Governance Primitive for Institutional AI Deployment: Addressing Authority Constraints in High-Stakes Systems
By
csemple
Pulled from the oven just right. Trustworthy, fact-dense, deeply satisfying.
Summary
The article discusses the institutional trust problem in AI deployment, particularly why AI agents fail to gain adoption in high-stakes institutions like healthcare, finance, and legal systems. The author, who led Product for Ontario's Digital Service, argues that institutions cannot justify probabilistic safety without governance primitives. They propose a governance primitive that makes authority constraints persistent and mechanically enforceable through tool filtering, describing it as a 'Physics Engine for Agents' that turns permissions into physical constraints. The solution works across domains with the same kernel and includes a reference implementation.
Key quotes
· 5 pulledHigh‑stakes institutions cannot justify probabilistic safety without governance primitives.
I built a governance primitive that makes authority constraints persistent and mechanically enforceable through tool filtering.
This is not a safety filter. This is a Physics Engine for Agents. It turns Permissions into Physical Constraints.
It works across domains (healthcare, finance, legal…) with the same kernel.
The reference implementation demonstrates the governance primitive for persistent, hierarchical authority constraints in LLM systems.
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