Agentic RAG

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.^{source: martin-fowler-bayer-reliable-agentic-ai-systems-2026.md}

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.^{source: martin-fowler-bayer-reliable-agentic-ai-systems-2026.md}

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.^{source: martin-fowler-bayer-reliable-agentic-ai-systems-2026.md}

Resources

Building Reliable Agentic AI Systems