AI-Powered Course Creation Digital Worker
Deploys a coordinated swarm of 9 specialized AI agents that autonomously process uploaded source materials (videos, documents, presentations) and transform them into production-ready courses. The system uses a Directed Acyclic Graph (DAG) for task orchestration, ReAct reasoning loops for intelligent decision-making, and human-in-the-loop interventions for quality assurance.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Mission Control - Source materials upload with job configuration including course title, description, target audience, difficulty level, output languages, and agent capabilities
Agent Orchestration - Task Execution DAG with phased workflow, live reasoning feed showing agent thoughts and actions, and Agent Fleet status panel
Agent Collaboration & Human Oversight - Inter-agent collaborations, AI Guardrails & Safety checks (content safety, bias detection, factual accuracy, compliance), and Human-in-the-Loop interventions
Final Output Dashboard - Total savings ($2,989), time saved (40h), quality score (100), safety score (89%), content generated summary, and processing metrics
AI Agents
Specialized autonomous agents working in coordination
Orchestrator Agent
Complex course creation involves multiple interdependent tasks that must execute in the correct sequence. Without central coordination, agents may work on incomplete data or create conflicting outputs.
Core Logic
Acts as the central brain that coordinates task execution and agent delegation. Manages the task graph, monitors checkpoint data for recovery, evaluates bottlenecks in the execution pipeline, and ensures proper sequencing of dependent operations. Uses Claude 3 Sonnet for orchestration decisions.
Planning Agent
Raw uploaded content lacks structure and learning context. Without analysis, the system cannot determine optimal content chunking, difficulty distribution, or assessment coverage.
Core Logic
Analyzes content structure and complexity to identify key learning objectives from source material. Determines optimal chunking strategy for embeddings, evaluates content difficulty distribution, and plans assessment coverage across topics. Creates vector embeddings for semantic search capabilities.
Transcript Agent
Video and audio content lacks searchable text, accessibility features, and timestamped navigation. Manual transcription is expensive and error-prone.
Core Logic
Generates accurate transcripts with precise timestamps using Claude 3 Haiku for efficiency. Processes audio segments, detects speaker changes, aligns timestamps with text segments, validates transcription accuracy, and formats output with proper punctuation. Achieves 97%+ accuracy with speaker identification.
Assessment Agent
Creating valid, pedagogically-sound assessments requires expertise in question design, difficulty calibration, and Bloom's taxonomy alignment. Manual question generation doesn't scale.
Core Logic
Analyzes content to identify question-worthy concepts and generates diverse assessment items. Creates multiple-choice questions with quality distractors, balances difficulty across question sets, validates question-answer alignment, and ensures Bloom's taxonomy coverage. Each question receives a quality score based on clarity and pedagogical validity.
Structure Agent
Raw content needs logical organization into chapters, modules, and learning paths. Without proper structure, learners struggle to navigate and progress through material effectively.
Core Logic
Organizes content into a hierarchical structure with chapters and modules using semantic analysis. Leverages vector search to group related topics, creates logical learning progressions, and defines clear learning objectives per section. Outputs standardized course structure artifacts.
Content Agent
Learners need summaries, key takeaways, and supplementary materials to reinforce learning. Creating these manually for each module is labor-intensive.
Core Logic
Generates chapter summaries, key point highlights, and supplementary learning materials using Claude 3 Haiku for cost efficiency. Uses vector search to identify critical concepts and creates engaging, learner-friendly content that reinforces core material.
Accessibility Agent
Learning content must comply with WCAG 2.1 AA standards and Section 508 requirements. Non-compliant content excludes learners with disabilities and creates legal risk.
Core Logic
Ensures WCAG compliance by checking all content against accessibility standards. Validates color contrast, alt text presence, keyboard navigation support, screen reader compatibility, and caption accuracy. Generates detailed compliance reports with specific remediation recommendations.
Translation Agent
Global organizations need learning content in multiple languages. Professional translation is expensive, slow, and difficult to maintain as source content evolves.
Core Logic
Translates content to multiple target languages with cultural adaptation using Claude 3 Haiku. Preserves technical terminology, adapts idioms and examples for target cultures, maintains consistent terminology across the course, and achieves 96%+ translation confidence scores.
Quality Agent
Automated content generation may introduce factual errors, bias, or quality issues. Without validation, these problems reach learners and damage credibility.
Core Logic
Acts as the final quality gate by running comprehensive validation checks. Validates factual accuracy against source material, detects bias and sensitivity issues, verifies learning objective coverage, and assesses engagement and clarity scores. Generates quality improvement recommendations and assigns overall quality grades.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
8 technologies
Architecture Diagram
System flow visualization