A model-agnostic runtime control layer that governs when an AI system is allowed to act autonomously.

Instead of assuming continuous autonomy, the system continuously evaluates confidence, uncertainty, and learning stability to decide whether an agent should:

• ACT
• DEFER
• ESCALATE

All decisions are evidence-based and auditable.

Most AI systems today:

• Continue acting while reliability degrades
• Detect failure only after harm occurs
• Lack internal mechanisms to revoke autonomy
• Rely on external kill switches or human oversight

This project demonstrates runtime governance — autonomy that can be granted, revoked, escalated, and restored during execution.

• Cold-start restraint (no blind autonomy)
• Evidence-based autonomy grant
• Silent degradation detection
• Autonomy revocation before failure propagates
• Escalation on persistent instability
• Recovery and re-authorization after evidence

The plot below shows the full lifecycle of autonomy control at runtime.

• Confidence (blue)
• Uncertainty (orange)
• Trust (green)
• Red regions indicate autonomy being blocked (DEFER / ESCALATE)

See: runtime_governance_plot.png

• Model-agnostic
• Evidence-based
• Explainable
• Auditable
• Recoverable (no deadlocks)

• Not a model
• Not fine-tuning
• Not prompt engineering
• Not AGI

This is infrastructure, not intelligence.

This repository is a working demonstration of runtime governance behavior and is intended as:

• A research artifact
• A safety primitive prototype
• A foundation for production integration