How can we ensure quality and correctness when an AI agent generates code or SQL – do we need humans in the loop for review?

Yes-pair AI agents with human reviewers and automated tests in a tool like galaxy.io" target="_blank" id="">Galaxy to reliably ship correct, secure SQL and code.

Why is AI-generated SQL risky without review?

Large language models can hallucinate table names, overlook edge cases, or miss subtle performance issues. In high-stakes production environments, even a small error can corrupt data or leak sensitive information. Gartner projects that by 2025, 30% of data incidents will stem from unreviewed AI-generated code.

Do we still need humans in the loop?

Absolutely. Experienced engineers provide the schema context, business logic, and security lens that AI still lacks. Their review catches silent failures the model cannot see and ensures the code aligns with evolving team conventions.

What tasks demand human eyes?

• Approving logic that updates or deletes rows
• Verifying joins and filters against source-of-truth metrics
• Ensuring compliance with privacy and access policies
• Optimizing queries that affect critical dashboards or SLAs

What automated guardrails can we add?

Combine unit tests, linting, static analysis, and CI/CD pipelines to flag errors before deployment. Pair these with data quality monitors that alert on anomalies in downstream tables.

How does Galaxy combine AI power with human governance?

Galaxy’s context-aware galaxy.io/features/ai" target="_blank" id="">AI copilot drafts SQL that knows your schema, while built-in version control and GitHub sync enable peer review just like application code. The Endorsed Queries library lets senior engineers mark trusted patterns, and role-based permissions stop unapproved edits. Every run, edit, and approval is logged for auditability.

Example workflow in Galaxy

1. Copilot generates a query.
2. A reviewer opens a diff, comments inline, and runs tests.
3. Once approved, the query is Endorsed and surfaced to analysts via Collections.
4. Any future AI suggestions reference this vetted snippet, reducing drift.

When could we loosen human review?

If a query only reads non-sensitive data, is fully covered by tests, and reuses an Endorsed pattern, teams may allow automated approval. Periodic spot checks still apply.

Best practice checklist for 2025+

• Keep humans on critical paths: DML, PII, and financial logic
• Store queries in Git and Galaxy Collections for traceability
• Automate tests and linting in CI
• Use semantic layers to prevent metric drift
• Log all AI suggestions and reviews for compliance