yoda-mcp

# ADR-0004: World-Class Validation Framework ## Status Accepted ## Context The Planner MCP system requires a validation framework to ensure all generated plans meet "world-class" quality standards before delivery to users. The system must be able to: 1. **Assess Plan Quality**: Evaluate completeness, accuracy, feasibility, and best practices 2. **Enforce Standards**: Reject plans that don't meet minimum quality thresholds 3. **Provide Feedback**: Give specific, actionable improvement suggestions 4. **Support Enhancement**: Enable automatic plan improvement through validation feedback 5. **Scale Performance**: Handle high-volume validation without becoming a bottleneck The challenge is defining "world-class" in measurable terms and creating a validation system that can objectively assess plan quality across diverse domains and requirements. ## Decision We will implement a **Comprehensive Validation Framework** with the following components: ### 1. 5-Tier Quality Certification System - **WORLD_CLASS (81-100)**: Exceptional quality, comprehensive, innovative - **ENTERPRISE (61-80)**: Excellent quality, scalable, well-architected - **PROFESSIONAL (41-60)**: High quality, complete, tested, documented - **STANDARD (21-40)**: Good quality, functional, basic requirements met - **BASIC (0-20)**: Minimal quality, incomplete, needs significant improvement ### 2. Multi-Dimensional Validation Engine - **Completeness Validation**: Ensures all requirements are addressed - **Technical Validation**: Verifies implementation feasibility and accuracy - **Best Practices Validation**: Checks adherence to industry standards - **Quality Scoring**: Quantitative assessment across multiple criteria - **Enhancement Suggestions**: Provides specific improvement recommendations ### 3. Pluggable Validation Rules - **Core Rules**: Universal validation criteria for all plans - **Domain-Specific Rules**: Specialized validation for different technology stacks - **Custom Rules**: Organization-specific quality standards - **Rule Priority System**: Weighted importance for different validation criteria ### 4. Validation Gateway - **Mandatory Gate**: All plans must pass validation before delivery - **Quality Thresholds**: Configurable minimum quality requirements - **Enhancement Loop**: Automatic plan improvement when standards aren't met - **Override Mechanism**: Admin override for exceptional circumstances ## Architecture Diagram ``` ┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐ │ Plan Request │───▶│ Orchestration │───▶│ MCP Servers │ └─────────────────┘ │ Engine │ └─────────────────┘ └──────────┬───────┘ │ ┌──────────▼───────┐ │ Generated Plan │ └──────────┬───────┘ │ ┌──────────▼───────┐ │ VALIDATION │◄─── Validation Rules │ GATEWAY │ ┌─────────────┐ └──────────┬───────┘ │ Core Rules │ │ │Domain Rules │ ┌───────────▼────────────┐│Custom Rules │ │ │└─────────────┘ NO │ Meets World-Class │ ◄───┤ Standards? │ │ │ │ │ └───────────┬────────────┘ │ │ YES │ ▼ ┌───────▼────────┐ ┌────────────────┐ │ Enhancement │ │ Certified Plan │ │ Engine │ │ Delivery │ └───────┬────────┘ └────────────────┘ │ ▼ ┌──────────────────┐ │ Improved Plan │ │ (retry validation)│ └──────────────────┘ ``` ## Implementation Details ### Validation Engine Core ```typescript interface ValidationEngine { async validatePlan(plan: ComprehensivePlan, context: ValidationContext): Promise<ValidationResult>; } class WorldClassValidationEngine implements ValidationEngine { private rules: ValidationRule[] = []; private enhancementEngine: EnhancementEngine; async validatePlan(plan: ComprehensivePlan, context: ValidationContext): Promise<ValidationResult> { const results: ValidationRuleResult[] = []; // Execute all validation rules for (const rule of this.rules) { const result = await rule.validate(plan, context); results.push(result); } // Calculate overall score and quality tier const overallScore = this.calculateOverallScore(results); const qualityTier = this.determineQualityTier(overallScore); return { passed: qualityTier >= QualityTier.WORLD_CLASS, qualityTier, overallScore, ruleResults: results, enhancementSuggestions: this.generateEnhancements(results), certification: this.generateCertification(qualityTier, results) }; } } ``` ### Quality Scoring Algorithm ```typescript interface QualityMetrics { completeness: number; // 0-100: Requirements coverage technical_accuracy: number; // 0-100: Technical feasibility best_practices: number; // 0-100: Industry standards adherence implementation_detail: number; // 0-100: Implementation specificity innovation: number; // 0-100: Creative and innovative solutions maintainability: number; // 0-100: Long-term sustainability scalability: number; // 0-100: Growth and scale considerations security: number; // 0-100: Security best practices performance: number; // 0-100: Performance optimization documentation: number; // 0-100: Documentation quality } class QualityScorer { // Weighted scoring algorithm private static readonly WEIGHTS: Record<keyof QualityMetrics, number> = { completeness: 0.20, // 20% - Most critical technical_accuracy: 0.18, // 18% - Very important best_practices: 0.15, // 15% - Industry standards implementation_detail: 0.12, // 12% - Actionability security: 0.10, // 10% - Non-negotiable baseline scalability: 0.08, // 8% - Future-proofing performance: 0.08, // 8% - Efficiency maintainability: 0.05, // 5% - Long-term care innovation: 0.02, // 2% - Bonus points documentation: 0.02 // 2% - Communication }; calculateScore(metrics: QualityMetrics): number { let totalScore = 0; for (const [metric, value] of Object.entries(metrics)) { const weight = this.WEIGHTS[metric as keyof QualityMetrics]; totalScore += value * weight; } return Math.round(totalScore); } } ``` ### Validation Rules #### Core Validation Rules 1. **Completeness Rule**: Ensures all user requirements are addressed 2. **Feasibility Rule**: Verifies technical implementation is possible 3. **Clarity Rule**: Checks for clear, unambiguous instructions 4. **Consistency Rule**: Ensures internal plan consistency 5. **Completeness Rule**: Validates requirement coverage #### Domain-Specific Rules 1. **Security Rule**: Validates security best practices 2. **Performance Rule**: Checks performance considerations 3. **Scalability Rule**: Ensures scalable architecture patterns 4. **Testing Rule**: Validates testing strategy inclusion 5. **Documentation Rule**: Checks documentation completeness #### Quality Tier Thresholds ```typescript enum QualityTier { BASIC = 0, // 0-20 points STANDARD = 21, // 21-40 points PROFESSIONAL = 41, // 41-60 points ENTERPRISE = 61, // 61-80 points WORLD_CLASS = 81 // 81-100 points } const WORLD_CLASS_THRESHOLD = 85; // Minimum score for certification ``` ## Validation Process Flow ### 1. Pre-Validation Preparation - Analyze request context and requirements - Select appropriate validation rule set - Configure quality thresholds based on user tier ### 2. Multi-Pass Validation - **Pass 1**: Core structural validation (completeness, consistency) - **Pass 2**: Technical accuracy and feasibility validation - **Pass 3**: Best practices and quality standards validation - **Pass 4**: Enhancement opportunity identification ### 3. Quality Assessment - Calculate dimensional scores across all criteria - Apply weighted scoring algorithm - Determine overall quality tier - Generate detailed feedback report ### 4. Enhancement Integration - If quality doesn't meet standards, trigger enhancement engine - Apply automatic improvements based on validation feedback - Re-validate enhanced plan (maximum 3 iterations) - Ensure continuous quality improvement ## Consequences ### Positive 1. **Quality Assurance**: Guarantees high-quality plan delivery 2. **Objective Standards**: Measurable, consistent quality criteria 3. **Continuous Improvement**: Automatic plan enhancement capabilities 4. **User Confidence**: Users know they're getting validated, high-quality plans 5. **Competitive Advantage**: "World-class" certification differentiates our service 6. **Scalable Quality**: Automated validation scales with system growth 7. **Feedback Loop**: Validation data improves overall system quality over time ### Negative 1. **Increased Latency**: Validation adds processing time to plan generation 2. **System Complexity**: Additional validation layer increases architectural complexity 3. **Resource Usage**: CPU and memory overhead for validation processing 4. **False Negatives**: Risk of rejecting actually good plans due to validation limitations 5. **Maintenance Overhead**: Validation rules need continuous refinement and updates 6. **Over-Engineering Risk**: May over-complicate simple planning requests ### Mitigation Strategies - **Performance Optimization**: Parallel validation rule execution - **Caching**: Cache validation results for similar plan patterns - **Adaptive Validation**: Lighter validation for simple requests - **Continuous Monitoring**: Track validation accuracy and adjust rules - **User Feedback**: Incorporate user feedback to improve validation accuracy ## Quality Examples ### World-Class Plan Characteristics (Score: 85+) - ✅ Complete requirement coverage (100%) - ✅ Detailed implementation roadmap with timelines - ✅ Security considerations throughout - ✅ Performance optimization strategies - ✅ Comprehensive testing approach - ✅ Scalability and maintainability plans - ✅ Risk assessment and mitigation strategies - ✅ Clear documentation and communication - ✅ Innovation and creative problem-solving - ✅ Industry best practices integration ### Enterprise Plan Characteristics (Score: 61-80) - ✅ Good requirement coverage (80%+) - ✅ Solid technical implementation approach - ✅ Basic security considerations - ✅ Performance awareness - ✅ Testing strategy included - ⚠️ Limited scalability considerations - ⚠️ Minimal risk assessment - ⚠️ Standard documentation level ### Professional Plan Characteristics (Score: 41-60) - ✅ Adequate requirement coverage (60%+) - ✅ Technically feasible approach - ⚠️ Basic security mentions - ⚠️ Limited performance considerations - ⚠️ Basic testing approach - ❌ No scalability planning - ❌ No risk assessment ## Alternatives Considered ### 1. Manual Quality Review **Description**: Human reviewers assess plan quality **Rejected Because**: - Doesn't scale with high request volume - Subjective quality assessments - High labor costs and slow turnaround - Inconsistent quality standards across reviewers ### 2. Simple Threshold Validation **Description**: Basic pass/fail validation with minimal criteria **Rejected Because**: - Doesn't provide nuanced quality assessment - No enhancement suggestions - Doesn't support continuous improvement - Can't differentiate quality levels ### 3. AI/ML-Based Quality Assessment **Description**: Machine learning models trained on quality examples **Rejected Because**: - Requires large training datasets - Black-box decision making - Difficult to explain validation decisions - Model drift and maintenance challenges ### 4. Peer Review System **Description**: Plans reviewed by other system users **Rejected Because**: - Introduces delays in plan delivery - Quality varies with reviewer expertise - Privacy and security concerns - Not suitable for real-time validation ## References - [Software Quality Metrics](https://www.iso.org/standard/35733.html) - ISO/IEC 25010 - [Quality Attributes in Software Architecture](https://resources.sei.cmu.edu/library/asset-view.cfm?assetID=513908) - [Validation vs Verification](https://www.guru99.com/verification-v-validation-in-a-software-testing.html) - [Quality Gates in CI/CD](https://docs.sonarqube.org/latest/user-guide/quality-gates/) - [Architecture Quality Attributes](https://www.oreilly.com/library/view/software-architecture-patterns/9781491971437/) --- **Author**: Architecture Team **Date**: 2024-01-15 **Reviewed By**: Engineering Leadership, Quality Assurance Team **Implementation Status**: Complete