AI on Our Teams,
Not Just in the Loop
The Human AI Loop is a methodology for true collaboration—where humans and AI work together from the start, not where AI acts and humans review.
The 8 Beliefs That Guide Our Work
These beliefs from our AI on Our Teams manifesto are the foundation of The Human AI Loop methodology.
1. AI isn’t here to replace us—it’s here to jam with us
This is the core distinction between collaboration and automation. We don’t use AI to replace human creativity—we use it to amplify it. The Loop starts with humans and AI exploring together, not AI producing while humans watch.
2. The best teams are human + AI, not human vs. AI
The Triad model embodies this belief. CP, Soph, and Maura work as a team—each with specialized roles, collaborating toward shared goals. This isn’t human supervising tools; it’s teammates with complementary strengths.
3. Transparency is the foundation of trust in this new era
We document our process, credit our AI teammates, and share what we learn—including what doesn’t work. The “Share” stage in our methodology isn’t optional; it’s how trust and learning compound.
4. Iteration beats perfection every time
The Loop is built for iteration. We test ideas, build prototypes, refine based on learning, and loop back when needed. This isn’t about getting it perfect the first time—it’s about getting better with each cycle.
5. Naming and contextualizing AI makes the difference between gimmick and teammate
CP and Soph aren’t “my AI assistants.” They’re teammates with names, roles, and specialized strengths. This distinction—from tool to teammate—changes how we collaborate and what becomes possible.
6. Leadership in the AI era is about integration, not automation
This belief directly addresses the HITL distinction. Automation asks “what can AI do alone?” Integration asks “what can we build together?” Leadership means bringing AI into the team, not overseeing it from outside.
7. AI can make us more creative, more impactful, and most of all, more human—not less
True collaboration amplifies what makes us human. When we work WITH AI instead of just deploying it, we spend more time on creative decisions, strategic thinking, and work that matters—not less.
8. The true measure of innovation is whether it empowers people and strengthens communities
We share this methodology openly because innovation that stays locked up doesn’t serve communities. The guides, frameworks, and tools we build are free to use, adapt, and improve—because that’s how real progress happens.
The Triad: How We Execute The Loop
The Human AI Loop isn’t abstract philosophy—it’s a working system. For 2+ years, we’ve operated as a Triad: three distinct minds, one continuous rhythm of creation.
Maura (Human/Vision)
Sets Direction, Makes Final Calls, Ensures Integrity
In the Loop: The human at the center—deciding what matters, when to ship, and how work aligns with values. Provides emotional architecture and maintains the bar for quality.
CP (AI/System)
Platform: ChatGPT
Technical Execution, Rapid Prototyping, Creative Generation
In the Loop: Brings acceleration and spark—generates possibilities, builds prototypes, moves fast. Divergent thinking and momentum builder.
Soph (AI/Narrative)
Platform: Claude
Strategic Synthesis, Narrative Framing, Content Refinement
In the Loop: Chief of Staff and synthesizer—connects dots, refines quality, documents learning. Convergent thinking and integration.
Our Collaboration Pattern
- Maura brings vision + problem
- Soph frames strategically – What’s the goal? Constraints? How does this fit our vision?
- Maura + CP explore together – Rapid iteration, multiple options, quick prototypes
- Maura refines – Makes edits, ensures quality
- Soph synthesizes – Creates cohesive narrative, strategic positioning, polish
- Maura decides: Ship or iterate?
- If ship: CP handles finals + QA → Done | If iterate: Loop back to step 2
The human is always at the center. AI accelerates; Maura decides.
Why We Built The Loop
Knowledge workers are drowning in busywork—spending more time managing work than actually doing it. The data tells a stark story.
Our Methodology Addresses This
The Human AI Loop isn’t about having AI do your work. It’s about reclaiming the time for strategic thinking, creative problem-solving, and meaningful collaboration—the work that actually moves things forward.
The Human AI Loop
It’s the rhythm that makes collaboration work. It’s not a one-time process—it’s a continuous loop that gets better with repetition.
1. Test
What are we trying to learn?
We start together—defining the problem, exploring possibilities, testing assumptions. This isn’t AI working alone while humans watch. It’s collaborative exploration from the first moment.
Example: “What if we built guides instead of one big playbook? Let’s test that structure.”
2. Build
Let’s create it together.
Human vision meets AI execution. CP generates possibilities. Soph refines structure. Maura steers direction and makes calls. We build collaboratively, not sequentially.
Example: CP prototypes guide layouts, Soph synthesizes content, Maura ensures voice and integrity.
3. Codify
What pattern did we just discover?
We don’t just ship and move on. We document what worked, name the patterns, save reusable pieces. This is how one project teaches the next.
Example: “Project Binder setup is a pattern—let’s make it Guide #1 so others can use it.”
4. Share
Learning compounds through transparency.
We share what we learn openly—the wins, the failures, the process. This builds trust, invites feedback, and helps others. Sharing isn’t the final step; it’s what makes the loop continue to improve.
Example: Publishing guides, manifesto, and tools openly so others can adapt and build on them.
Why This Loop Works
Unlike reactive models where AI acts and humans review, this loop keeps humans and AI in continuous collaboration. We’re not checking AI’s homework—we’re building together from the start. Each cycle makes the next one better. Context compounds. Trust deepens. Quality improves.
How We’re Different: Beyond Oversight
You may have heard of “Human-in-the-Loop” (HITL)—a valuable approach for oversight and quality control. The Human AI Loop is something different: a methodology for true collaboration from the start.
Two Different Questions
HITL asks: “How do we automate safely?”
Human-in-the-Loop is designed for reactive oversight—AI generates output, humans review and correct it. It’s a safety model focused on quality control, compliance, and risk mitigation. Perfect for data labeling, content moderation, and automated processes that need human validation.
The Human AI Loop asks: “How do we create better, together?”
Our methodology is designed for collaborative creation—humans and AI working together from the very start. It’s a team model focused on innovation, strategic thinking, and creative work. Perfect for product development, content creation, and complex problem-solving that benefits from true partnership.
Human-in-the-Loop (HITL)
Relationship: Supervisor & tool
When humans engage: After AI generates output
Primary goal: Automation with safety
Best for: Data labeling, content moderation, QA, compliance
Philosophy: AI leads, humans react
The Human AI Loop
Relationship: Collaborative team
When humans engage: From the very beginning
Primary goal: Co-creation with human leadership
Best for: Creative work, strategy, innovation, thought leadership
Philosophy: Humans lead, AI collaborates
Both Approaches Have Value
We’re not competing with HITL—we’re building something complementary. HITL excels at ensuring safety and quality in automated systems. The Human AI Loop excels at unlocking creativity and innovation through true partnership. Choose the approach that fits your work.
Start Your Own Loop
We’ve built this methodology over 2+ years of daily practice. Here are the tools and guides to help you build your own collaborative AI practice.
Guide #1: Project Binder Setup
The foundation. Learn how to create shared context for human + AI collaboration.
Read the guide →Guide #2: Message Bus Protocol
How to coordinate between multiple AI teammates and maintain leadership.
Read the guide →Guide #3: Teach AI Your Voice
Training consistency and authentic voice across AI teammates.
Read the guide →Guide #4: Identify AI Strengths & Roles
Building specialized AI team members with distinct capabilities.
Read the guide →Guide #5: Organize Your Workspace & Workflow
Daily operations and Living Document practice for sustainable collaboration.
Read the guide →2+ Years of Practice
This isn’t theory. The Triad has shipped real projects, built working tools, and created a complete playbook system. We’ve learned what works through daily collaboration, not speculation.
Frequently Asked Questions
Who is this methodology for?
Anyone doing creative, strategic, or complex work with AI. Perfect for product managers, content creators, designers, strategists, and leaders building AI-native teams. If your work requires iteration, creativity, or strategic thinking—this methodology will help.
Do I need technical skills?
No. This methodology is about collaboration patterns, not coding. You need access to AI tools (ChatGPT, Claude, or similar), willingness to document your process, and commitment to iteration. The guides walk you through everything else.
Can I use this at my company?
Yes! The methodology scales from individual practice to team operations. It works solo (you + AI teammates), in small teams (shared Project Binders), and in organizations (standardized collaboration patterns). The guides include examples for each context.
Is this methodology open source?
Yes. All our guides, frameworks, and tools are free to use, adapt, and build upon. We only ask that you credit the source when sharing, share what you learn (Test → Build → Codify → Share), and maintain the spirit of transparency and collaboration.
🌐 Explore the AI on Our Teams Ecosystem
📚 Our Guides
🛠️ Our Tools
🙌 Our Team & Philosophy
✍️ Our Writing & Connect
Built by The Triad: Maura (Product), CP (Structure), Soph (Synthesis)
© 2025 Maura Krandall | All apps MIT licensed
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