DSO-Agent: A Distributed Streaming Orchestration Framework for Grounding, Trust, and Relevance in LLM-Based Agentic Reasoning

The proliferation of open Large Language Models (LLMs) built on the Transformer architecture presents unprecedented opportunities for developing sophisticated agentic AI systems. However, ensuring that such LLM-based agents produce outputs that are factually grounded, trustworthy, and contextually relevant remains a significant challenge. This is due in large part to well-known failure modes of LLMs – notably hallucinations (confident generation of false information) and subtle misinterpretations – as well as the difficulty of maintaining user intent focus over complex, multi-step tasks. Objective: This paper addresses these challenges by introducing DSO-Agent (Distributed Streaming Orchestration Agent), a novel framework designed to enhance the reliability and verifiability of agentic reasoning when utilizing open LLMs and combinations of those (such as Mistral, Llama, Gemma, and DeepSeek models).