How to Benchmark AI Agent Memory Systems Rigorously and Reproducibly

AI agent memory systems are increasingly marketed with claims like 'infinite context' and 'perfect recall,' but these often fail under real-world workloads. A rigorous benchmarking methodology called MemoryBench has been proposed to evaluate such systems beyond synthetic, vendor-curated tests. The framework covers key memory types — including vector stores, recurrent summaries, structured slot memory, and neural memory — each with distinct latency profiles and failure modes. MemoryBench assesses end-to-end agent performance across tasks such as fact retrieval and temporal reasoning, using metrics like Recall@k, Mean Reciprocal Rank, and P95 latency on corpora scaling to millions of items. The approach emphasizes testing at production scale, isolating variables, and reporting statistical distributions rather than averages to surface real trade-offs.