Reproduce “Agentic AI with Orchestrator-Agent Trust: A Modular Visual Classification Framework with Trust-Aware Orchestration and RAG-Based Reasoning” on OpenResearch
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About this paper
Konstantinos I. Roumeliotis, Ranjan Sapkota, Manoj Karkee, Nikolaos D. Tselikas
A modular agentic AI framework leverages trust-aware orchestration and retrieval-augmented generation (RAG) to improve the accuracy and reliability of generalist multimodal agents in zero-shot visual classification tasks. This system achieved a 77.94% relative accuracy improvement in diagnosing apple leaf diseases without requiring agent fine-tuning.
Key insights
- Explicitly modeling and quantifying agent trust, beyond self-reported confidence, significantly enhances the reliability and decision-making capabilities of agentic AI systems.
- Retrieval-Augmented Generation (RAG) can be effectively extended to visual contexts (Image-RAG) to mitigate agent overconfidence and provide evidence-grounded reasoning in zero-shot visual classification.
- Achieving high accuracy and trustworthiness in zero-shot settings is possible without expensive fine-tuning by combining sophisticated trust-aware orchestration and RAG-based re-evaluation.
Summary by alphaXiv.
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