Fan-out and fan-in graph design, learned through accommodation research

Using Airbnb listing research as the example, the steps for implementing graph engineering end to end are as follows. Before running the whole graph, test a single pipeline end to end on one area only, Gangnam-gu, and set up a structure that manages state across three folders — markdown, raw and logs — under a run ID. Dynamic page rendering and proxy handling are left to a single call to a web-scraper API, and validation is written programmatically as Python scripts rather than done by an LLM, then reused at every node. Next, the main agent spins up subagents for three areas — Gangnam, Mapo and Jongno — in parallel to fan out the research, gathers the results that passed parallel validation into one context (fan-in) and has an LLM recommend three final candidates; that recommendation is validated again programmatically and, only if it passes, handed to a human-in-the-loop node where a person approves it. This sequence — pipeline test → turn into nodes → provide parallel tools → fan-out/fan-in → validation → approval — can serve as is as the backbone of a hands-on exercise in agent workflow design.

This is a note: my own write-up after reading several sources. It has no source link; events checked against a source are kept as records.