Dynamic Multi-Agent RAG Pathway provides a modular architecture where each agent handles specific tasks—such as document retrieval, vector search, context summarization, or generation—while a central orchestrator dynamically routes inputs and outputs between them. Developers can define custom agents, assemble pipelines via simple configuration files, and leverage built-in logging, monitoring, and plugin support. This framework accelerates development of complex RAG-based solutions, enabling adaptive task decomposition and parallel processing to improve throughput and accuracy.

Llama Guard is an AI-driven agent focused on cybersecurity. It continuously monitors network activity, identifies potential threats, and automatically responds to mitigate risks. By utilizing machine learning algorithms, Llama Guard adapts to new vulnerabilities, providing real-time protection for organizations. Its functionalities include threat analysis, incident response, and compliance management, making it an essential tool for safeguarding critical information and minimizing security breaches.