Architecture Decision Records (ADR)
Autonomous AI Curriculum Development System
ADR-001: TaskExecutor Framework for Autonomous Execution
Status: Accepted
Date: 2025-11-09
Context
The original system required manual copy/paste of Task protocol calls, creating a significant bottleneck in autonomous curriculum development.
Decision
Implement TaskExecutor framework as central execution engine that automatically executes Claude Task protocol calls without human intervention.
Rationale
- Eliminates Bottleneck: Removes manual copy/paste requirement
- Progress Tracking: Enables real-time monitoring and reporting
- Error Handling: Provides retry logic and graceful degradation
- Quality Gates: Integrates validation into execution pipeline
Consequences
- Positive: Fully autonomous operation, better reliability, comprehensive tracking
- Negative: Additional complexity, dependency on Claude Code integration
- Mitigation: Graceful fallback to manual execution when TaskExecutor unavailable
ADR-002: Template-Based Content Generation
Status: Accepted
Date: 2025-11-09
Context
Need to generate 480 content units (15 types × 4 levels × 8 modules) efficiently while maintaining consistency and quality.
Decision
Implement template-based approach with parameter substitution enabling 95% reusability across all content generation.
Rationale
- Efficiency: 95% template reuse vs. creating each piece individually
- Consistency: Standardized patterns ensure uniform quality
- Scalability: Easy adaptation to new modules and content types
- Maintenance: Single template updates propagate everywhere
Consequences
- Positive: Massive efficiency gains, consistent output, easy scaling
- Negative: Potential rigidity, template design complexity
- Mitigation: Flexible parameter system, autonomous template execution
ADR-003: Multi-Agent Coordination Pattern
Status: Accepted
Date: 2025-11-09
Context
Different content types require different expertise (research, content creation, quality assurance, optimization).
Decision
Implement specialized agent coordination where each content type maps to specific primary and supporting agents.
Rationale
- Expertise Matching: Right agent for the right task
- Quality Optimization: Specialized agents produce better results
- Parallel Execution: Multiple agents can work simultaneously
- Workflow Efficiency: Clear responsibility assignment
Consequences
- Positive: Higher quality output, better resource utilization, clear workflows
- Negative: Coordination complexity, potential agent conflicts
- Mitigation: Orchestrator agent for coordination, clear agent responsibilities
ADR-004: Quality-First Development Approach
Status: Accepted
Date: 2025-11-09
Context
Educational content requires high standards for accuracy, pedagogical effectiveness, and consistency.
Decision
Implement automated quality validation pipeline integrated into every content generation workflow.
Rationale
- Educational Standards: Content must meet pedagogical requirements
- Consistency: Cross-content validation ensures uniform quality
- Reliability: Automated validation scales with content volume
- Improvement: Quality feedback enables continuous enhancement
Consequences
- Positive: Consistent high quality, automated compliance, improvement feedback
- Negative: Additional processing time, validation complexity
- Mitigation: Parallel validation, configurable quality thresholds
ADR-005: NotebookLM-First Optimization
Status: Accepted
Date: 2025-11-09
Context
Content will be consumed through AI-enhanced learning platforms like NotebookLM requiring specific optimization.
Decision
Implement specialized NotebookLM optimizer as core tool for content preparation and enhancement.
Rationale
- AI Enhancement: Optimized content enables better AI tutoring
- Learning Experience: Improved student interaction with material
- Future-Ready: Prepares content for AI-assisted education
- Metadata Rich: Enhanced content supports advanced learning analytics
Consequences
- Positive: Superior learning experience, AI-ready content, advanced analytics
- Negative: Additional processing step, optimization complexity
- Mitigation: Automated optimization, configurable enhancement levels
ADR-006: Incremental Enhancement Strategy
Status: Accepted
Date: 2025-11-09
Context
Existing scripts worked but had manual bottlenecks and limited automation capabilities.
Decision
Enhance existing system incrementally rather than complete rebuild, adding autonomous capabilities while preserving proven patterns.
Rationale
- Risk Mitigation: Builds on proven foundation vs. starting from scratch
- Time Efficiency: Faster delivery of enhanced capabilities
- Backward Compatibility: Maintains existing functionality
- Proven Patterns: Leverages successful agent coordination approaches
Consequences
- Positive: Faster implementation, lower risk, maintained stability
- Negative: Some architectural compromises, gradual improvement curve
- Mitigation: Clear enhancement roadmap, modular improvements
ADR-007: Specialized Tools Architecture
Status: Accepted
Date: 2025-11-09
Context
Specific activities like NotebookLM optimization, quality validation, and progress tracking need focused implementations.
Decision
Create specialized tools directory with single-purpose, reusable tools that can be called by other scripts.
Rationale
- Separation of Concerns: Each tool has single, focused responsibility
- Reusability: Tools can be used across multiple workflows
- Maintainability: Easier to update and enhance specific capabilities
- Testability: Focused tools are easier to test and validate
Consequences
- Positive: Clean architecture, better maintainability, enhanced reusability
- Negative: Increased number of components, integration complexity
- Mitigation: Clear interfaces, comprehensive documentation, automated testing
ADR-008: Progress Tracking and Reporting
Status: Accepted
Date: 2025-11-09
Context
Autonomous systems need visibility into operation status, progress, and quality metrics.
Decision
Implement comprehensive progress tracking with JSON-based persistence and real-time reporting capabilities.
Rationale
- Visibility: Users need insight into autonomous operation status
- Debugging: Progress logs help identify and resolve issues
- Analytics: Performance metrics enable system optimization
- Accountability: Clear tracking of what was done and results
Consequences
- Positive: Full system visibility, better debugging, performance insights
- Negative: Storage overhead, reporting complexity
- Mitigation: Configurable detail levels, automated cleanup, efficient formats
Summary of Key Architectural Principles
- Autonomous-First: Eliminate manual intervention where possible
- Template-Driven: Maximize reusability through parameterized patterns
- Quality-Integrated: Build validation into every workflow
- Agent-Specialized: Match expertise to task requirements
- Tool-Focused: Create reusable, single-purpose components
- Progress-Visible: Provide comprehensive tracking and reporting
- Enhancement-Ready: Design for continuous improvement and scaling
Document Maintained By: System Architecture Team
Review Cycle: Monthly
Next Review: 2025-12-09