AI Integration

Using Invariant with LLMs and AI agents.

Overview

Invariant's validation gate is designed to work with AI assistants and LLM-based analytics tools. When an AI generates queries, Invariant provides:

1. Guardrails — Prevent semantically invalid operations
2. Explanations — Structured error messages the AI can understand
3. Remediations — Actionable steps to fix issues

Why AI integration matters

LLMs are good at generating syntactically correct queries but often make semantic mistakes:

• Summing percentages
• Comparing incompatible datasets
• Ignoring boundary changes

Invariant catches these errors before they reach users.

Integration topics

Topic	What it covers
Tool Contracts	Structured interfaces for AI tools
Remediation Actions	Safe mutations an AI can apply

Quick example

# AI generates a query
query = ai_agent.generate_query(user_request)

# Invariant validates
result = kernel.validate_query(query)

if result.is_blocked:
    # Feed issues back to AI for self-correction
    corrected_query = ai_agent.fix_query(
        query,
        issues=result.issues,
        remediations=result.remediations
    )
    result = kernel.validate_query(corrected_query)

Design principles

Machine-readable errors: All issues include structured codes, evidence, and remediations.

Safe remediations: Remediation actions are bounded and auditable.

Context slices: AI tools can request minimal context needed for a decision.