Building a Context-Aware Customer Support Agent on MongoDB Atlas

A tutorial by Samuel Molling describes how to build a customer support agent using MongoDB Atlas. The core is a retrieval-augmented generation pipeline that combines vector and lexical search, merges the ranked results with Reciprocal Rank Fusion, and reranks candidates with a cross-encoder before an LLM writes the answer. The author argues that real support conversations are context-dependent, so follow-up questions like "how much is that?" cannot be searched on their own and must be rewritten using conversation history. Answers may come from documentation, resolved past tickets, or customer account data, and some requests must be escalated to a human. The article therefore adds query rewriting, conversation memory, multiple data sources, customer entitlements, operational tools, confidence scoring, and escalation logic around the retrieval layer.