編排者-代理信任：具備信任感知編排與基於RAG推理的模組化代理式AI視覺分類系統

摘要

现代人工智能（AI）日益依赖融合视觉与语言理解的多智能体架构。然而，一个紧迫的挑战依然存在：我们如何能在无需微调的零样本场景下信任这些智能体？我们提出了一种新颖的模块化Agentic AI视觉分类框架，该框架整合了通用多模态智能体、非视觉推理协调器以及检索增强生成（RAG）模块。应用于苹果叶病害诊断，我们对比了三种配置：（I）基于置信度协调的零样本配置，（II）性能提升的微调智能体配置，以及（III）通过CLIP图像检索与重评估循环增强的信任校准协调配置。利用置信度校准指标（ECE, OCR, CCC），协调器调节各智能体间的信任度。我们的结果显示，在零样本设置下，采用信任感知协调与RAG，准确率提升了77.94%，整体达到85.63%。GPT-4o展现出更好的校准性，而Qwen-2.5-VL则表现出过度自信。此外，基于图像RAG的预测通过视觉相似案例进行验证，通过迭代重评估纠正了智能体的过度自信。所提出的系统将感知（视觉智能体）与元推理（协调器）分离，实现了可扩展且可解释的多智能体AI。这一蓝图可扩展至诊断、生物学及其他对信任要求严格的领域。所有模型、提示、结果及系统组件，包括完整的软件源代码，均已公开发布于Github以支持可重复性、透明性及社区基准测试：https://github.com/Applied-AI-Research-Lab/Orchestrator-Agent-Trust。

English

Modern Artificial Intelligence (AI) increasingly relies on multi-agent architectures that blend visual and language understanding. Yet, a pressing challenge remains: How can we trust these agents especially in zero-shot settings with no fine-tuning? We introduce a novel modular Agentic AI visual classification framework that integrates generalist multimodal agents with a non-visual reasoning orchestrator and a Retrieval-Augmented Generation (RAG) module. Applied to apple leaf disease diagnosis, we benchmark three configurations: (I) zero-shot with confidence-based orchestration, (II) fine-tuned agents with improved performance, and (III) trust-calibrated orchestration enhanced by CLIP-based image retrieval and re-evaluation loops. Using confidence calibration metrics (ECE, OCR, CCC), the orchestrator modulates trust across agents. Our results demonstrate a 77.94\% accuracy improvement in the zero-shot setting using trust-aware orchestration and RAG, achieving 85.63\% overall. GPT-4o showed better calibration, while Qwen-2.5-VL displayed overconfidence. Furthermore, image-RAG grounded predictions with visually similar cases, enabling correction of agent overconfidence via iterative re-evaluation. The proposed system separates perception (vision agents) from meta-reasoning (orchestrator), enabling scalable and interpretable multi-agent AI. This blueprint is extensible to diagnostics, biology, and other trust-critical domains. All models, prompts, results, and system components including the complete software source code are openly released to support reproducibility, transparency, and community benchmarking at Github: https://github.com/Applied-AI-Research-Lab/Orchestrator-Agent-Trust

編排者-代理信任：具備信任感知編排與基於RAG推理的模組化代理式AI視覺分類系統

Orchestrator-Agent Trust: A Modular Agentic AI Visual Classification System with Trust-Aware Orchestration and RAG-Based Reasoning

摘要

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