Agentic RAG, which decomposes a complex question into dependency-aware sub-questions, has become the dominant paradigm for multi-hop QA. Yet existing systems can only repair errors at the failed step or simply return to the previous step and retry, and thus fail to handle two pervasive failure modes: inaccurate decomposition, where the plan is flawed before execution begins, and error propagation, where an upstream mistake surfaces downstream. We propose that addressing both requires global self-correction, the ability to identify the root cause of a failure and repair it at the right location. To this end, we propose **LoCoRAG**, a globally self-correcting agentic RAG framework. LoCoRAG maintains a persistent dual memory through a two-stage thinker-generator pipeline, separating what the corpus contains from what the system has concluded. Then, a memory-grounded failure localization module attributes each failure to specific causes and pinpoints its true origin. Together, LoCoRAG drives a self-correcting global replan that selects a matched repair, from local refinement to upstream rollback with downstream regeneration. Across existing multi-hop QA benchmarks, LoCoRAG achieves SOTA performance on all datasets and all metrics, with a 10.8 points average LLM-Acc gain over the strongest baseline on 3-hop-or-deeper scenarios.