The field of retrieval-augmented generation (RAG) systems is experiencing significant growth, with a focus on enhancing the effectiveness and interpretability of these systems. Recent developments have seen the integration of large language models (LLMs) with document retrieval mechanisms, leading to improved decision accuracy and structured agent collaboration. The use of LLM agents has also become a promising approach, enabling the interpretation of RAG tasks, especially for complex reasoning question-answering systems. Furthermore, the development of trainable open-source LLM agent frameworks has expanded the applicability of RAG systems to real-world applications. Noteworthy papers include: Agent Distillation, which proposes a framework for transferring task-solving behavior from LLM-based agents into smaller language models with retrieval and code tools. Vision Meets Language: A RAG-Augmented YOLOv8 Framework for Coffee Disease Diagnosis and Farmer Assistance, which introduces a novel AI-based precision agriculture system that combines vision and language models to identify potential diseases in tree leaves. AI-Supported Platform for System Monitoring and Decision-Making in Nuclear Waste Management with Large Language Models, which presents a multi-agent RAG system that integrates LLMs with document retrieval mechanisms to enhance decision accuracy. Agent-UniRAG: A Trainable Open-Source LLM Agent Framework for Unified Retrieval-Augmented Generation Systems, which proposes a novel approach for unified RAG systems using the LLM agent concept.