I Built a Quality Control System for AI Code Generation

I shipped broken code three times in one week. Not edge cases—fundamental errors that any test would have caught. The AI said “should work” and I believed it.

Building a quality control system for AI code generation means enforcing mandatory gates before implementation begins—loading relevant skills, validating context budget, and blocking rationalization phrases like “should work” that indicate unverified claims. The result is a two-gate system where tools literally cannot execute until quality checks pass.

Why Did I Need Quality Gates for AI?

The problem wasn’t the AI’s capability. Claude is remarkably good at generating code. The problem was my workflow—or lack of one.

I’d describe what I wanted. Claude would write it. I’d paste it in. Sometimes it worked. Sometimes I’d spend hours debugging issues that existed from the first line. Without me realizing it, I was trusting confidence over evidence.

That specific anxiety of deploying something you haven’t tested—the kind where you refresh the page three times hoping the error goes away—became my default state.

Well, it’s more like… I was using AI as a code generator when I needed it to be a quality-controlled collaborator.

How Does the Two-Gate System Work?

The system enforces two mandatory checks before any tool can execute. Like buttoning a shirt from the first hole—skip it, and everything else is wrong.

Gate 0: Meta-Orchestration (Priority 0)

This gate loads immediately and handles three things:

Gate 1: Auto-Skill Activation (Priority 1)

This gate analyzes your query and activates relevant skills:

I love automation. But I spend hours building systems to slow myself down.

What Is Progressive Disclosure and Why Does It Save 60% of Tokens?

Most Claude configurations load everything upfront. Every skill, every rule, every example—thousands of tokens consumed before you’ve even asked a question.

Progressive disclosure flips this. Load metadata first. Load details on demand.

The 3-Tier System

Tier 1: Metadata (~200 tokens)

• Skill name, triggers, dependencies
• Just enough to route the query

Tier 2: Schema (~400 tokens)

• Input/output types
• Constraints and quality gates
• Tools available

Tier 3: Full Content (~1200 tokens)

• Complete handler logic
• Examples and edge cases
• Only loaded when actively using the skill

The meta-orchestration skill alone: 278 lines at Tier 1, 816 with one reference, 3,302 fully loaded. That’s 60% savings on every session that doesn’t need the full content.

What Phrases Does the System Block?

The automated verification system flags specific patterns in code comments and commit messages. Here’s the complete breakdown of phrases that indicate insufficient testing or assumptions:

These phrases aren’t banned because they’re wrong. They’re banned because they indicate claims without evidence.

That hollow confidence of claiming something works without checking—the system makes it impossible.

How Does AMAO Handle Parallel Execution?

AMAO (Adaptive Multi-Agent Orchestrator) adds sophisticated orchestration on top of the gate system:

DAG Engine

• Directed acyclic graph for task dependencies
• Max 50 tasks with cycle detection
• Parallel grouping for independent operations
• Critical path analysis for optimization

Context Governor

• 75% max budget, 60% warning threshold, 20% reserve
• Predictive usage analysis
• Auto-compact at 70%
• Phase unloading to release memory between stages

Skill Evolution

• Pattern detection: 5 occurrences triggers skill proposal
• Auto-approval at 85% confidence
• Deprecation at 30% effectiveness
• Weighted feedback: 40% build, 30% test, 20% reverts, 10% user

The parallel execution runs up to 3 concurrent tasks with a 5-minute timeout. If parallel fails, it falls back to sequential—safety over speed.

What Are the 4 Pillars of Quality?

Every check maps to one of four pillars:

1. State & Reactivity

• Svelte 5 runes only ($state, $props, $derived)
• No legacy patterns that cause confusion
• State updates via $effect for side effects

2. Security & Validation

• All user input sanitized (XSS prevention)
• Form inputs validated with Zod
• API routes validate request schema
• No inline scripts in production

3. Integration Reality

• Every component used in at least one route
• No orphaned utility files
• All API routes consumed by UI
• Every feature has verification

4. Failure Recovery

• Error boundaries on all route groups
• Graceful degradation for failed API calls
• Loading states for async operations
• User-friendly error messages

FAQ: Building Quality Systems for AI Code Generation

What is a two-gate system for AI code generation? A two-gate system enforces quality checks before any implementation begins. Gate 0 loads meta-orchestration and validates context budget. Gate 1 activates relevant skills based on your query. Both must pass before tools are unblocked.

How much do token savings matter with progressive disclosure? Progressive disclosure saves 60% of tokens by loading skill metadata first (~200 tokens), then schemas on demand (~400 tokens), then full content only when needed (~1200 tokens). This prevents context overflow on long sessions.

Why block phrases like ‘should work’ in AI development? Phrases like ‘should work’ and ‘probably fine’ indicate unverified claims. Blocking them forces evidence-based completion—actual build output, test results, or screenshots before marking work complete.

Can I implement this system for my own Claude Code setup? Yes. Start with a CLAUDE.md file that enforces gate checks. Add hooks for UserPromptSubmit (skill activation) and Stop (build verification). The meta-orchestration plugin pattern works for any codebase.

What’s the difference between AMAO and Cortex 2.0? AMAO handles orchestration—parallel execution, context budgeting, skill evolution. Cortex 2.0 handles skill definitions with 3-tier progressive disclosure. They work together: AMAO decides what to run, Cortex defines how skills work.

I thought I needed better prompts. Well, it’s more like… I needed better systems around the prompts. The AI was always capable. I just needed guardrails that made “should work” impossible to say.

Maybe the goal isn’t to trust AI more. Maybe it’s to trust evidence—and build systems that make evidence the only path forward.

Related Reading

This is part of the Complete Claude Code Guide. Continue with:

• Context Management - Dev docs workflow that prevents context amnesia
• Evidence-Based Verification - Why “should work” is the most dangerous phrase
• Token Optimization - Save 60% with progressive disclosure
• What is RAG? - Foundational concept behind context-aware AI

Written by Chudi Nnorukam

I design and deploy agent-based AI automation systems that eliminate manual workflows, scale content, and power recursive learning. Specializing in micro-SaaS tools, content automation, and high-performance web applications.

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