Financial agents need two critical capabilities that set them apart from simple chatbots: the ability to ground responses in authoritative documents through intelligent retrieval, and continuous evaluation to ensure accuracy in regulated environments. Agentic RAG transforms retrieval from a passive lookup into an active reasoning loop, while long-term memory enables personalization across sessions. Together with robust evaluation frameworks, these capabilities create production-ready financial assistants.
Multi-Agent RAG and Building Complete Systems
Standard RAG retrieves from a single source, but real problems often require information from multiple specialized domains. Multi-Agent RAG coordinates multiple retrieval specialists, each expert in querying specific data sources, then synthesizes their findings into coherent answers. In this final post of the series, I’ll explore Multi-Agent RAG patterns and bring together everything we’ve learned into complete, production-ready systems.
Agentic RAG and Agent Evaluation Strategies
Traditional RAG (Retrieval-Augmented Generation) follows a fixed pattern: query in, documents out, response generated. But what if the agent could decide when and how to retrieve? Agentic RAG gives agents control over their own knowledge acquisition. In this post, I’ll explore this dynamic approach to retrieval, then tackle the equally important question: how do we know if our agents actually work?
Agentic RAG and Human-in-the-Loop with LangGraph
Traditional RAG is a one-shot process: retrieve documents, generate answer, done. Agentic RAG breaks this limitation—agents can evaluate retrieval quality, reformulate queries, and iterate until they find what they need. Combined with human-in-the-loop patterns, you build systems that are both autonomous and controllable.