Why LLM Agent Memory Silently Degrades at Scale — and How to Catch It

In production AI systems, memory retrieval failures rarely trigger errors or alerts — instead, agents quietly return plausible but incorrect answers while all monitoring dashboards appear normal. The root cause is that vector-based retrieval systems always return the nearest matching chunks, even when no genuinely relevant data exists, leaving the language model to generate fluent but wrong responses. This problem worsens significantly as data scales up: benchmark results show leading retrieval accuracy dropping from 92.5 on smaller datasets to just 48.6 when corpus size reaches 10 million tokens. Temporal reasoning is especially vulnerable, as semantically similar phrases like 'cancelled subscription' and 'asked about cancelling' can score nearly identically in embedding space, causing the model to pick the wrong fact. The recommended defence is adding explicit verification checks on retrieved chunks before they reach the model, so retrieval quality failures surface loudly rather than drifting undetected for days or weeks.