RAG Workflow and Loop Engineering: The Dispatcher That Decides When to Loop and When to Stop
| Source: Towards Data Science
Tags: RAG, agentic-RAG, enterprise-AI, retrieval-augmented-generation, LLM-engineering, document-intelligence
Towards Data Science closes a 13-part enterprise RAG series by introducing the dispatcher layer — a deterministic code component that decides which retrieval loops to run, how many times to iterate, and when to stop, without handing orchestration to the LLM.
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This long-form Towards Data Science piece closes Part III of an enterprise RAG engineering series by building the dispatcher: the component that parses a question, selects which loop patterns to run, and bounds each one — without letting the LLM decide its own iteration logic. The core argument: handing orchestration to an LLM in an enterprise pipeline creates unpredictable, hard-to-audit behavior. A readable code dispatcher that composes loop patterns explicitly (re-retrieval, listing aggregation, synthesis passes) gives engineers control over stopping conditions and retry depth. The article demonstrates the system on the NIST Cybersecurity Framework with a real multi-hop query: 'What are all the Categories under GOVERN, and which one covers supply chain risk?' — a question that simultaneously fires TOC retrieval, list aggregation, and synthesis. A runnable notebook companion shows the IterationRecord history and the should_continue function in action. This is Part 13 of the series; newcomers are directed to the overview article first. It represents a practitioner-grade approach to agentic RAG that prioritizes auditability and predictability over flexibility.