Persona-as-Configuration: Generative Stakeholder Reporting for Agricultural Floods

Abstract:Cyber-physical systems built on deterministic edge inference, such as on-vehicle flood detection for agricultural fields, produce structured decision logs that must be interpreted differently by heterogeneous stakeholders. Pairing such systems with large language models (LLMs) to generate stakeholder-specific reports introduces a tension: the generative layer is non-deterministic, while the edge plane must remain replayable and auditable. We propose an architectural pattern resting on two invariants: unidirectional consumption, in which the generative layer is a strict read-only consumer of the deterministic plane and never writes back, and persona-as-configuration, in which stakeholder adaptation is a versioned prompt-template artifact rather than runtime improvisation. We instantiate the pattern as a context-aware dashboard layer over the JSON decision logs of a previously published edge-based standing-water detection system, and analyse how the integration boundary admits standard generative-reliability mitigations as configuration- or middleware-level extension points. A structured expert review rated the pattern favourably across five ISO/IEC 25010-aligned quality dimensions, with strongest agreement on separation of concerns. End-user evaluation with agricultural stakeholders is planned for future work.

Accepted at CASA 2026 (9th Workshop on Context-Aware, Autonomous and Smart Architectures), co-located with ECSA 2026. 16 pages, 2 figures

Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)

arXiv:2607.17774 [cs.SE]

(or arXiv:2607.17774v1 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2607.17774 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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