QED Taxonomy Guide

Comprehensive classification system for AI development patterns.

Domain Classification

Patterns are organized by functional domain:

Architecture & Design

Core system design patterns and architectural decisions.

System structure and components
Design patterns and principles
Scalability and performance architecture

Implementation & Development

Practical implementation patterns and development practices.

Coding patterns and techniques
Framework usage and integration
Development workflows

Operations & Maintenance

Production operations and system maintenance patterns.

Deployment and scaling
Monitoring and observability
Performance optimization

Security & Compliance

Security patterns and compliance requirements.

Authentication and authorization
Data protection and privacy
Regulatory compliance

Team & Process

Collaboration patterns and team processes.

Team workflows and communication
Knowledge sharing and documentation
Organizational integration

Quality & Validation

Quality assurance and validation patterns.

Testing strategies
Risk assessment and mitigation
Quality metrics and standards

Risk Profile Classification

Patterns are classified by risk level using a traffic light system:

🟢 Low Risk (Green)

Characteristics:

Well-established patterns
Minimal security implications
Easy to reverse or modify
Suitable for all contexts

Examples:

Documentation generation
Code formatting
Simple refactoring

🟡 Managed Risk (Yellow)

Characteristics:

Requires some expertise
Moderate security considerations
Needs monitoring and controls
Context-dependent implementation

Examples:

Team collaboration patterns
Integration with existing systems
Performance optimization

🔴 High Risk (Red)

Characteristics:

Complex implementation
Significant security implications
Difficult to reverse
Requires extensive validation

Examples:

Multi-agent orchestration
Production deployment patterns
Compliance implementations

Context Classification

Patterns are tagged for organizational context:

Startup Context

Characteristics:

Rapid iteration focus
Minimal governance
Resource constraints
High risk tolerance

Pattern Selection:

Prioritize velocity
Minimize overhead
Focus on MVP
Embrace experimentation

Mid-Market Context

Characteristics:

Balancing growth and stability
Emerging governance needs
Team scaling challenges
Moderate risk tolerance

Pattern Selection:

Gradual process introduction
Team collaboration focus
Scalability planning
Selective governance

Enterprise Context

Characteristics:

Complex governance requirements
Multiple stakeholder management
Integration complexity
Low risk tolerance

Pattern Selection:

Compliance first
Comprehensive documentation
Change management focus
Proven patterns only

Regulated Industries

Characteristics:

Strict compliance requirements
Audit trail necessity
Data sovereignty concerns
Zero risk tolerance

Pattern Selection:

Regulatory compliance mandatory
Security by default
Extensive validation
Conservative approach

Maturity Classification

Patterns progress through maturity levels:

Experimental

Status: Under active development

Limited production usage
Rapid changes expected
Early adopter feedback
High uncertainty

Emerging

Status: Gaining adoption

Some production usage
Stabilizing interfaces
Growing community
Moderate confidence

Validated

Status: Production proven

Widespread usage
Stable interfaces
Well-documented
High confidence

Mature

Status: Industry standard

Universal adoption
Extensive tooling
Best practices established
Very high confidence

Pattern Metadata Structure

Each pattern includes standardized metadata:

---
pattern_id: unique-identifier
title: Human-readable title
domain: Architecture|Implementation|Operations|Security|Team|Quality
risk_profile: Low|Managed|High
maturity: Experimental|Emerging|Validated|Mature
contexts:
  - startup
  - mid_market
  - enterprise
  - regulated
tags:
  - collaboration
  - automation
  - security
dependencies:
  - pattern_id_1
  - pattern_id_2
author: Original author
date_created: YYYY-MM-DD
date_updated: YYYY-MM-DD
version: 1.0.0
---

Using the Taxonomy

For Pattern Discovery

Start with context - Identify your organizational context
Consider risk tolerance - Determine acceptable risk level
Browse by domain - Find patterns in relevant domains
Check maturity - Prefer validated/mature patterns
Review dependencies - Ensure prerequisites are met

For Pattern Documentation

Use the template - Start with Pattern Template
Apply metadata - Include all classification fields
Be specific - Clear problem and solution statements
Include examples - Practical implementation guidance
Document trade-offs - Honest assessment of limitations

For Pattern Evaluation

Risk assessment - Evaluate against risk profile
Context fit - Match to organizational needs
Maturity check - Consider stability requirements
Dependency analysis - Ensure feasibility
ROI calculation - Estimate implementation value

Evolution and Governance

Pattern Lifecycle

Proposal - New pattern identified
Evaluation - Initial assessment and classification
Experimental - Limited testing and feedback
Validation - Broader adoption and refinement
Maturity - Widespread usage and stability
Deprecation - Obsolescence and replacement

Classification Updates

Taxonomy classifications are updated based on:

Production usage evidence
Community feedback
Security assessments
Compliance reviews
Performance metrics

Contribution Guidelines

When contributing patterns:

Use standardized metadata format
Provide evidence for classifications
Include implementation examples
Document known limitations
Reference related patterns

Quick Reference

By Risk Tolerance

Conservative (Regulated/Enterprise):

Low risk patterns only
Mature/Validated maturity
Extensive documentation required

Moderate (Mid-Market):

Low and Managed risk patterns
Validated/Emerging maturity
Balanced documentation

Aggressive (Startup):

All risk levels acceptable
Any maturity level
Minimal documentation