Context Windows Forget What Matters — I Built a Usage-Reinforced Decay Engine for AI Agent Memory
Emmimal P Alexander Jul 24, 2026 23 min read Share Image by the author, generated with ChatGPT (DALL·E) TL;DR: Most long-running AI agent memory systems evict old context using a sliding window: if something hasn’t been touched in N turns, it’s gone. This treats a foundational fact stated once on turn 1 and a throwaway debug log from turn 40 as identical — whichever is older loses, no matter how many times either one has actually been used. I built a memory engine that scores retention using the Ebbinghaus forgetting curve, where every recall reinforces an item’s stability and pushes its eviction horizon out non-linearly.
- ▪Emmimal P Alexander Jul 24, 2026 23 min read Share Image by the author, generated with ChatGPT (DALL·E) TL;DR: Most long-running AI agent memory systems evict old context using a sliding window: if something hasn’t been touched in N turns,
- ▪This treats a foundational fact stated once on turn 1 and a throwaway debug log from turn 40 as identical — whichever is older loses, no matter how many times either one has actually been used.
- ▪I built a memory engine that scores retention using the Ebbinghaus forgetting curve, where every recall reinforces an item’s stability and pushes its eviction horizon out non-linearly.
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Large Language Models Context Windows Forget What Matters — I Built a Usage-Reinforced Decay Engine for AI Agent Memory How to build a usage-reinforced memory decay engine for AI agent memory that outperforms naive recency-window pruning on long-running, multi-session tasks — with a deterministic, zero-dependency Python implementation you can verify yourself. Emmimal P Alexander Jul 24, 2026 23 min read Share Image by the author, generated with ChatGPT (DALL·E) TL;DR: Most long-running AI agent memory systems evict old context using a sliding window: if something hasn’t been touched in N turns, it’s gone.
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