Retrieval-Augmented Generation (RAG) systems are being improved through the integration of AI agents, according to an analysis by Shubham Sharma for VentureBeat. While traditional RAG systems combine data retrieval with language models to provide contextual answers, they are limited to single knowledge sources. The new “agentic RAG” approach incorporates AI agents that can access multiple data sources, validate information, and make reasoning-based decisions about data retrieval. Weaviate’s technology experts explain that these agents can utilize various tools like web search, calculators, and software APIs to gather comprehensive information. While the technology shows promise for more accurate and versatile data processing, it currently faces challenges including latency issues and higher computational costs. Implementation has been made easier through frameworks like DSPy and LangChain, though proper failure handling remains crucial for reliability.