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Agentic RAG

Retrieval-augmented generation wrapped in an agent workflow with planning, tool routing, evidence reflection, and answer synthesis.

Agentic RAG is a retrieval-augmented generation system wrapped in an agent workflow rather than a single retrieve-then-answer call. In Bayer PRINCE, the workflow clarifies intent, plans, dispatches specialized researcher steps over unstructured PDFs and structured databases, reflects on whether evidence is sufficient, and only then asks a writer agent to synthesize the final answer.¹

The Bayer case shows why the agentic layer matters in enterprise domains: the hard part is not just vector search, but routing between overlapping tools and data contracts. PRINCE uses RAG for unstructured study reports, Text-to-SQL for structured metadata, hybrid retrieval, query expansion, reranking, and a developing hierarchy of domain-specific researcher sub-agents so one flat tool list does not become unmanageable.¹

Agentic RAG also needs context discipline. Larger context windows did not remove the need to decide what each stage sees; PRINCE separates planning context, retrieval context, evidence context, and synthesis context to reduce context pollution and make debugging, evaluation, and improvement tractable.¹