script builds a prompt with the table schema and asks a model to return raw SQL.
The problem is that this pipeline is fragile. If the model generates a query with a syntax error, or worse, a syntactically valid but semantically incorrect query, there's nobody reviewing the result before showing it to the user. There's no error correction, no verification that the answer actually makes sense.
That's where agents come in. Instead of a model that just "translates" text into SQL, an agent can execute the query, observe the result, and decide whether it needs to fix something before responding. That's exactly what smolagents, Hugging Face's library for building agents with very little code, does.
What is smolagents?
