Creative Proof (2024-Early 2025)
"We used creative projects to test whether structured disagreement could produce work none of the voices could create alone. It worked—but we didn't know why."
Zafiro Cycle — Poetry Series
AI-assisted poetry exploring identity, migration, and human-machine symbiosis. Six volumes completed.
MAAG v1.0 — Multi-Agent Article Generator
Early multi-agent system for generating structured articles. Internal use only. Proved viability of structured roles for analysis and drafting.
The Last Algorithm — Novel
Techno-thriller written via deliberate AI-human collaboration. Published May 22, 2025. First full test of structured multi-agent reasoning over long-form narrative.
Architectural Extraction (Mid-2025)
"We systematized the methodology. We built the orchestration layer. We made deliberation repeatable."
MAAG v2.1 — Multi-Agent Article Generator
Production-grade refinement of MAAG. Formalized role separation for strategy, ethics, and creativity across multiple LLM providers. Current and final planned version of MAAG.
MAGI v6 — Multi-Agent Reasoning Architecture
Next-generation successor to v5. Current milestone: v6.9 (Nov 21, 2025). Formalizes Strategos, Semanticus, and Narratos as role-based agents. Provider-agnostic routing. Living product with continuous development through 2026.
MAGI Ecosystem — Architecture Whitepaper
Technical and conceptual overview of the MAGI ecosystem: orchestration layers, arbitration logic, epistemic roles, governance, and open-source strategy.
Dynnovators Framework Atlas
First version developed Sep–Nov 2025. Captures the studio's conceptual, methodological, and technical patterns as a living framework. Evolves continuously without fixed release cadence.
What Didn't Work (And What We Learned)
Multi-agent systems aren't magic. Sometimes deliberation creates noise, not signal. Here's what we got wrong:
MAAG v1.5 (Never Released)
What we tried: Automating synthesis without human oversight.
What happened: Coherent but ethically hollow content. Agents optimized for flow, not meaning.
KinSight Pre-Alpha
What we tried: Genealogical storytelling without trauma-aware constraints.
What happened: Narratives that were factually accurate but emotionally reckless. System surfaced family trauma without appropriate framing.
MAGI v5 Performance Issue
What we tried: Synchronous deliberation for real-time applications.
What happened: Token budget constraints overwhelmed multi-agent coordination. System became too slow for production use.
Institutional Validation (Late 2025-2026)
"Now we're proving it scales. KinSight is our institutional test. If it succeeds, deliberation becomes infrastructure. If it fails, we'll document why—and what that teaches us about the limits of multi-agent systems."
KinSight — The Institutional Test
Trauma-aware genealogical storytelling system built on MAGI v6.9. Partnership with major genealogy platform to test whether multi-agent deliberation can handle production-scale complexity.
If it succeeds: Proves structured disagreement scales beyond creative projects to enterprise deployment. Opens path to commercial partnership and formal business entity.
If it fails: We'll document which constraints matter most—and what that teaches us about the limits of multi-agent systems in production.
Evaluation Milestone: July 2026. Partner assesses narrative quality, ethical compliance, and production readiness.
The Last Algorithm — Graphic Series
Graphic novel adaptation testing multi-agent deliberation in constrained creative spaces. Scripts, panel breakdowns, and storyboards ready. Issues 1-3 by March 2026, 4-5 by April, 6-7 by May.
MAGI.Lite — Hybrid Edge AI
Bringing MAGI's deliberative core to low-power devices. Selected for testing on Unihiker K10. Working prototype expected by end of January 2026. Scout & Council hybrid architecture: local agent for low-stakes decisions, cloud deliberation for high uncertainty.
Where We Fit in the Agentic AI Landscape
The agentic AI market will reach $10.41 billion in 2025, but most deployments remain experimental rather than production-scale. The gap isn't model capability—it's enterprise readiness.
Dynnovators Studio builds the orchestration layer that makes multi-agent systems institutional-ready:
Auditability
Every deliberation is traceable. You can see where agents agreed, where they diverged, and why. No black boxes. No unexplainable outputs.
Human Escalation
When ambiguity is high, the system routes to human judgment. We don't replace humans—we augment them. Critical decisions remain human-supervised.
Ethical Oversight
Built-in trauma-aware constraints, cultural sensitivity checks, and governance layers. Not bolted on as an afterthought—designed into the architecture.
We're not selling agents. We're selling trust.
If you're an institution that needs AI you can explain, defend, and trust—let's talk.
What's Next: The Institutional Test
KinSight Evaluation Milestone
Our genealogy partner will assess whether MAGI v6.9 can deliver:
- Narrative quality that matches human genealogists
- Ethical compliance that satisfies institutional review boards
- Production readiness that scales to thousands of users
If it succeeds: Structured disagreement becomes proven infrastructure for institutional AI. We transition to commercial partnership and formal business entity. Other institutions see a model for auditable, defensible AI deployment.
If it fails: We document which constraints matter most. We learn what multi-agent deliberation cannot do at enterprise scale. We share those lessons publicly.
Most consultancies hide their failures and cherry-pick successes. We're committed to epistemic honesty: documenting what works, what doesn't, and why.
KinSight is either going to validate 18 months of methodology development or teach us where our approach breaks. Either outcome advances the field's understanding of multi-agent systems in production.
Beyond KinSight: The Research Roadmap
MAGI v7.0-7.5 (2026-2027)
Asynchronous deliberation, parallel execution, streaming synthesis. Removes performance bottlenecks that currently limit real-time applications.
Contingent on v6.9 production validation
Deliberation as a Service
If institutional deployment succeeds, MAGI could become infrastructure for organizations that need AI they can explain, defend, and trust. Not because it's perfect—because it shows its work.
Vision, not commitment
Open Research Platform
Publishing deliberation patterns, failure modes, and synthesis strategies. Building a knowledge base for structured disagreement in production environments.
In progress via Framework Atlas
Learning in Public
We're documenting the KinSight journey as it happens. Not retrospectively polished case studies, but real-time learning:
- What we expected vs. what we found
- Where agent disagreement revealed genuine complexity vs. poor prompting
- Which synthesis patterns scaled vs. which created bottlenecks
- How institutional requirements shaped (and sometimes broke) the architecture
Whether KinSight succeeds or fails, the methodology lessons will be public. Because clarity scales faster than technology—and shared learning scales faster than proprietary knowledge.
We're sharing quarterly updates on institutional AI deployment, methodology refinements, and lessons learned from production systems.
We send updates quarterly, never sell your information, and you can unsubscribe anytime.
Want to Build AI You Can Defend?
If you're working on problems where single-voice AI isn't enough—where you need decisions you can audit, explain, and stand behind—let's talk about what structured disagreement could reveal.