sf-agent-framework/sf-core/tasks/validation-framework-design.md

AI Agent Orchestration Framework for Salesforce Development - Two-phase architecture with 70% context reduction

# Validation Framework Design Task

This task guides the design and implementation of comprehensive validation
frameworks for Salesforce data quality, ensuring systematic verification of data
integrity, business rules, and system compliance.

## Purpose

Enable data validation engineers to:

- Design scalable validation architectures
- Implement multi-layer validation strategies
- Create reusable validation components
- Build automated validation pipelines
- Establish validation governance

## Prerequisites

- Business rules documented
- Data quality requirements defined
- System architecture understood
- Performance requirements established
- Compliance standards identified

## Validation Framework Architecture

### 1. Framework Layers

**Multi-Layer Validation Model**

```yaml
Presentation Layer:
  Purpose: User input validation
  Components:
    - Field format validation
    - Required field checks
    - Real-time feedback
    - Client-side validation
  Technology:
    - Lightning Web Components
    - Validation rules
    - JavaScript validators
    - HTML5 constraints

Business Logic Layer:
  Purpose: Business rule enforcement
  Components:
    - Cross-field validation
    - Workflow rules
    - Process validation
    - Trigger-based checks
  Technology:
    - Apex triggers
    - Validation rules
    - Flow automation
    - Process Builder

Data Layer:
  Purpose: Data integrity enforcement
  Components:
    - Database constraints
    - Referential integrity
    - Unique constraints
    - Data type validation
  Technology:
    - Schema definition
    - Lookup relationships
    - Master-detail relationships
    - External IDs

Integration Layer:
  Purpose: External system validation
  Components:
    - API validation
    - Message validation
    - Sync verification
    - Error handling
  Technology:
    - REST/SOAP APIs
    - Middleware validation
    - Event-driven validation
    - Batch processing
```

### 2. Validation Components

**Core Framework Elements**

```yaml
Validation Engine:
  Responsibilities:
    - Rule execution
    - Priority management
    - Performance optimization
    - Result aggregation
  Features:
    - Pluggable architecture
    - Async processing
    - Bulk validation
    - Real-time validation

Rule Repository:
  Structure:
    - Business rules catalog
    - Technical rules library
    - Custom rule definitions
    - Rule versioning
  Management:
    - CRUD operations
    - Rule activation/deactivation
    - Impact analysis
    - Change tracking

Result Handler:
  Functions:
    - Error collection
    - Warning management
    - Success tracking
    - Report generation
  Integration:
    - Notification system
    - Logging framework
    - Analytics platform
    - Dashboard updates

Configuration Manager:
  Capabilities:
    - Dynamic configuration
    - Environment-specific settings
    - Feature toggles
    - Performance tuning
  Storage:
    - Custom settings
    - Custom metadata
    - Platform cache
    - Static resources
```

### 3. Validation Patterns

**Common Validation Patterns**

```yaml
Synchronous Validation:
  Use Cases:
    - User data entry
    - API requests
    - Critical business rules
    - Real-time feedback
  Implementation:
    - Validation rules
    - Apex triggers
    - Before-save flows
    - Formula fields

Asynchronous Validation:
  Use Cases:
    - Bulk data loads
    - Complex calculations
    - External system checks
    - Performance-intensive validations
  Implementation:
    - Batch Apex
    - Queueable Apex
    - Platform Events
    - Scheduled jobs

Event-Driven Validation:
  Use Cases:
    - Integration points
    - State changes
    - Milestone events
    - Cross-system validation
  Implementation:
    - Platform Events
    - Change Data Capture
    - Streaming API
    - Apex triggers

Scheduled Validation:
  Use Cases:
    - Periodic audits
    - Compliance checks
    - Data quality reports
    - Trend analysis
  Implementation:
    - Scheduled Apex
    - Scheduled Flows
    - External schedulers
    - Batch processes
```

## Framework Design Process

### Phase 1: Requirements Analysis

**Validation Requirement Gathering**

```yaml
Business Requirements:
  Data Quality Standards:
    - Completeness: 95% required fields filled
    - Accuracy: 99% format compliance
    - Consistency: 100% referential integrity
    - Timeliness: Real-time validation for critical fields

  Compliance Requirements:
    - GDPR: Personal data validation
    - HIPAA: Healthcare data checks
    - SOX: Financial data verification
    - Industry-specific regulations

  Performance Requirements:
    - Response Time: < 100ms for sync validation
    - Throughput: 10,000 records/minute
    - Scalability: Support 10x growth
    - Availability: 99.9% uptime

Technical Requirements:
  Platform Constraints:
    - Governor limits consideration
    - API call limitations
    - Storage limitations
    - Processing time limits

  Integration Points:
    - External system validations
    - Third-party service calls
    - Middleware dependencies
    - Database constraints
```

### Phase 2: Architecture Design

**Framework Blueprint**

```yaml
Component Architecture:
  Core Services:
    ValidationService:
      - Rule execution engine
      - Result aggregation
      - Error handling
      - Performance monitoring

    RuleManager:
      - Rule CRUD operations
      - Rule categorization
      - Priority management
      - Version control

    ConfigurationService:
      - Dynamic configuration
      - Environment settings
      - Feature flags
      - Performance tuning

    NotificationService:
      - Error notifications
      - Alert management
      - Report distribution
      - Dashboard updates

  Supporting Components:
    CacheManager:
      - Rule caching
      - Result caching
      - Configuration caching
      - Performance optimization

    LoggingFramework:
      - Validation logs
      - Error tracking
      - Audit trails
      - Performance metrics

    MonitoringService:
      - Health checks
      - Performance tracking
      - Usage analytics
      - Trend analysis
```

### Phase 3: Implementation Strategy

**Development Approach**

```yaml
Modular Development:
  Core Modules: 1. Base validation engine 2. Rule management system 3. Result processing 4.
    Configuration management

  Extension Modules: 1. Custom validators 2. Integration adapters 3. Reporting components 4.
    Analytics plugins

Testing Strategy:
  Unit Testing:
    - Individual validator tests
    - Rule execution tests
    - Error handling tests
    - Performance tests

  Integration Testing:
    - End-to-end validation flows
    - Cross-module integration
    - External system integration
    - Load testing

  User Acceptance Testing:
    - Business rule validation
    - Performance verification
    - Usability testing
    - Documentation review
```

## Advanced Framework Features

### 1. Intelligent Validation

**Machine Learning Integration**

```yaml
Anomaly Detection:
  Purpose: Identify unusual patterns
  Implementation:
    - Statistical analysis
    - Pattern recognition
    - Threshold learning
    - Adaptive rules

  Use Cases:
    - Fraud detection
    - Data quality issues
    - System anomalies
    - User behavior

Predictive Validation:
  Purpose: Prevent errors before they occur
  Implementation:
    - Historical analysis
    - Trend prediction
    - Risk scoring
    - Proactive alerts

  Benefits:
    - Reduced errors
    - Improved efficiency
    - Better user experience
    - Cost savings
```

### 2. Self-Healing Validation

**Auto-Correction Mechanisms**

```yaml
Correction Strategies:
  Format Corrections:
    - Phone number formatting
    - Date standardization
    - Address normalization
    - Name capitalization

  Data Enrichment:
    - Missing data inference
    - Default value application
    - Lookup completion
    - Calculated fields

  Relationship Repair:
    - Orphaned record handling
    - Duplicate merging
    - Hierarchy fixing
    - Reference updating
```

### 3. Validation Analytics

**Performance Monitoring**

```yaml
Key Metrics:
  Validation Performance:
    - Execution time per rule
    - Success/failure rates
    - Error distribution
    - Resource utilization

  Business Impact:
    - Data quality scores
    - Compliance rates
    - User satisfaction
    - Cost of errors

  System Health:
    - Framework availability
    - Response times
    - Error rates
    - Capacity utilization

Dashboards:
  Executive Dashboard:
    - Overall health score
    - Compliance status
    - Trend indicators
    - Action items

  Operational Dashboard:
    - Real-time monitoring
    - Error queues
    - Performance metrics
    - System status

  Technical Dashboard:
    - Resource usage
    - API metrics
    - Error details
    - Debug information
```

## Framework Governance

### Maintenance and Evolution

**Governance Structure**

```yaml
Change Management:
  Rule Changes:
    - Impact assessment
    - Testing requirements
    - Approval workflow
    - Deployment process

  Framework Updates:
    - Version control
    - Backward compatibility
    - Migration planning
    - Documentation updates

Quality Assurance:
  Code Reviews:
    - Peer review process
    - Automated code analysis
    - Performance review
    - Security review

  Testing Standards:
    - Test coverage requirements
    - Performance benchmarks
    - Security testing
    - User acceptance criteria

Documentation:
  Technical Documentation:
    - Architecture diagrams
    - API documentation
    - Configuration guides
    - Troubleshooting guides

  User Documentation:
    - User guides
    - Training materials
    - Best practices
    - FAQ sections
```

## Best Practices

### Design Principles

- Keep validation logic centralized
- Make rules configurable, not hard-coded
- Design for performance and scale
- Implement comprehensive error handling
- Maintain clear separation of concerns

### Implementation Guidelines

- Use declarative tools when possible
- Implement bulkification for all validators
- Cache frequently used data
- Monitor and optimize performance
- Version control all components

### Operational Excellence

- Establish clear governance processes
- Implement comprehensive monitoring
- Maintain up-to-date documentation
- Conduct regular reviews and audits
- Plan for continuous improvement

## Success Criteria

✅ Framework architecture documented ✅ All validation layers implemented ✅
Performance targets achieved ✅ Governance processes established ✅ Monitoring
and analytics operational ✅ Team trained and documentation complete