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

Computer Science > Artificial Intelligence arXiv:2604.24686 (cs) [Submitted on 27 Apr 2026] Title:Governing What You Cannot Observe: Adaptive Runtime Governance for Autonomous AI Agents Authors:German Marin, Jatin Chaudhary View a PDF of the paper titled Governing What You Cannot Observe: Adaptive Runtime Governance for Autonomous AI Agents, by German Marin and 1 other authors View PDF HTML (experimental) Abstract: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 \textbf{Informational Viability Principle}: governing an agent reduces to estimating a bound on unobserved risk $\hat{B}(x) = U(x) + SB(x) + RG(x)$ and allowing an action only when its capacity $S(x)$ exceeds $\hat{B}(x)$ by a safety margin. The \textbf{Agent Viability 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. \textbf{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 $t^*$ 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. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2604.24686 [cs.AI] (or arXiv:2604.24686v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2604.24686 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Jatin Chaudhary [view email] [v1] Mon, 27 Apr 2026 16:46:15 UTC (46 KB) Full-text links: Access Paper: View a PDF of the paper titled Governing What You Cannot Observe: Adaptive Runtime Governance for Autonomous AI Agents, by German Marin and 1 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence…

This excerpt is published under fair use for community discussion. Read the full article at arXiv.org.