Agentic Retrieval–Augmented Generation (RAG) systems have advanced the ability of large language models to handle complex, multi–step questions by dynamically planning and executing retrieval operations. However, these systems remain vulnerable to intermediate reasoning breakdowns, redundant retrieval, and evidence inconsistency, which collectively cause answer drift in multi–hop question answering. We propose MERMAIDS, a framework that augments Agentic RAG with a Structured Evidence Memory (SEM) module and a Cross-task Evidence Reuse Cache (ERC). Extracted evidence is organized into a lightweight knowledge graph following a claim–evidence–source–time schema, and a dedicated Conflict Detectionmodule identifies contradictions and triggers selective re-retrieval or answer confidence degradation. Experiments on HotpotQA, MuSiQue, MultiHop-RAG, and FEVER show that MERMAIDS achieves 5.9–8.6 point improvements in exact match accuracy over strong baselines, while reducing redundant retrieval steps by 28–35% and attaining conflict detection precision above 81%.