AgentCore RAG Agents: Key Production Pitfalls Missing from Official Tutorials

A developer spent a month analyzing AgentCore's RAG and AI agent features after discovering a detailed Japanese-language walkthrough on Qiita that had no English coverage. The tutorial, built on AWS infrastructure, contains implicit assumptions about the Tokyo region's IAM roles and endpoint configurations that silently break deployments in other AWS regions. A critical undocumented limitation is that at 1,000-plus document scale, embedding model recall drops roughly 30% without hybrid search combining BM25 and vector methods. AgentCore differentiates itself from LangChain-style wrappers by treating retrieval as a first-class tool-calling action rather than a prompt engineering workaround. However, multi-turn conversation management beyond 20 turns exposes architectural gaps, requiring developers to build custom context-windowing solutions not covered in any getting-started guide.