I've been exploring a question that keeps appearing in AI governance discussions:
If advanced AI systems become persistent agents operating inside infrastructure, how do governance mechanisms evolve beyond policy and audits?
This project explores a possible approach: embedding governance constraints directly into AI compute environments.
The core idea is the Compute Escalation Governance Protocol (CEGP).
CEGP introduces signed compute envelopes that define operational boundaries for AI systems. If a system attempts to expand compute beyond its envelope, it must request escalation through a structured governance pathway.
The goal is to align governance friction with increasing levels of autonomy and economic participation.
The repo contains:
– a capability-tiered governance architecture
– protocol draft for compute escalation
– enforcement primitives
– enterprise deployment model
Hi HN,
I've been exploring a question that keeps appearing in AI governance discussions:
If advanced AI systems become persistent agents operating inside infrastructure, how do governance mechanisms evolve beyond policy and audits?
This project explores a possible approach: embedding governance constraints directly into AI compute environments.
The core idea is the Compute Escalation Governance Protocol (CEGP).
CEGP introduces signed compute envelopes that define operational boundaries for AI systems. If a system attempts to expand compute beyond its envelope, it must request escalation through a structured governance pathway.
The goal is to align governance friction with increasing levels of autonomy and economic participation.
The repo contains: – a capability-tiered governance architecture – protocol draft for compute escalation – enforcement primitives – enterprise deployment model
Curious to hear technical critiques.