Agentic Retrieval-Augmented Generation (Agentic RAG)

An Agentic Retrieval-Augmented Generation (Agentic RAG) is a knowledge-grounded context-aware agent generation technique that combines language models with retrieval systems within agent workflows.

• AKA: Retrieval-Augmented Agent, RAG Agent.
• Context:
  • It can typically fetch Relevant Documents from knowledge bases before generating responses.
  • It can typically integrate Retrieval Calls into agent planning processes for dynamic sourcing.
  • It can typically reduce Hallucinations through factual grounding in retrieved content.
  • ...
  • It can often maintain Retrieval Memorys to avoid repeated querys.
  • It can often implement Fallback Mechanisms when retrieval failures occur.
  • ...
  • It can range from being a Simple Agentic RAG to being a Sophisticated Agentic RAG, depending on its retrieval complexity.
  • It can range from being a Single-Source Agentic RAG to being a Multi-Source Agentic RAG, depending on its source diversity.
  • ...
  • It can coordinate Retrieval Timings with generation phases.
  • It can optimize Query Formulations for retrieval effectiveness.
  • It can balance Retrieval Costs against accuracy gains.
  • ...
• Example(s):
  • Domain-Specific Agentic RAGs, such as:
    • Policy Retrieval Agent fetching company policy documents for compliance querys.
    • Technical Support RAG searching product manuals for troubleshooting guidance.
  • Framework-Integrated Agentic RAGs, such as:
    • LangChain RAG Agent combining search tools with LLM generation.
    • LangGraph RAG Node performing retrievals within graph workflows.
  • Multi-Modal Agentic RAGs, such as:
    • Image-Text RAG Agent retrieving visual content alongside text documents.
    • Code RAG Agent fetching code examples for programming assistance.
  • ...
• Counter-Example(s):
  • Pure Generative Models, which rely solely on training data without retrieval.
  • Search-Only Agents, which retrieve without generation integration.
  • Static Knowledge Systems, which use fixed knowledge without dynamic retrieval.
• See: Knowledge Base, Retrieval Augmented Generation (RAG) Framework, AI Agent Frameworks, RAG Pipeline.

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