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Software Design Document (SDD)

Autonomous AI Curriculum Development System​

1. Introduction​

1.1 Purpose​

This document provides detailed software design specifications for the Autonomous AI Curriculum Development System, a production-grade framework for automated educational content generation using Claude's multi-agent capabilities.

1.2 Scope​

The system automates curriculum development across 480 content opportunities (15 types × 4 levels × 8 modules) with 95% template reusability and zero manual intervention.

1.3 Definitions​

  • TaskExecutor: Autonomous execution engine for Claude Task protocol
  • Content Types: concepts, tutorials, examples, exercises, implementations, projects, etc.
  • Skill Levels: beginner, intermediate, advanced, expert
  • Agent Coordination: Multi-agent workflow orchestration

2. System Architecture​

2.1 High-Level Architecture​

2.2 Component Architecture​

Core Framework Components​

  1. Template Engine (01_content_structure_analyzer.py)
  2. Workflow Generator (02_agentic_curriculum_automation.py)
  3. Orchestration Manager (03_orchestrated_curriculum_automation.py)
  4. Pattern Unifier (04_unified_reusable_automation_framework.py)

Execution Engine Components​

  1. TaskExecutor (core/task_executor.py)
  2. Content Generator (autonomous/generate_content_autonomous.py)
  3. Quality Validator (autonomous/validate_quality_autonomous.py)

Specialized Tools​

  1. NotebookLM Optimizer (tools/notebooklm_optimizer.py)

3. Detailed Design​

3.1 TaskExecutor Engine​

class TaskExecutor:
    def execute_task(task_prompt: str, agent_type: str) -> Dict[str, Any]
    def execute_task_sequence(task_list: List[Dict]) -> Dict[str, Any]
    def get_progress_report() -> Dict[str, Any]

Key Features:

  • Autonomous Task protocol execution
  • Progress tracking with JSON persistence
  • Retry logic with configurable attempts
  • Error handling and graceful fallbacks

3.2 Content Generation Pipeline​

3.3 Quality Assurance Framework​

Validation Criteria:

  • Technical accuracy (30% weight)
  • Pedagogical effectiveness (25% weight)
  • Clarity and structure (20% weight)
  • Completeness (15% weight)
  • Consistency (10% weight)

Minimum Thresholds:

  • Technical accuracy: 85%
  • Overall quality: 80%

4. Interface Specifications​

4.1 CLI Interface​

# Content generation
python autonomous/generate_content_autonomous.py \
  --content-type concepts \
  --module module1_foundations \
  --skill-level beginner \
  --batch

# Quality validation  
python autonomous/validate_quality_autonomous.py \
  --content-dir generated_content \
  --batch

# NotebookLM optimization
python tools/notebooklm_optimizer.py \
  --content-file content.md \
  --output-dir optimized/

4.2 API Specifications​

TaskExecutor Interface​

execute_task(
    task_prompt: str,      # Task description for Claude agent
    agent_type: str,       # Specialized agent type
    task_id: Optional[str], # Unique identifier
    max_retries: int = 3   # Retry attempts
) -> Dict[str, Any]        # Execution result

Content Generator Interface​

generate_single_content(
    content_type: str,     # Type of content to generate
    module: str,           # Target module
    topic: str,            # Specific topic
    skill_level: str       # Target skill level
) -> Dict[str, Any]        # Generation result

5. Data Design​

5.1 Content Specification Schema​

{
  "content_type": "concepts",
  "module": "module1_foundations", 
  "topic": "mathematical_foundations",
  "skill_level": "beginner",
  "agent": "ai-curriculum-specialist",
  "expected_deliverable": "concept explanation document"
}

5.2 Progress Tracking Schema​

{
  "session_start": "2025-11-09T12:00:00Z",
  "tasks_completed": [],
  "tasks_failed": [],
  "current_task": null,
  "completion_rate": 0.85
}

6. Implementation Details​

6.1 Error Handling Strategy​

  • Retry Logic: 3 attempts with exponential backoff
  • Graceful Degradation: Manual execution fallback
  • Error Logging: Comprehensive error tracking
  • Recovery Mechanisms: State restoration capabilities

6.2 Performance Considerations​

  • Template Caching: Pre-loaded pattern templates
  • Parallel Execution: Concurrent task processing
  • Progress Persistence: Regular state saving
  • Memory Management: Efficient content handling

6.3 Security Measures​

  • Input Validation: Parameter sanitization
  • File Path Security: Directory traversal prevention
  • Error Sanitization: Sensitive information filtering
  • Access Control: File permission management

7. Quality Attributes​

7.1 Reliability​

  • 95% Success Rate: Automated execution reliability
  • Error Recovery: Automatic retry mechanisms
  • State Persistence: Progress tracking resilience

7.2 Performance​

  • Template Reusability: 95% pattern efficiency
  • Execution Speed: Optimized agent coordination
  • Resource Usage: Efficient memory and storage

7.3 Maintainability​

  • Modular Design: Loosely coupled components
  • Clear Interfaces: Well-defined API contracts
  • Documentation: Comprehensive usage guides

8. Deployment Architecture​

8.1 Environment Requirements​

  • Python 3.8+
  • Claude Code integration
  • File system access
  • JSON processing capabilities

8.2 Configuration Management​

  • Environment-specific settings
  • Agent configuration parameters
  • Quality thresholds and criteria
  • Progress tracking preferences

9. Testing Strategy​

9.1 Unit Testing​

  • Component-level functionality validation
  • Mock Claude agent responses
  • Error condition testing

9.2 Integration Testing​

  • End-to-end workflow validation
  • Agent coordination testing
  • Quality pipeline verification

9.3 Performance Testing​

  • Load testing with multiple concurrent tasks
  • Memory usage profiling
  • Execution time optimization

10. Future Enhancements​

10.1 Planned Features​

  • Real-time dashboard interface
  • Advanced analytics and metrics
  • API integration improvements
  • Batch processing optimizations

10.2 Scalability Considerations​

  • Distributed task execution
  • Cloud-based agent coordination
  • Advanced caching strategies
  • Performance monitoring integration

Document Version: 1.0
Last Updated: 2025-11-09
Status: Production Ready