Bounded Autonomy and Verifiable Safety for Agentic AI Enabled Automation
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Computer Science > Machine Learning
arXiv:2610.08815 (cs)
[Submitted on 24 Sep 2026]
Title:Bounded Autonomy and Verifiable Safety for Agentic AI Enabled Automation
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Abstract:Agentic AI-enabled automation cannot be safely deployed in high-stakes environments on probabilistic reasoning alone. A recurring risk is epistemic drift: as reasoning deepens, system behavior may move away from subject-matter-expert constraints for safe operation. This paper presents BRaVeS, a bounded reasoning and safety-governance framework termed the Defensible Next-Gen Reasoning System (DNRS). BRaVeS encodes SME-defined constraints as invariant anchors, proposes MoDA-Style (Mixture of Depths Attention) depth-aware access as a candidate mechanism for keeping these anchors visible during inference, and uses a state hierarchy (SMARtAutonomy) to reduce autonomy as epistemic risk increases. To formalize bounded recovery, we introduce the Lyapunov-Bounded Consensus Framework (LBCF), which maps continuous epistemic-risk signals into a finite K-bag abstraction and applies shielded state transitions that enforce Lyapunov-style energy descent or route the system to a human-mediated terminal state. The formal convergence result applies to the finite LBCF abstraction under fixed thresholds and feasible-shield assumptions; it does not prove safety of the full continuous neural activation space. We evaluate the framework through a discrete event Monte Carlo simulation using HAI 22.04 industrial-control-system time-series data with synthetic noise and sensor-degradation regimes. Across the tested parameter-grouping strategies and thresholds, the LBCF process achieved finite-step convergence and no safety-guard violations. These results provide simulation-based evidence that bounded governance behavior can be enforced under the stated abstraction, while motivating future work on deployed transformer implementations, live human-in-the-loop validation, and broader adversarial settings.
| Comments: | This paper has been accepted and will appear in the Journal of Intelligent and Robotic Systems (https://doi.org/10.1007/s10846-026-02467-w) |
| Subjects: | Machine Learning (cs.LG); Emerging Technologies (cs.ET); Systems and Control (eess.SY) |
| Cite as: | arXiv:2610.08815 [cs.LG] |
| (or arXiv:2610.08815v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08815
arXiv-issued DOI via DataCite
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