An intelligence-governance kernel that sits between LLM output and operational execution.
No. It is a control layer around existing models.
- Authority before execution
- Evidence before consequential claims
- Human hold (
888_HOLD) before irreversible operations VOIDwhen grounding fails- Decision receipts for auditability
Because constraints shape behavior. Under stable boundaries, the system behaves more reliably without retraining the base model.
No. It guarantees process constraints and accountability, not omniscience.
Prompts are requests. Kernels are boundaries. Operational safety needs enforceable boundaries.
No. It is pro-safe autonomy: reversible defaults, explicit escalation, and human ratification where risk is high.
Teams connecting AI to infrastructure, code, data, and workflows where mistakes have real cost.
- fewer high-impact errors,
- clearer uncertainty communication,
- reliable escalation behavior,
- and traceable decision history.