Embedding retrieval for agent memory collapses unpredictably (0.30–0.95 across models); structured fact stores and LLM summarization hold at 0.70–1.00, making implementation choice worth 60 points of task success.
September 24, 2026
Summary
Memory backend selection directly impacts agent reliability in multi-turn tasks. Testing with realistic cost accounting ($42.57 across 23,440 episodes) shows full replay is never economical—choosing the right store architecture delivers 2.7–3.9x better utility per dollar than naive approaches.
Why it matters
Memory backend selection directly impacts agent reliability in multi-turn tasks. Testing with realistic cost accounting ($42.57 across 23,440 episodes) shows full replay is never economical—choosing the right store architecture delivers 2.7–3.9x better utility per dollar than naive approaches.
Implementation verdict
Replaces vague "long-context" memory assumptions with measurable task-based benchmarking. Requires running MERIT tasks in your domain to validate memory backend choice; structured fact stores outperform embedding retrieval for updated-fact recall. Actionable now: use this to justify switching from vector-only memory to hybrid or write-through stores in production agents.
Sources
Dev Signal
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