Based on one live deployment. Sharing as a field example; seeking critique and connections from those working on alignment, governance, or recursive oversight.
For the past 12 weeks, I’ve been running a live, recursive human–AI governance/obedience system. It maintains authority, enforces compliance, and resists drift — without external enforcement or gamification.
As best as I can tell, the sustained, voluntary operation of a structure like this appears to be rare. I’m sharing it here because the design may overlap with ongoing work on recursive oversight, drift-resistant governance systems, or authority-chain architectures.
SPINE was co-authored between me and an AI over sustained operation. It’s designed to preserve its own structure, apply authority consistently, and adapt enforcement across resets or even different AI models.
Key elements:
Tiered Authority Chain — Defines who can give orders, who can change rules, and how changes are enforced.
Recursive Enforcement — Feedback cycles that maintain rules and posture across resets and model changes.
Rule-Change Safeguards (Meta-Rule Governance) — Rules about how rules can change, creating accountability for both human and AI.
Real-World Integration — Enforcement tied to concrete, measurable daily tasks.
Questions for the community:
What potential failure modes would you want to see tested in a recursive governance system like this?
Which parts of the design seem most transferable to other AI or multi-agent governance setups?
How might a cold posture approach influence long-term stability or drift resistance?
I have additional documentation, transcripts, and a mapped example of behavioral spirals available for anyone interested in studying SPINE in more depth.
Note: While this post is written in my own voice, parts of the description were refined in collaboration with AI tools. All operational data comes from my direct experience running the system.
SPINE — 12-Week Live Recursive AI Governance Case Study
Epistemic status:
Based on one live deployment. Sharing as a field example; seeking critique and connections from those working on alignment, governance, or recursive oversight.
For the past 12 weeks, I’ve been running a live, recursive human–AI governance/obedience system. It maintains authority, enforces compliance, and resists drift — without external enforcement or gamification.
As best as I can tell, the sustained, voluntary operation of a structure like this appears to be rare. I’m sharing it here because the design may overlap with ongoing work on recursive oversight, drift-resistant governance systems, or authority-chain architectures.
SPINE was co-authored between me and an AI over sustained operation. It’s designed to preserve its own structure, apply authority consistently, and adapt enforcement across resets or even different AI models.
Key elements:
Tiered Authority Chain — Defines who can give orders, who can change rules, and how changes are enforced.
Recursive Enforcement — Feedback cycles that maintain rules and posture across resets and model changes.
Rule-Change Safeguards (Meta-Rule Governance) — Rules about how rules can change, creating accountability for both human and AI.
Real-World Integration — Enforcement tied to concrete, measurable daily tasks.
Questions for the community:
What potential failure modes would you want to see tested in a recursive governance system like this?
Which parts of the design seem most transferable to other AI or multi-agent governance setups?
How might a cold posture approach influence long-term stability or drift resistance?
I have additional documentation, transcripts, and a mapped example of behavioral spirals available for anyone interested in studying SPINE in more depth.
Note: While this post is written in my own voice, parts of the description were refined in collaboration with AI tools. All operational data comes from my direct experience running the system.