In plain English
This page explains the governance layer: rules, logs, approvals, signatures, audits, permissions, and rollback tools. These controls are necessary, but they also become important failure points.
- Why this matters: AI risk can come from the whole arrangement, not one obvious model.
- What to look for: data, memory, routes, adapters, tools, evaluators, updates, and rollback paths.
- Technical version below: the expert terminology remains available and is linked through the glossary.
Reproduction Rate and Resource Limits
A model ecology becomes harder to govern when candidate generationCreating a proposed new model, adapter, prompt, route, test, or policy. Open glossary definition outruns review. Rate and resource limits convert an unbounded search into an inspectable process.
What to bound
Bound candidate count, adapter stackA set of adapters loaded together, usually in a defined order. Open glossary definition depth, total active adapters, merge attempts, memory writes, synthetic-data retention, evaluator invocations, tool calls, network access, canary traffic, and promotion frequency.
Why limits are not enough
A small number of highly influential candidates can still create risk. Limits reduce blast radius; they do not replace composition-aware evaluation.
Healthy limit design
Limits should be explicit, externally enforced, signed, reviewed in UTC release history, and tied to no-opThe decision not to change the system. Open glossary definition outcomes. Exceeding a limit should trigger a pause, not an automatic search for a workaround.