Agentic RAG
Build an agentic RAG pipeline that retrieves, reasons, and acts
Traditional RAG pipelines retrieve documents and stuff them into a prompt. Agentic RAG goes further — the agent decides what to retrieve, evaluates relevance, reformulates queries, and chains multiple retrieval steps until it has enough context to answer accurately. This blueprint shows you how to build a production-ready agentic RAG pipeline on EigenForge.
Stack
Implementation
- 1
Index your knowledge base
Chunk your documents, generate embeddings, and store them in a vector database. EigenForge connects to all major vector stores out of the box.
- 2
Define the retrieval tool
Create a tool that the agent can call to search your vector database. Configure similarity thresholds, result counts, and metadata filters.
- 3
Build the agentic loop
Configure the agent to evaluate retrieved results for relevance, reformulate queries when results are insufficient, and chain multiple retrieval steps before generating a final answer.
- 4
Add guardrails and evaluation
Set up citation verification, hallucination detection, and answer quality evaluation. Test against a benchmark set of questions with known answers.
- 5
Deploy and monitor
Deploy your agentic RAG pipeline with full observability — trace every retrieval step, LLM call, and decision point in production.
What You Get
- Higher answer accuracy than naive RAG through multi-step retrieval
- Agent adapts retrieval strategy based on query complexity
- Full citation traceability from answer back to source documents
- Production-ready with monitoring, evaluation, and guardrails
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