How Much Memory Does Your Agent Actually Need?

This post details the calibration of agent memory dosage across eight models, revealing three distinct patterns: strong models with headroom benefit from the full guideline set, weaker models perform best with curated retrieval (a core set plus task-specific guidelines), and saturated models show no measurable gain. It introduces the AppWorld benchmark for evaluation and emphasizes that memory calibration, not just accumulation, is key to optimizing agent performance and cost. The post also highlights that the learning loop changes the guidance available to the agent, not the underlying model weights.