Building AI Teams That Think: From Bible Education to Mission Planning
Part 4: When Everything Came Together (In Ways I Didn’t Expect)
My final project wasn’t what anyone expected.
While others in the course built email assistants and customer service workflows, I created a multi-agent system that transforms complex theological research into engaging educational content for my mother’s Bible teaching ministry.
Then I turned around and applied the same principles to mission planning for defense applications.
Turns out, the skills are the same—only the stakes are different.
Week 4 was about putting everything together: personalization (Week 1) + voice analysis (Week 2) + systematic evaluation (Week 3) = autonomous workflows where multiple AI agents collaborate to accomplish complex tasks.
And what I discovered was this: The principles of building reliable AI systems are universal. The contexts change. The content varies. The stakes differ dramatically. But the fundamental architecture—specialized agents working together with clear roles, systematic handoffs, and human oversight—that works everywhere.
From Bible education to mission planning. From my mom’s blog to national security applications.
The same principles. The same skills. Just… different consequences when things go wrong.
The Bible Content Creator: Solving a Real Problem
Let me tell you why I chose this project.
My mom has been teaching Bible studies for decades. She’s insightful with deep love of history and context, a gift for making complex scripture accessible, a nurturing teaching style that makes people feel safe asking questions. But creating content for her blog and videos? That takes HOURS. Research. Scriptwriting. Production notes. Theological accuracy checks. Voice consistency.
And she’s doing it all manually.
I watched her spend entire days researching a single Bible story, cross-referencing commentaries, checking original Greek and Hebrew, organizing her thoughts into a coherent teaching script. The cognitive load was enormous. The time investment was unsustainable.
So I thought: What if AI could handle the research and initial scriptwriting, freeing her to focus on what she does best—the actual teaching?
But here’s the critical requirement: The AI had to capture HER voice. Not generic Bible teaching. Not sterile theological content. HER specific style—warm, nurturing, accessible, theologically sound, inviting questions, making connections to everyday life and just plain inspiring.
This wasn’t just about efficiency. It was about preserving authenticity while scaling capability.
Sound familiar? Because that’s exactly what I’d been learning for three weeks: personalization + voice + evaluation = reliable AI that actually sounds human.
The Multi-Agent Workflow Architecture
Here’s what I built (and this is where everything from Weeks 1-3 came together):
Research Analysis Assistant → Scriptwriting Assistant → Production Assistant → Evaluation
Each agent has a specialized function. Each maintains context from the previous step. Each contributes to a cohesive final output.
Let me break down how this actually works:
Agent 1: Research Analysis Assistant
This agent uses Chain-of-Thought reasoning broken into five clear steps:
Document Scan: Read the source material (Bible passage, commentaries, historical context)
Element Identification: Pull out key theological concepts, historical details, cultural context
Theological Mapping: Connect concepts to broader biblical themes and practical applications
Structure Organization: Arrange information in a logical teaching flow
Priority Assessment: Identify what’s most important for the target audience
This isn’t just “summarize this passage.” This is sophisticated analysis that mimics how my mom actually THINKS about preparing a lesson. Breaking down complex information, making connections, organizing for teaching effectiveness.
The Chain-of-Thought process makes the AI’s reasoning visible and checkable. I can see WHAT it identified and WHY it prioritized certain elements. That transparency is critical for theological content where accuracy matters.
Agent 2: Scriptwriting Assistant
This is where voice becomes everything.
I fed this agent detailed characteristics of my mom’s teaching style:
Warm, nurturing tone that makes people feel safe
Accessible language that explains complex concepts simply
Personal anecdotes and everyday life connections
Questions that invite reflection rather than lecturing
Theological precision without academic jargon
Enthusiasm for discovery (”Isn’t that amazing?!”)
But here’s the sophisticated part: I incorporated the EMOTIONPROMPT ENHANCEMENT techniques (that I learned in Tyler & Sara’s course)—cutting-edge prompting methods that help AI embody emotional qualities, not just mimic surface-level language patterns.
This agent doesn’t just write in my mom’s style. It captures her HEART. The nurturing quality. The genuine excitement about scripture. The invitation to think alongside her rather than being taught AT.
Agent 3: Production Assistant
This agent adds practical production notes:
Suggested visuals or graphics
Emphasis points for delivery
Potential audience questions to address
Timing recommendations
Follow-up discussion prompts
Because a script isn’t just words—it’s a blueprint for actual teaching.
The Evaluation Loop
And of course (because Week 3 taught me this is EVERYTHING), the workflow includes systematic evaluation:
Theological accuracy check
Voice consistency assessment
Accessibility verification (is this understandable to the target audience?)
Engagement potential (will this hold attention?)
Practical applicability (can people actually USE these insights?)
The output isn’t final until it passes evaluation. That’s how you maintain quality at scale.
The Sample Outputs: Proof of Concept
I tested this workflow on three different Bible stories:
The unclean spirit in Capernaum
Water into wine at Cana
The nobleman’s son
The consistency across all three was remarkable. Same warm tone. Same accessible explanations. Same invitation to discovery. Same theological precision.
My mom read the outputs and said: “This sounds like me. How did you DO that?”
That’s when I knew the system worked. Not just functionally—but authentically.
The Systems Thinking Breakthrough
But here’s what Week 4 really taught me: Building agentic workflows isn’t about AI capabilities. It’s about systems thinking.
Each agent has a specialized function (like team members with specific expertise). Each agent maintains context from previous steps (like passing information in a relay). Each agent contributes to a cohesive whole (like an orchestra playing a symphony).
This is exactly what my UX design background prepared me for. Understanding workflows. Identifying handoff points. Designing for collaboration. Maintaining consistency across complex systems.
And suddenly I was making connections (because that’s what my brain does):
Remember my CAMPS failure? The 4.5-year Air Force project that never shipped? We tried to build everything for everyone all at once. We never understood the underlying system patterns. We never broke down complex workflows into specialized components.
What if we’d approached it differently? What if we’d identified the core pattern (requirement → allocation → planning → execution) and built specialized agents for each phase? What if we’d started with ONE line of business, proven the concept, then scaled?
The Bible Content Creator workflow is what CAMPS should have been: Specialized components. Clear handoffs. Systematic evaluation. Iterative refinement.
Evolution, not revolution.
From Bible Education to Mission Planning
Here’s where it gets really interesting.
By the end of Week 4, I’d already started applying these same principles to my actual work at Vega Federal Solutions.
I created a mission planning demo for my bosses. Same architecture. Different context. WAY different stakes.
Research Analysis Agent → Mission Planning Agent → Resource Allocation Agent → Risk Assessment Agent → Evaluation
The workflow structure is identical to the Bible Content Creator. But instead of theological research, it’s analyzing mission requirements. Instead of scriptwriting, it’s generating courses of action. Instead of production notes, it’s resource allocation recommendations.
And here’s the critical insight: The principles are universal, but the evaluation criteria are context-specific.
For Bible education:
Theological accuracy
Voice authenticity
Accessibility
Engagement
For mission planning:
Operational feasibility
Resource optimization
Risk mitigation
Decision confidence
Different criteria. Same systematic evaluation framework.
The “Barrel Master” Insight Revisited
Remember Week 1 when my personalized Professor understood “Barrel Master” terminology without explanation? That moment of realizing AI could understand domain-specific context?
That’s what these workflows do at scale.
The mission planning agents understand military terminology, operational constraints, asset allocation principles—not because I explained every acronym, but because I built that domain knowledge into the system instructions. Just like I built my mom’s teaching voice into the Bible Content Creator.
Personalization isn’t just about making AI “nice.” It’s about building domain expertise into AI systems so they can operate effectively in specialized contexts.
And in high-stakes government applications? That domain expertise is the difference between AI that’s “interesting” and AI that’s actually USEFUL.
The Four Perspectives Framework (Healthcare → Defense)
This connects directly to something I wrote about months ago: Vincent Buil’s work at Phillips Healthcare on designing AI for high-stakes medical applications.
He emphasized four perspectives for AI governance:
Purpose: Well-defined use cases
Pathways: Carefully designed workflows
Policy: Regulatory compliance and safety
Pixels: Prototype-based validation
At the time, I recognized these principles applied to defense applications. But I didn’t fully understand HOW to operationalize them.
Week 4 showed me: This is HOW.
Purpose: My Bible Content Creator has a clear use case—transform research into teaching scripts. My mission planning demo has a clear use case—generate courses of action for asset allocation.
Pathways: The multi-agent workflows ARE the carefully designed pathways. Research → Scriptwriting → Production. Mission Analysis → Planning → Resource Allocation → Risk Assessment.
Policy: The evaluation criteria embedded in each workflow ensure compliance with domain-specific standards (theological accuracy for Bible content, operational feasibility for mission planning).
Pixels: The prototypes I built demonstrate the concepts in action, allowing for testing and refinement before full deployment.
The Pattern Library approach Buil described—creating reusable design patterns that enforce governance while remaining practical—that’s exactly what these agent workflows are.
The Pattern Library isn’t just a design system. It’s the implementation mechanism for responsible AI in high-stakes contexts.
And now I know how to build it.
The Bigger Picture: What This Means for High-Stakes AI Design
By Week 4, I was seeing connections everywhere:
The skills I learned aren’t just about building AI assistants. They’re about designing human-AI collaboration systems where:
Specialized agents handle specific tasks (like specialized team members)
Context flows seamlessly between agents (like well-designed handoffs)
Human oversight happens at critical decision points (like approval gates in workflows)
Systematic evaluation ensures reliability (like quality control in manufacturing)
Voice and authenticity are preserved (like brand consistency in design systems)
This is what I’ve been writing about for months—shared responsibility, appropriate trust calibration, transparency, human agency—but now I have the PRACTICAL SKILLS to actually build it.
In healthcare: Agents that analyze patient data → generate diagnostic recommendations → flag risks → present options to doctors with clear reasoning
In defense: Agents that analyze mission requirements → generate courses of action → assess risks → present recommendations to commanders with transparent logic
In intelligence: Agents that process raw data → identify patterns → generate hypotheses → present findings to analysts with confidence levels
Same architecture. Different domains. Universal principles.
The Neurodivergent Advantage Confirmed
And here’s where everything from Week 1 comes full circle:
My neurodivergent brain—the pattern recognition, the parallel processing, the comfort with complexity, the ability to see connections across disparate domains—this isn’t just helpful for AI collaboration. It’s ESSENTIAL for designing multi-agent systems.
Building these workflows requires:
Seeing the big picture AND the minute details simultaneously (macro + micro thinking)
Understanding how specialized components work together (systems thinking)
Identifying handoff points and context requirements (workflow design)
Maintaining consistency while allowing specialization (pattern recognition)
Evaluating across multiple criteria simultaneously (parallel processing)
These are exactly the cognitive traits I documented in Week 1. The traits I’ve spent years thinking were “scattered” or “unfocused.”
Turns out they’re not bugs. They’re features. They’re exactly what complex AI system design requires.
The Business Applications Multiplying
By the end of Week 4, my brain was spinning with applications (and now I had the skills to actually BUILD them):
For my bosses’ startup (DOD contracting): Multi-agent workflow for proposal generation—Research Agent → Technical Writing Agent → Compliance Check Agent → Executive Summary Agent → Evaluation
For my husband’s accounting company: Multi-agent workflow for financial analysis—Data Collection Agent → Analysis Agent → Recommendation Agent → Risk Assessment Agent → Client Communication Agent
For my mom’s blog: Already built! And now thinking about expansion—Video Script Agent → Social Media Content Agent → Discussion Guide Agent → Engagement Analytics Agent
For my own UX work: Multi-agent workflow for design documentation—User Research Synthesis Agent → Design Rationale Agent → Stakeholder Communication Agent → Technical Specification Agent
Every single one follows the same pattern: Specialized agents. Clear handoffs. Systematic evaluation. Human oversight at critical decision points.
What I’m Building Next
Here’s where I’m taking these skills:
Immediate application: Refining the mission planning demo for actual use at Vega Federal Solutions. Testing with real users (Barrel Masters and mission planners). Iterating based on feedback. Building evaluation frameworks that measure operational effectiveness, not just AI performance.
Medium-term goal: Applying these principles to the NGA/NRO contracts my company is pursuing. Building AI systems that help intelligence analysts make sense of complex data while preserving their expertise and decision-making authority.
Long-term vision: Positioning myself at the intersection of UX design, AI, and high-stakes applications. Not just designing interfaces—designing collaboration systems where humans and AI work together effectively in contexts where consequences matter.
Because here’s what seven weeks taught me: The future of AI isn’t about making it smarter. It’s about making it more collaborative.
And collaboration requires:
Understanding how humans actually think (personalization)
Preserving authentic human voice (voice analysis)
Systematic improvement and reliability (evaluation)
Specialized agents working together (agentic workflows)
Human oversight at critical points (shared responsibility)
These aren’t AI skills. These are DESIGN skills. UX skills. Systems thinking skills.
And I have them now.
The Transformation I’m Sitting With
Seven weeks ago, I thought I was learning to build AI assistants.
What I actually learned was how to design collaboration between human intelligence and artificial intelligence in ways that preserve what makes us human while amplifying our capabilities.
Week 1: My neurodivergent brain isn’t something to fix—it’s exactly what AI collaboration requires
Week 2: My “scattered” communication style is sophisticated cognitive transparency that invites collaborative thinking
Week 3: Systematic evaluation is how you bridge the gap between AI capability and AI reliability
Week 4: The skills for building reliable AI systems are universal—only the contexts and stakes vary
And now I’m taking these skills into environments where the stakes are life-and-death. Where trust matters. Where human agency is non-negotiable. Where “good enough” isn’t good enough.
The $50 Billion Question Answered
Remember that statistic from Week 1? Only 1% of executives believe their AI investments have reached maturity. 47% identify talent skill gaps as the primary barrier.
Now I understand what that gap actually is.
It’s not about people who can use AI tools. It’s not about people who can write prompts. It’s not even about people who understand AI capabilities.
It’s about people who can design human-AI collaboration systems that actually work in complex, high-stakes environments.
People who understand:
How to personalize AI for specific contexts and users
How to preserve authentic voice and domain expertise
How to systematically evaluate and improve AI performance
How to build multi-agent workflows with clear roles and handoffs
How to maintain human oversight without creating bottlenecks
How to scale reliability, not just capability
That’s the 1% skill. That’s the $50 billion talent gap.
And after seven weeks, I’m in that 1%.
Where I’m Going Next
I’m taking everything I learned and applying it to the work that matters most to me: designing AI for government and defense applications where lives are at stake.
I’m writing about it (like this series) to help others understand what effective AI collaboration actually requires.
I’m building systems that demonstrate these principles in action—not just theory, but working prototypes that solve real problems.
And I’m positioning myself as someone who can bridge the gap between AI capability and human need. Between technical possibility and ethical responsibility. Between efficiency and humanity.
Because if AI is going to amplify human intelligence rather than replace it, we need designers who understand both the technical AND the human side of that equation.
We need people who can see patterns across complex systems (neurodivergent advantage). Who can preserve authentic voice while scaling capability (brand voice analysis). Who can systematically improve reliability (evaluation frameworks). Who can design collaboration, not just automation (agentic workflows).
The question isn’t whether AI belongs in high-stakes environments. The question is: Are we designing it with the rigor, responsibility, and human-centered thinking those stakes demand?
I spent seven weeks learning how to answer that question with “yes.”
The Final Insight
Here’s what I’m sitting with as I finish this series:
My neurodivergent brain—the one I spent years trying to “manage” and “fix”—has been training for this moment my entire life.
The pattern recognition. The parallel processing. The comfort with complexity. The ability to see connections across disparate domains. The tendency to question everything. The discomfort with surface-level solutions.
These aren’t bugs in my cognitive process. They’re features. They’re exactly what designing reliable AI systems in complex environments requires.
And the skills I learned in seven weeks—personalization, voice analysis, systematic evaluation, agentic workflows—these aren’t just AI skills. They’re fundamental design skills for the era we’re entering.
An era where human-AI collaboration is the norm, not the exception. Where the question isn’t “can AI do this?” but “how do we design AI to work WITH humans effectively?” Where the differentiator isn’t AI capability but human-AI partnership quality.
Seven weeks ago, I thought I was learning AI tools.
What I actually learned was how to design the future of work.
And I’m just getting started.
In the spirit of transparency I advocate for in AI development: I worked with Claude to structure and refine these reflections from my entire seven-week journey through Sara and Tyler’s AI Agentic Fundamentals course. The Bible Content Creator, the mission planning demo, the connections to my UX work, and the insights about neurodivergent advantages are all from my actual course experience, with AI assistance in articulating the patterns and principles I discovered. This series itself is an example of the human-AI collaboration I’m describing—preserving my authentic voice while leveraging AI to help structure and refine my thinking.
