Technical deep-dives on deterministic governance, agent safety architecture, and the infrastructure behind autonomous AI systems.
Deterministic enforcement tells you what happened. Advisory reasoning tells you what a second opinion would say. Divergence intelligence tells you where those two layers disagree — and how dangerous the disagreement is.
New agents start sandboxed with full enforcement. As they demonstrate compliance through evaluation and fine-tuning, they graduate to lighter enforcement. If they drift, they get auto-demoted. The kernel never goes away — it becomes a safety net.
Random sampling breaks auditability. If you cannot reproduce a governance decision with the same inputs, you cannot defend it in court. Dealgo uses hash-based deterministic sampling — no Math.random(), no flaky CI, no courtroom surprises.
Capsules are composable governance policy bundles. They enforce tighten-only validation (no capsule can weaken policy) and strictest-wins merge semantics when multiple capsules apply. System floors are always enforced.