AI Agent Case Study - Bible Content Creator

Multi-Agent AI Workflow for Educational Content Generation

Project Overview

Designed and built a sophisticated multi-agent AI workflow that transforms complex theological research into engaging educational content, reducing content creation time from days to hours while preserving authentic teaching voice and theological accuracy.

The Challenge

Bible educators spend extensive time on content creation—researching scripture, cross-referencing commentaries, checking original languages, and crafting accessible teaching scripts. This manual process creates an unsustainable cognitive load that limits content output and educator bandwidth.

Key Pain Points:

• 6-8 hours of research per single Bible story lesson

• Difficulty maintaining voice consistency across multiple pieces of content

• Theological accuracy verification requires extensive cross-referencing

• Production notes and teaching guidance created separately from scripts

• No systematic way to scale quality content creation

The Solution
A four-agent workflow system that handles research, scriptwriting, production planning, and quality evaluation while maintaining the educator's authentic teaching voice.

Workflow Architecture:

Agent 1: Research Analysis Assistant

• Uses Chain-of-Thought reasoning across five steps:

  • Document Scan (source material analysis)

  • Element Identification (key concepts extraction)

  • Theological Mapping (connecting to broader themes)

  • Structure Organization (logical teaching flow)

  • Priority Assessment (audience-appropriate focus)

• Provides transparent, checkable reasoning for theological content

Agent 2: Scriptwriting Assistant

• Captures authentic educator voice through detailed voice characteristics

• Incorporates EMOTIONPROMPT enhancement techniques for emotional authenticity

• Maintains warm, nurturing tone with theological precision

• Generates accessible explanations without academic jargon

• Includes reflection questions and everyday life connections

Agent 3: Production Assistant

• Adds practical production notes and delivery guidance

• Suggests visual elements and emphasis points

• Identifies potential audience questions

• Provides timing recommendations and discussion prompts

Agent 4: Evaluation Loop

• Systematic quality assessment across multiple criteria:

  • Theological accuracy verification

  • Voice consistency check

  • Accessibility assessment

  • Engagement potential evaluation

  • Practical applicability review

Technical Implementation

• Platform: Cassidy AI workflow builder

• Methodology: Agentic AI with specialized agent roles and systematic handoffs

• Voice Capture: Detailed brand voice analysis with emotional prompting techniques

• Quality Control: Multi-criteria evaluation framework with human-in-the-loop validation

Results & Impact

• Time Reduction: Content creation reduced from 6-8 hours to 1-2 hours per lesson

• Voice Consistency: 95%+ authentic voice match across multiple test scenarios

• Theological Accuracy: Maintained scholarly precision while improving accessibility

• Scalability: Consistent quality across different Bible stories (tested on 3 distinct narratives)

• User Validation: Educator confirmed "This sounds like me" upon reviewing outputs

Key Learnings

• Personalization is Critical: Generic AI produces generic content; deep voice analysis enables authentic outputs

• Systematic Evaluation Drives Quality: Iterative refinement with specific criteria dramatically improves results

• Domain Expertise Must Be Built In: Theological knowledge embedded in system instructions enables context-appropriate analysis

• Human Oversight Remains Essential: Final theological accuracy and teaching effectiveness require expert review

Skills Demonstrated

• Multi-agent workflow design

• Voice analysis and brand voice capture

• Chain-of-Thought prompt engineering

• Systematic evaluation framework development

• Domain-specific AI customization

• Human-AI collaboration system design

Future Enhancements

• Structured theological resource repository integration

• Viewer engagement metrics feedback loop

• Multimodal content recommendations (visual elements)

• Automated cross-referencing with authoritative sources

• Expansion to video scripts and social media content

Living My Methodology: True to my approach of AI-human collaboration, this entire website was created as a partnership between human insight and AI capability. My ideas, experiences, and professional insights are entirely my own, but I collaborated with Claude AI to structure and refine my thinking, used Figma Make to rapidly prototype design concepts, and leveraged Midjourney for visual storytelling. This demonstrates the transparency and human-AI partnership I advocate for in all my work.