One Gate Is Not Enough: Composing Stateful Pre-Action Controls for Agentic AI

Abstract:Agentic AI systems take consequential actions governed by more than one pre-action control at once: authority, resource, and evidence gates that can admit, degrade, or remediate an action before it executes. This paper's central object is remediation-induced control coupling: a remediation applied by one control can change the action, evidence, or context another control evaluates, invalidating that control's earlier judgment. We formalize this coupling and give a remediate-and-regate protocol that restores per-action soundness in the current bounded, idempotent setting under its stated assumptions. We further show that the two implemented remediation operators (evidence substitution and resource-budget downroute) do not commute -- a finite-model checker finds concrete counterexample instances -- making remediation order part of the control-plane semantics rather than an implementation detail. A governed evidence buffer that trusts its own most recent admitted write is a further instance of the same problem at the level of state -- current admissibility does not imply future reference trustworthiness -- and is vulnerable to poisoning from declared-uncovered defect classes; two mitigations reduce, not eliminate, that exposure. Supporting results establish the exact condition under which positive-weight linear aggregation of gate outcomes can compensate a member veto, a unified cross-control Evidence Set, and that composition manufactures no new detection coverage, reported honestly. Empirically, on a deterministic open-data artifact composing three published engines unmodified, CH1-CH5 meet their registered decision rules across all 30 pre-registered seeds; CH6 does so under W1 but not under the smaller W2 workflow, reported as such. This is a mechanism demonstration on open payload data with a synthetic metadata layer, not a claim about production prevalence.

29 pages. Code and data: this https URL (Zenodo DOI: https://doi.org/10.5281/zenodo.22003399)

Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)

arXiv:2608.18360 [cs.SE]

(or arXiv:2608.18360v1 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2608.18360 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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