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

Summary

This paper talks about Orchestrator-Agent Trust, a new AI system that uses multiple specialized agents to work together on visual classification tasks like identifying apple leaf diseases. The system combines agents that analyze images with a reasoning orchestrator that helps decide the best answer by considering the agents' confidence and explanations.

What's the problem?

The problem is that AI models, especially when they haven't been trained on a specific task, can give wrong or overconfident answers and it's hard to know which model to trust. This is a big issue in fields where mistakes can cause real problems, like agriculture or medicine.

What's the solution?

The system solves this by having multiple AI agents each give their guess with a confidence score and explanation. Then, a non-visual orchestrator reviews all answers, weighs their trustworthiness, and can ask the agents to reconsider their answers using additional image retrieval techniques. This makes the final decision more accurate and trustworthy.

Why it matters?

This matters because it improves how much we can trust AI decisions in important real-world tasks by making the AI system not only more accurate but also more explainable and reliable, which is critical for high-stakes uses like diagnosing plant diseases.