Text-to-SQL vs. Decision-Making Skills
Text-to-SQL Answers. Fig Decides.
Text-to-SQL tools turn a question into a query — useful, but it stops at rows on a screen. Fig is the decision-making skills layer: it governs the definitions behind the answer, knows what the answer should be, remembers what happened, and acts within your rules.
Text-to-SQL Question → query engines & semantic modeling | Fig Decision-making skills layer | |
|---|---|---|
| Turns a question into a query | ||
| Consistent, governed metric definitions | ||
| Enforced on every agent (can't bypass) | ||
| Knows what a number should be (expectations) | ||
| Anomaly detection + root cause | ||
| Remembers outcomes and learns | ||
| Acts within governed guardrails |
Fig delivers a governed decision, not just a query.
Text-to-SQL generates a SELECT and hands you rows. Getting the right query is table stakes — it doesn't tell you whether the answer is good, bad, or who's affected.
Fig knows what the answer should be.
A query engine returns whatever the data says. With no expectation or forecast, it can't flag that $1,847 is wrong when you planned for $1,200.
Fig remembers and gets smarter.
Text-to-SQL is stateless — every question starts from scratch. Nothing learns from how past decisions turned out.
Fig can act, safely.
Generating SQL is read-only by design. A decision-making skills layer can draft, alert, and adjust — within policy, authority, budget, and undo.
Questions people ask
What is the difference between text-to-SQL and a decision-making skills layer?
Text-to-SQL translates a natural-language question into a database query and returns rows. A decision-making skills layer turns data into a governed decision: it enforces consistent definitions on every agent, knows what a number should be, detects anomalies and their root cause, remembers outcomes, and acts within guardrails. Text-to-SQL answers; a decision-making skills layer decides.
Isn't accurate text-to-SQL enough for AI analytics?
Accurate SQL is necessary but not sufficient. The query is only the first step. Without shared definitions enforced on every agent, an expectation of what's normal, memory of past outcomes, and a safe way to act, a text-to-SQL tool produces statistically correct answers that can still be operationally wrong — and it can never decide what to do next.
Does a decision-making skills layer replace text-to-SQL?
It absorbs it. Translating a question to a governed query is one capability inside a decision-making skills layer like Fig — but Fig adds the expectations, memory, and governed action that a standalone text-to-SQL engine lacks.
Can text-to-SQL tools learn from outcomes?
Generally no. Most text-to-SQL and semantic-modeling tools are stateless: they map questions to queries but don't capture how a decision turned out or feed it back. Learning from outcomes is a defining capability of a decision-making skills layer.
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