UX Invariants for AI-powered Software Engineering

Observed Failure Modes in LLM-Generated Interfaces

LLMs can generate functional UI code in seconds. They also reliably:

• Break interaction consistency
• Ignore accessibility edge cases
• Introduce silent UX regressions
• Optimize for syntax rather than human behavior

Speed without guardrails compounds UX debt.

UX Invariants as Guardrails

UX Invariants are non-negotiable rules that define what must always be true in a user interface,regardless of framework, stack, or AI agent.

• Behavioral guardrails for LLMs
• A shared UX contract between humans and machines
• Lightweight UX governance without a design system

This is UX as infrastructure.

What You Get

A curated, production-tested set of UX invariants expressed in plain Markdown, designed for direct use in AI-assisted workflows.

Includes invariants covering:

• Core interaction behavior
• Accessibility and affordances
• Error handling and recovery
• System state, feedback, and latency
• Common UX failure modes and anti-patterns

No tooling lock-in. No proprietary formats.

Where It Fits

UX Invariants operate below prompts and above code.

• Context during LLM code generation
• Agent instructions (AGENTS.md)
• PR review and human QA rubrics
• CI-adjacent validation (manual or automated)

Start enforcing UX at generation time. Harden enforcement later if needed.

Why Invariants (Not Guidelines)

Guidelines are optional. Invariants are enforced.

• Deterministic expectations
• Clear failure conditions
• Easier evaluation by humans and machines
• Stable across prompts, tools, and agents

If a rule can’t be evaluated, it will be ignored.

Why Not Just Ask an AI to Generate UX Rules?

You can. What you’ll get is a plausible-sounding list of guidelines,many of which will be vague, unenforceable, or harmful in production.

UX Invariants are not generated. They are distilled from repeated failure, enforced tradeoffs, and long feedback loops.

The value is not the words. It’s the judgment encoded in what’s included,and what’s deliberately excluded.

AI can generate rules. It can’t tell you which ones survived reality.

Who It’s For

• Solopreneurs shipping AI-generated UI without a design team
• Technical founders balancing speed and product quality
• Senior engineers responsible for long-term UX integrity

If you use LLMs to build interfaces, these invariants are for you.

About the Author

Authored by a senior frontend engineer with over 25 years of experience designing, building, and maintaining production user interfaces.

These UX invariants are distilled from real failure modes encountered across decades of UI development,now amplified by AI-generated code.

The goal is simple: enable speed without surrendering usability.

Licensing & Pricing Signals

UX Invariants are licensed, not subscribed to.

• One-time purchase
• Stable, versioned rule set
• Intended for production systems
• Individual, team, and organization licenses

This is infrastructure-grade UX judgment, not consumable content.

Invariant Excerpts

Example invariant structure, rendered as a spec-like block.

Invariant: Primary actions are never hidden behind hover-only controls.

Rationale: Hover-only affordances fail on touch devices and are invisible to keyboard users.

Violation: A table shows actions only on row hover, with no alternative access.

Enforcement: Provide a visible action column or contextual menu trigger at all times.

Ship AI-Generated UI Without Shipping UX Regressions

Enforce UX invariants now. Formalize enforcement later.