Long-term memory significantly enhances language-model agents but introduces a critical failure mode: retrieval surfaces relevant yet stale, deleted, or mutually inconsistent facts. A query about a current address may retrieve the correct address alongside deleted ones. The answer is present, but the memory set is unsafe. We reframe agent memory as a governed object-selection problem and present MemGov, a substrate-agnostic middleware layer between existing retrieval pipelines and agent context. MemGov wraps retrieved evidence as explicit memory objects, detects must-link and cannot-link constraints, and applies deterministic constraint-aware repair, returning either a certified safe memory set or an auditable obstruction certificate rather than silently passing unsafe combinations. Evaluations on controlled governance scenarios and MemoryAgentBench-style end-to-end tasks, deliberately separating retrieval quality from governance decisions, demonstrate that while raw retrieval recovers answer-bearing evidence successfully, it simultaneously returns detected unsafe memory combinations in 47-51% of cases. The same retrieval substrates governed by MemGov reduce unsafe merges to 0% while preserving answer utility within about 2-4 percentage points, with fully auditable reasoning for all repair and refusal decisions. Additional diagnostics assess eligibility, temporal constraints, and paired answer retention.