Long-horizon LLM agents often fail not because relevant information is absent, but because semantically relevant memories are epistemically invalid: stale observations, failed tool traces, or claims contradicted by newer verified evidence are retrieved and treated as support. We introduce HELM (Hierarchical Epistemic Learned Memory), a memory framework that makes this reliability problem explicit. HELM exposes memory through logged read/write/consolidate/prune operations and stores each item with provenance and epistemic state. Its three-tier Semantic-HNSW Nested Memory (SHNM) links episodic tool traces to consolidated recalls and thematic indices, enabling coarse-to-fine retrieval that can be audited back to concrete evidence spans. Epistemic governance filters and re-ranks recalls by status, freshness, consistency, recency, and provenance, while a lightweight learned controller decides when memory operations are worth their cost. Across five long-horizon benchmarks, HELM improves success over strong memory baselines on GAIA, WebArena, Mind2Web, StreamBench, and LifelongAgentBench while reducing effective context tokens and epistemic errors. New scaling, training-free, and governance-only ablations show that the main gains persist with a 32B backbone and that epistemic governance alone explains a large share of the reliability improvement.