AI Agent for SQL Preparation & Review
Generated SQL that runs is not the same as SQL that is right. This agent writes against your actual schema, checks the query for the mistakes that return a plausible wrong number, estimates what it will scan, and explains in plain language exactly what it counts.
What is an AI SQL analyst agent?
An AI SQL analyst agent is a governed software worker that prepares and validates read-only SQL. It grounds each query in the live catalogue, tests the draft for join fan-out, null exclusion and grain errors, estimates scan cost, and returns a plain-language reading of the population and grain the query produces.
What it does
What it is not
The query ran, returned a number, and the number was wrong
SQL fails loudly when the syntax is broken and silently when the logic is. A join against a table with repeated keys quietly triples a total, a filter on a nullable column drops the rows that mattered, and the result looks exactly like a correct answer because there is nothing on the screen to suggest otherwise.
Fan-out inflates the total
Joining to a table at a finer grain multiplies rows, and the sum that follows is confidently wrong by a factor nobody notices.
Nulls disappear silently
A predicate on a nullable column excludes the rows where it is null, and those are often the ones the question was about.
Generated SQL invents columns
A model writing from memory produces a column name that sounds right, fails, and then produces another that exists but means something else.
Cost is discovered at runtime
The query is correct and scans a year of an unpartitioned table, which is discovered by the warehouse bill rather than by review.
Written against the schema, checked before it runs
Grounding
Every Column Is One That Exists
Schema read, not remembered.
Tables, columns, types, keys and partition layout are read from the live catalogue before a line is written, so the query refers to objects that exist with the types they actually have, and ambiguity between similarly named columns is resolved rather than guessed.
- Catalogue read before drafting
- Types and keys verified, not assumed
- Similar column names disambiguated
- Partition and clustering keys respected
From the catalogue
Validation
The Checks That Catch Silent Errors
Grain, nulls, and duplication.
Before the query is offered, the agent tests whether each join preserves grain, whether any predicate silently excludes nulls, whether aggregation happens at the intended level, and whether a distinct count is hiding duplicated keys.
Before execution
Explanation
What This Query Actually Counts
In a sentence a reviewer can check.
Each query is returned with a plain-language reading of the population it selects, the grain it produces and the rows it deliberately excludes, so a reviewer can disagree with the logic without having to read the SQL line by line.
Population and grain
How the AI SQL Analyst Agent runs a task
- STEP 01
Read the catalogue
Table definitions, column types, declared keys, partitioning and any documented relationships are retrieved first, because a query written from an assumed schema is the single most common source of confidently wrong output.
Catalogue readKey discovery - STEP 02
Establish the grain
The intended unit of the answer is fixed before joins are written — one row per customer, per order, per day — so that every subsequent join can be judged against whether it preserves that unit or silently multiplies it.
Grain definitionJoin planning - STEP 03
Draft and self-check
The query is written and then examined for the failure modes that do not raise an error: fan-out from a non-unique key, predicates that exclude nulls, aggregation above or below the intended level, and duplicate-hiding distincts.
Draft SQLFan-out testNull audit - STEP 04
Cost it before running
Estimated scan volume is checked against partition and clustering keys, and where a rewrite would prune substantially more without changing the result, that alternative is offered alongside the original.
Cost estimatePruning rewrite - STEP 05
Explain it in words
The final query is returned with a description of the population selected, the grain produced, the exclusions applied and the time basis used, so it can be reviewed by someone who reads business logic rather than SQL.
Plain-language readingAssumption list
Systems the AI SQL Analyst Agent connects to
Schema and execution
Validation
Inputs, outputs and runtime
- Ingests
- Stated information needLive schema catalogueDeclared keys and partitionsAccess grantsExisting query to review
- Produces
- Read-only queryPlain-language query readingValidation check resultsEstimated scan costAssumptions forced by schema
- Triggered by
- Analyst requestQuery review requestCost investigation
- Human oversight
- A reviewer confirms the logic before reliance
- Models
- Open-weight LLMs you host — Llama, Qwen or Mistral class
- Typical latency
- Seconds to draft, longer to validate
- Deployment
- On-premise or sovereign cloud with egress control
- Data residency
- Schema and results remain in your warehouse
Where the SQL Analyst Agent pays back
Ad Hoc Query Drafting
Turn a defined information need into a correct query against tables the requester is entitled to read.
Query Review
Check an existing query for fan-out, null exclusion and grain errors before its output reaches a report.
Reconciling Two Numbers
Compare two queries that should agree and identify the join or filter that makes them differ.
Cost Reduction
Rewrite an expensive query to use partition pruning and the available clustering without changing what it returns.
Self-Service Enablement
Let a non-SQL colleague state a need and receive a query with a plain-language explanation to check.
Schema Orientation
Explain how a set of unfamiliar tables join, at what grain, and which keys are actually unique.
AI SQL Analyst Agent vs chatbots and SaaS copilots
Text-to-SQL demos succeed on a schema with eleven tables and clean names; the reason they disappoint in production is that real warehouses have non-unique keys, nullable columns that matter, and four tables that look like the right one.
| Generic chatbot | SaaS copilot | VDF AI | |
|---|---|---|---|
| Schema knowledge | Guessed | Partial metadata | Read from the live catalogue |
| Join correctness | Unchecked | Unchecked | Grain and fan-out tested |
| Null handling | Ignored | Ignored | Exclusions surfaced explicitly |
| Cost awareness | None | None | Scan estimated before running |
| Explanation | Restates the SQL | Comments | Population and grain in words |
| Write statements | Will generate | Will generate | Blocked by the connection |
| Schema exposure | Pasted to vendor | Vendor tenancy | Never leaves your network |
Governance and controls
A query agent is only safe if the boundary is enforced by the credential rather than by a prompt, because an instruction not to write is exactly the kind of constraint that natural-language input erodes.
Read-only connection
Write statements fail at the driver
Grants inherited
Same visibility as the requesting user
Statement logging
Every executed query is recorded
Cost ceiling
Large scans require a human decision
No credential storage
Connections use brokered identities
Masked columns respected
Warehouse masking policies still apply
Evidence it leaves behind
What changes after rollout
Who runs the AI SQL Analyst Agent
Analytics engineer
Reviews a draft that has already been tested for the errors most likely to survive review, and gets the join-grain reasoning written down rather than having to derive it from the query text.
Operations manager without SQL
States what they need in business terms and receives both a query and a description of exactly which rows it will count, which is the part they can actually verify against how the business works.
Data platform owner
Sees analytical demand served through a read-only path with every statement logged and costed, rather than through an expanding set of individual warehouse credentials nobody has reviewed since issue.
Questions about the AI SQL Analyst Agent
What is an AI SQL analyst agent?
It is an agent that prepares read-only SQL properly: reading your live catalogue before writing, checking the draft for join fan-out, null exclusion and grain errors, estimating scan cost, and explaining in plain language what the query counts.
How is an AI SQL analyst agent different from a generic chatbot?
A general model writes SQL from a remembered idea of what your schema might contain. This agent reads the catalogue first and then tests the draft for the specific mistakes that return a wrong number without failing.
Can an AI SQL analyst agent run on-premise on warehouse schema and query data?
Yes. Your schema is itself commercially descriptive — table and column names reveal products, segments and processes — so both the catalogue and the results stay inside your own environment.
What does an AI SQL analyst agent produce, and in what format?
A read-only query, a plain-language reading of what it selects and at what grain, the validation checks it passed, an estimated scan cost, and a note of any assumption the schema forced.
Where does an AI SQL analyst agent fit in a governed AI programme?
It prepares the statement, not the conclusion. Interpreting results belongs to the data analyst agent, metric governance to the BI agent, and nothing it produces can write to your data.
Can it create tables, views or run an update?
No. The connection it uses is read-only at the database level, so a write statement fails at the driver regardless of what the agent was asked to do. This matters more than it sounds: instruction-level restrictions on a natural-language system are negotiable under adversarial or simply confused input, and a credential is not.
How does it know whether a join will fan out?
It checks uniqueness on the join key rather than trusting the declared constraint, because in most warehouses a primary key on paper and a unique column in practice are different things. Where the key is not unique it says so, states the multiplier that results, and proposes the aggregation or qualification that restores the intended grain.
Does it work with dbt models and a semantic layer?
Yes, and it prefers them. Where a governed model or metric already defines the logic, that definition is used rather than a fresh derivation, and the query is written against the model layer instead of the raw tables underneath. Rewriting business logic that has already been agreed and tested is a reliable way to produce a second, competing number.
What happens with a very large or expensive query?
Scan volume is estimated before execution and compared against the ceiling you configure. Above it, the query is returned with the estimate and a rewrite that prunes more aggressively, and running the original becomes a human decision. Warehouse spend is a real constraint, and an agent that could quietly issue full-table scans on demand would be an expensive one.
How is this different from the data analyst agent?
The division is between the statement and the question. This agent is accountable for the query being correct, cheap and comprehensible against the schema as it exists. The data analyst agent is accountable for whether the question was the right one, whether rival explanations survive, and what the result actually supports. Correct SQL answering the wrong question is a common and entirely separate failure.
Check the query before it produces a number
See the AI SQL Analyst Agent draft and validate a query against your live schema.