Implementing Inertial Coherence: A New Metric for Stable, Human-Governed AI
I’m excited to release the foundational architecture for Ethical Coherence Governance (ECG), a governance metric designed to address logical drift in multi-agent and autonomous AI systems.
The problem:
Most current AI governance relies on static, “human-on-the-loop” oversight. This model begins to fail as systems transition from isolated prompts to continuous, recursive interaction patterns and long-horizon autonomy.
The shift:
As agents move into persistent reasoning and Recursive Coherence Interaction (RCI), governance must become an internal system property, not an external checkpoint.
The solution:
The Pearson ECG Metric provides a real-time measurement of the delta between system intent and execution stability.
It transforms governance from a regulatory fence into an operational habit embedded inside the agent’s reasoning loop.
Key contributions:
Metric Definition: A formal calculation of Inertial Coherence for evaluating whether an AI system remains human-governed under recursion and optimization pressure.
Governance Architecture: An execution-level blueprint for high-stakes agentic deployments, where coherence must be maintained continuously, not post-hoc.
Human-Centered Alignment: Techniques for sustaining the Human–AI Reflective Mirror, ensuring long-form interactions preserve human authority, meaning, and intent.
https://huggingface.co/datasets/JuniperPearson/AIS-Governance-Architecture
I’m actively looking for researchers, safety engineers, and governance architects interested in implementing the Pearson ECG Metric inside internal sandboxes or evaluation pipelines.
Open to collaboration, testing partnerships, and design strategy sessions.
Juniper Pearson
AI Architect & Safety Specialist
www.linkedin.com/in/juniperpearson