Intelligent anomaly detection in dynamic visual environments requires reconciling real-time performance with semantic interpretability. Conventional approaches address only fragments of this challenge. Reconstruction-based models capture low-level deviations without contextual reasoning, object detectors provide speed but limited semantics, and large vision-language systems deliver interpretability at prohibitive computational cost. This work introduces a cascading multi-agent framework that unifies these complementary paradigms into a coherent and interpretable architecture. Early modules perform reconstruction-gated filtering and object-level assessment, while higher-level reasoning agents are selectively invoked to interpret semantically ambiguous events. The system employs adaptive escalation thresholds and a publish-subscribe communication backbone, enabling asynchronous coordination and scalable deployment across heterogeneous hardware. Extensive evaluation on large-scale monitoring data demonstrates that the proposed cascade achieves a threefold reduction in latency compared to direct vision-language inference, while maintaining high perceptual fidelity (PSNR = 38.3 dB, SSIM = 0.965) and consistent semantic labeling. The framework advances beyond conventional detection pipelines by combining early-exit efficiency, adaptive multi-agent reasoning, and explainable anomaly attribution, establishing a reproducible and energy-efficient foundation for scalable intelligent visual monitoring.

翻译：动态视觉环境中的智能异常检测需要兼顾实时性能与语义可解释性。传统方法仅能应对该挑战的局部问题：基于重构的模型虽能捕获低级偏差但缺乏上下文推理，目标检测器速度快但语义信息有限，而大型视觉语言系统虽能提供可解释性却伴随难以承受的计算开销。本研究提出一种级联多智能体框架，将这些互补范式统一为协调且可解释的架构。早期模块执行重构门控过滤与目标级评估，而高层推理智能体则被选择性调用以解释语义模糊事件。该系统采用自适应升级阈值与发布-订阅通信主干，支持异构硬件间的异步协调与可扩展部署。在大规模监控数据上的广泛评估表明，所提出的级联架构相比直接视觉语言推理可降低三倍延迟，同时保持高感知保真度（PSNR = 38.3 dB, SSIM = 0.965）与一致的语义标注。该框架通过融合早期退出机制的高效性、自适应多智能体推理与可解释的异常归因，超越了传统检测流程，为可扩展的智能视觉监控建立了可复现且高能效的基础。