What We Learned Evaluating Agent Memory:The Results (Part 2)

The evaluation of Couchbase Agent Memory reveals that raw messages outperform summaries across datasets due to the importance of exact wording in Single-Session Assistant questions, where summarization often loses critical details. While summaries enhance SSP and MS scores by reducing noise and highlighting preferences, they are less effective for scenarios requiring precise entity matching. Experiments show that more context does not necessarily yield better answers, as excessive information introduces noise. Temporal grounding poses a significant challenge, with failures stemming from the model's inability to resolve relative time expressions accurately. Hybrid search combining vector similarity, BM25, and entity extraction improves temporal reasoning by directly matching named entities, though it may affect Multi-Session precision. The study emphasizes that agent memory retrieval differs from document RAG, requiring configuration adjustments based on question types and scale. Evaluation methods significantly influence outcomes, highlighting the need for consistent judging to produce interpretable results. Temporal grounding improvements, such as prepending session dates, offer actionable insights for enhancing recall. The study concludes that assumptions about small-haystack optimization and RAG-like evaluation may not hold at production scale, urging teams to test systems under realistic conditions to ensure functionality.

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