Retrieval-Augmented Generation (RAG) has become the gold standard for grounding LLMs in proprietary data. However, for high-stakes enterprise environments, standard "linear" RAG pipelines often fall short. Noise in retrieval leads to "retrieval-induced" hallucinations, while model synthesis errors lead to "generation-induced" hallucinations. To move AI from an experimental prototype to a reliable production tool, we must move beyond simple linear chains.
In this session, I will demonstrate a robust Multi-Agent RAG architecture built with LangGraph and PostgreSQL (pgvector). Drawing from my recent research, I show how to transition to a state-based agentic workflow that treats hallucination mitigation as a multi-stage process.
We will deep-dive into:
Proactive Context Optimization: Using specialized agents for Query Rewriting and Batch Reranking to filter noise before it reaches the generator.
Reactive Verification: Implementing a dedicated "Judge" agent that cross-checks generated answers against source evidence, creating a feedback loop to refine answers rather than guessing.
Enterprise Scalability: Managing state and high-dimensional vector data in a production-ready environment.
Attendees will leave with a validated blueprint for building self-correcting AI systems that provide traceable, verifiable, and hallucination-free answers.