Agentic Retrieval-Augmented Generation

Agentic Retrieval-Augmented Generation describes RAG systems that embed autonomous AI agents into traditional retrieval-augmented generation pipelines. These agents leverage agentic design patterns including reflection, planning, tool use, and multi-agent collaboration to dynamically manage retrieval strategies, iteratively refine contextual understanding, and adapt workflows to meet complex task requirements. The survey is a review of existing work rather than a new method; it examines how agentic RAG addresses part of the limitations of conventional RAG, such as single-pass retrieval and the absence of any check on whether retrieved context is relevant, at the cost of higher latency and more model calls per query. The survey covers foundational principles of agentic intelligence, implementation architectures, real-world applications across industries, and identifies scaling challenges while proposing future research directions for autonomous information retrieval and generation systems.

Related terms

• Retrieval-Augmented Generation Agentic RAG
• C-RAG: Certified Generation Risks for Retrieval-Augmented Language Models
• Hire Retrieval-Augmented Generation (RAG) developers
• IM-RAG (Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologues)
• RAG (Retrieval-Augmented Generation)

Related services: RAG development service, Single-agent system development.

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