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Digital workers, evaluated and owned

Prioritize the right processes. Measure the results. Keep the source code.

Digital Worker 9 AI Agents Example

AI-Powered Course Creation Digital Worker

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation.

9 AI Agents
8 Tech Stack
AI Orchestrated
Scoped Availability
Worker ID: course-creation-worker

Problem Statement

The challenge addressed

Manual course creation is time-consuming and resource-intensive. Subject matter experts spend countless hours structuring content, generating transcripts, creating assessments, supporting review of accessibility requirements, and translating material...

Solution Architecture

AI orchestration approach

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Deploys a coordinated swarm of 9 specialized AI agents that autonomously process uploaded source materials (videos, docume...
Interface Preview 4 screenshots

Illustrative preview (sample data, not verified outcomes): Mission Control - Source materials upload with job configuration including course title, description, target audience, difficulty level, output languages, and agent capabilities

Illustrative preview (sample data, not verified outcomes): Agent Orchestration - Task Execution DAG with phased workflow, live reasoning feed showing agent thoughts and actions, and Agent Fleet status panel

Illustrative preview (sample data, not verified outcomes): Agent Collaboration & Human Oversight - Inter-agent collaborations, AI Guardrails & Safety checks (content safety, bias detection, factual accuracy, compliance), and Human-in-the-Loop interventions

Illustrative preview (sample data, not verified outcomes): Final Output Dashboard - Total savings ($2,989), time saved (40h), quality score (100), safety score (89%), content generated summary, and processing metrics

Multi-Agent Orchestration

AI Agents

Specialized autonomous agents working in coordination

9 Agents
Parallel Execution
AI Agent

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

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. 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.

EXAMPLE #1
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AI Agent

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

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. 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.

EXAMPLE #2
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AI Agent

Transcript Agent

Video and audio content lacks searchable text, accessibility features, and timestamped navigation. Manual transcription is expensive and error-prone.

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. 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. Performance requires validation on representative workloads.

EXAMPLE #3
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AI Agent

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

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. 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.

EXAMPLE #4
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AI Agent

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

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. 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.

EXAMPLE #5
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AI Agent

Content Agent

Learners need summaries, key takeaways, and supplementary materials to reinforce learning. Creating these manually for each module is labor-intensive.

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. 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.

EXAMPLE #6
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AI Agent

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

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Supports review of WCAG requirements 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.

EXAMPLE #7
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AI Agent

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

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. 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 Performance requires validation on representative workloads.

EXAMPLE #8
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AI Agent

Quality Agent

Automated content generation may introduce factual errors, bias, or quality issues. Without validation, these problems reach learners and damage credibility.

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. 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.

EXAMPLE #9
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Technical Details

Worker Overview

Technical specifications, architecture, and interface preview

System Overview

Technical documentation

The Course Creation Digital Worker is an enterprise-grade multi-agent orchestration system that automates end-to-end course development. Users upload source content through Mission Control, configure learning parameters, and launch the agent swarm. The system executes through six screens: Mission Control (input configuration), Orchestration (task graph visualization and agent activity), Collaboration (inter-agent communication), Observability (metrics, tokens, latency tracking), Artifacts (generated outputs), and Final Output (course package with export options).

Tech Stack

8 technologies

Standalone components with OnPush change detection

RxJS BehaviorSubjects for reactive state management across agent services

LLM integration supporting Claude 3 (Opus, Sonnet, Haiku) and GPT-4 models

Task Graph Service for DAG-based workflow orchestration with checkpoint recovery

Token usage tracking with cost estimation per model provider

AI Guardrails Service for content safety, bias detection, and hallucination prevention

HITL Service for human approval workflows on critical decisions

SCORM/xAPI export capability for LMS integration

Architecture Diagram

System flow visualization

AI-Powered Course Creation Digital Worker Architecture
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