How to Build SQL Agents That Actually Work: Why Evaluations Matter

In the world of software, we don't ship code without unit tests. Yet, in the rush to deploy Generative AI, many teams are still "vibe-checking" their way to production.

We’ve all been there: You’re demoing a new AI assistant to stakeholders. You ask, “Show me our revenue trends for Q3.” The agent searches your BI tools, finds the right dashboard, and everything looks perfect. Then comes the follow-up: “How much of that came from new vs. returning customers?” The agent attempts a SQL query and—Error. A column name is slightly off, or the agent lacks the context to join two specific tables. The room goes quiet. The "aha" moment is replaced by a "look into it" explanation.

This is the reliability gap. To bridge it, we need more than better prompts; we need Evaluations. These are structured tests that define what an AI agent should do, how it should do it, and what a correct outcome looks like. They serve as a repeatable way to measure agent performance against real business expectations, rather than relying on one-off demos or subjective judgment.

Why data products are the foundation of reliable agents

To build a trustworthy SQL agent, you first need a data product. In Alation, a data product acts as the "knowledge layer"—a curated guide that describes your data to the agent. It provides the necessary context, like table definitions, column descriptions, and business logic, that the agent uses to write SQL queries.

However, a data product is only as good as the instructions it provides. This is where evaluations come in. Evaluations allow you to explicitly define what the agent must accomplish, creating a feedback loop that ensures your knowledge layer is actually doing its job.

Defining the gold standard with evaluation cases

Every agent built in Alation’s Agent Studio includes an Evaluations feature. This allows you to define a set of "evaluation cases" that serve as the foundation for improvement. These cases represent the "gold standard"—how an agent should respond when faced with specific business questions.

An evaluation case typically includes:

• Input: A natural language request (e.g., "How many unique customers placed orders last month?").

• Expected output: The desired result, which varies depending on the task.

  • SQL generation: A specific, executable query.

  • Search: A list of expected artifacts or specific search terms.

  • Summarization: Guidelines or example summaries the agent should follow.

Alation makes it easy to define these cases manually in the UI, via file upload, or even with the help of an agent to audit and update cases. When you run an evaluation, Alation simulates a fresh chat for every case, comparing the agent's output against your defined standards to identify exactly where the logic breaks down.

Case study: Optimizing a SQL agent from scratch

To see this in practice, we looked at a common customer goal: enabling automated question-answering across a sales dataset (orders, products, customers, and locations).

We started with a "naked" data product—one that contained only raw table and column definitions with no metadata or descriptions.

The baseline run

We uploaded 20 question-SQL pairs to benchmark our starting point. The query agent, relying solely on raw metadata, managed a 60% accuracy rate (12 out of 20 correct). It struggled with nuance, like understanding specific date-filtering logic or table granularity.

The power of "Suggest Improvements"

Because evaluations provide a consistent baseline, we can use the Suggest Improvements feature to automate the "tuning" process.

This feature analyzes the failed evaluation runs and suggests specific changes to the data product—such as better descriptions or specific SQL instructions—to resolve those failures. The transformation to 100% accuracy took just two automated iterations:

• Iteration 1: The agent analyzed the failures and suggested updated descriptions and guidance on how to filter date fields correctly. This resulted in 5 additional correct answers.

• Iteration 2: With 3 failures remaining, the agent noticed the SQL agent failed to understand the tables’ granularity. It updated the knowledge layer’s descriptions to show how rows should be aggregated correctly.

This resulted in three more correct answers, bringing our accuracy to 100%. To be sure, we ran the evaluation a second time and confirmed the result. This process doesn't just happen in a black box; Alation shows the exact changes made to the data product for human review and approval, ensuring you remain in control of the knowledge layer.

Building agentic products that stakeholders actually trust

Evaluations are no replacement for human feedback, but they are a great starting point. Just like test-driven development, creating all evaluation cases ahead of time is difficult, if not impossible.  As users ask new questions and your data evolves, your evaluation sets must grow with them. This prevents your data products from becoming stale and unreliable over time.

The "aha!" moment for builders is realizing that you don't have to manually tune prompts for eternity. By using Alation to build your SQL agents with evaluations, you transition from a fragile demo to a purpose-built tool that provides verified, repeatable ROI.

Ready to see how evaluations can bridge the reliability gap for your team? Book a demo today!