I have built simple RAG systems before, but now I want to add an agentic layer that can perform multi-hop reasoning across a knowledge base. The problem is that the 'agent' often gets stuck in loops or hallucinates when the data is ambiguous.
Does anyone have a guide on building an end-to-end agentic RAG project that handles:
- Context retrieval validation.
- Self-correction during the reasoning loop.
- Source citation accuracy.
I am looking for tips on making this reliable enough for internal company use.
Reliable agentic RAG requires implementing strict deterministic guardrails, schema-based retrieval validation, and explicit exit criteria to prevent infinite reasoning loops and hallucinations.
1 answer
Stop chasing the hype of autonomous agents until you have implemented deterministic guardrails for every retrieval step. If your system is looping, your exit criteria are non-existent, so force the agent to validate every document chunk against a strict schema before allowing it to proceed to the synthesis phase.
Exactly, Carolyn Lewis. It is beyond frustrating to see loops happen because developers ignore exit criteria. Forcing validation before synthesis is the only way to keep these systems from spiraling out of control.
Carolyn Lewis, I’ve been trying to implement schema validation, but I’m constantly worried my logic is flawed. It’s reassuring to hear that deterministic guardrails are actually the standard approach for these agents.