Governing What You Cannot Observe: Adaptive Runtime Governance for Autonomous AI Agents

Autonomous AI agents can remain fully authorized and still become unsafe as behavior drifts, adversaries adapt, and decision patterns shift without any code change. We propose the $\mathbf{\text{Informational}}\mathbf{\text{Viability}}\mathbf{\text{Principle}}$: governing an agent reduces to estimating a bound on unobserved risk $\widehat{B}(x)=U(x)+SB(x)+RG(x)$ and allowing an action only when its capacity $S(x)$ exceeds $\widehat{B}(x)$ by a safety margin. The $\mathbf{\text{Agent}}\mathbf{\text{Viability}}\mathbf{\text{Framework}}$, grounded in Aubin’s viability theory, establishes three properties – monitoring (P1), anticipation (P2), and monotonic restriction (P3) – as individually necessary and collectively sufficient for documented failure modes. $\mathbf{\text{RiskGate}}$ instantiates the framework with dedicated statistical estimators (KL divergence, segment-vs-rest $z$-tests, sequential pattern matching), a fail-secure monotonic pipeline, and a closed-loop Autopilot formalised as an instance of Aubin’s regulation map with kill-switch-as-last-resort; a scalar Viability Index $VI(t)\in [-1,+1]$ with first-order $t^{\ast }$ prediction transforms governance from reactive to predictive. Contributions are the theoretical framework, the reference implementation, and analytical coverage against published agent-failure taxonomies; quantitative empirical evaluation is scoped as follow-up work.