How Should Text2SQL Fit Inside an Agent System? Practical Guide for Interviews

1. Common Misconception: Text2SQL Is Not an Agent

Text2SQL should be viewed as a capability component, not as an autonomous agent that makes decisions, manages dialogue flow, or provides fallback explanations.

2. Correct Positioning of Text2SQL in Agent Architecture

A standard Agent system consists of three layers:

Decision layer (Agent / Planner)

Capability layer (Tools / Function Calls)

Execution layer (Database / API / External systems)

Text2SQL belongs exclusively to the capability layer.

Text2SQL = a Tool that can be called by the Agent

3. Text2SQL as a Function Call

In practice, Text2SQL is exposed as a function:

text2sql(question: string) -> { sql: string, rows: array }

Key points:

Input is a natural‑language question.

Output is a structured result (SQL string and result rows).

The tool never returns a natural‑language answer directly.

Tool provides facts; Agent provides expression.

4. When Does the Agent Decide to Call Text2SQL?

The decision is driven by intent and data shape. The Agent checks:

Is the question a structured query?

Does it involve explicit fields, conditions, or ordering?

Is a reproducible result required?

If all conditions are satisfied, the Agent invokes Text2SQL; otherwise it falls back to RAG or plain generation.

5. End‑to‑End Agent + Text2SQL Call Flow

User Input

市值最大的 5 只银行股有哪些？

Agent Decision Logic

The Agent recognises a structured query and decides to call Text2SQL.

Function Call

{
  "name": "text2sql",
  "arguments": {
    "question": "市值最大的5只银行股"
  }
}

Text2SQL Internal Steps

Combine schema and question into a prompt.

Generate the SQL statement.

Execute the SQL and return rows.

SELECT stock_name, market_cap
FROM stocks
WHERE industry = '银行'
ORDER BY market_cap DESC
LIMIT 5;

Tool Returns Result

{
  "sql": "...",
  "rows": [
    ["工商银行", 15000],
    ["建设银行", 14000],
    ...
  ]
}

Agent Next Steps

Interpret the rows.

Check completeness.

Decide whether another tool is needed.

Generate the final natural‑language answer.

市值最大的银行股是工商银行，其次是建设银行……

6. Multi‑Turn Dialogues and Text2SQL

Case 1: Incomplete Parameters

When the user asks “帮我查一下 PE 比较低的股票”, the Agent should first ask for missing details instead of calling Text2SQL.

Agent detects insufficient parameters.

Agent asks clarification.

User provides missing info.

Agent calls Text2SQL.

Text2SQL only handles queries with fully specified conditions.

Case 2: Parameter Completion Over Multiple Turns

user: 帮我查一下 PE 比较低的股票
assistant: 请问 PE 低于多少？
user: 低于 10
assistant: 是否限定行业？
user: 银行
assistant: 调用 text2sql

7. Hard Boundary Rules Between Agent and Text2SQL

Text2SQL does not perform intent detection.

Text2SQL does not ask for missing parameters.

Text2SQL does not converse with the user.

Text2SQL does not provide fallback explanations.

Text2SQL does not decide whether to query.

All these responsibilities belong to the Agent.

8. Handling Text2SQL Failures

Typical failure modes include SQL syntax errors, empty results, or missing data. Text2SQL should return a failure status.

{
  "success": false,
  "error": "no such column: market_value"
}

The Agent then decides to retry, modify the query, inform the user, or fall back to RAG or human assistance.

Fallback logic belongs to the Agent, not the Tool.

9. How to Explain This in an Interview

Use the following concise script:

In an Agent system, Text2SQL is usually wrapped as a Function Call that only translates a clear natural‑language query into SQL and executes it. The Agent decides when to invoke it, handles missing parameters through multi‑turn clarification, and is responsible for interpreting results, fallback handling, and final response generation. This separation keeps responsibilities clear, makes the system stable, and shows you are building a system rather than merely stitching modules together.