Constraint-by-Balance (C-by-B) is a novel AI safety architecture designed to survive emergence.
Instead of relying solely on training-time alignment or human oversight, C-by-B embeds a dedicated Evaluator model alongside the agent's cognitive process—an independent reasoning stream that blocks irreversible or unbalanced harm in real time.
Grounded in scientific and regulatory precedent, it evaluates proposed actions using causal harm graphs and enforces constraint through revision or veto. C-by-B doesn't tune preferences—it constrains power.
By separating optimization from safety and operating at AI-native speed, it offers a scalable, interpretable foundation for safe, agentic AI.
