Why Agents Fail at Long-Horizon Tasks: The Memory Debate
Inevitable_Fee1895 · reddit · 2026-07-05
The author points out that most agent frameworks default to "conversation replay" for memory, which fails in long-horizon tasks; larger context windows won't fix this. Citing Chroma's context-rot report (which evaluated 18 models and found accuracy drops significantly before token limits are reached, with the worst degradation in the middle of the window) and the paper "AI Agents Need Memory Control Over More Context," they advocate for bounded internal states and active memory submission per turn. This approach replaces infinitely growing conversation replays to reduce drift and hallucinations.
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