Bounded Autonomy and Verifiable Safety for Agentic AI Enabled Automation

arXiv:2610.08815v1 Announce Type: new 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

BRaVeS framework promotes safe AI deployment

This is early-stage academic safety-governance research validated only by simulation, with explicit caveats that it does not prove safety in real neural systems. The implication is mildly positive in that it represents careful, bounded thinking about agentic AI risk, but evidence strength is low and claims are narrow.

This paper proposes a framework for verifiable safety in agentic AI. It addresses a significant risk of 'epistemic drift' in high-stakes environments, where AI systems might deviate from human-defined safety constraints. While the results are based on simulation and abstract conditions, the intention to build safer AI systems is a positive development, making it materially optimistic.

The paper presents a framework for safer AI deployment in high-stakes environments. It demonstrates simulation-based evidence of finite-step convergence and no safety violations. This implies materially optimistic implications for human flourishing.

SOURCE     arXiv cs.LG (Machine Learning) (tier 1)
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DOCUMENT   db51eea0-22f8-4a47-bec5-4d885b1bd6a9
           https://arxiv.org/abs/2610.08815
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EVIDENCE   2 extracted excerpts
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MODEL RUN  4 runs, methodology 1.0.1
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SCORE      +11  (Mixed or uncertain)
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CONFIDENCE 74%