The research paper Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG explains how the next generation of AI, called Agentic RAG (Agentic Retrieval-Augmented Generation), goes beyond today’s static “chatbot” model and enables AI to behave more like an autonomous knowledge worker. Traditional AI models often make mistakes because they rely only on what they were trained on. Agentic RAG fixes this by allowing AI to actively search for information, evaluate what it finds, refine its approach, and use external tools or data sources to reach better answers. Instead of producing a single response, the AI can plan, verify, and correct itself, just as a skilled employee would when completing research, writing reports, analysing documents, or making decisions. For business leaders, this matters because it dramatically reduces hallucinations and increases trust, while enabling automation of higher-value work such as contract analysis, customer support, compliance checks, risk assessment, and research synthesis. In short, this paper outlines how Agentic RAG enables AI systems that don’t just retrieve information, they think, adapt, and act, unlocking significant efficiency gains and creating a pathway to true digital workforce augmentation.

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Key point: This paper argues that Agentic RAG transforms AI from a passive text generator into an autonomous problem-solver that can search, evaluate, and self-correct using external knowledge sources, enabling more accurate and reliable results for complex business tasks.