Agent Memory Handoff Cuts Active Context by 98.6% While Preserving Recall

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A memory handoff system for AI agents replaces large conversation windows with compact summaries while storing the original text externally for later retrieval. Across 10,241 completed runs, the system reduced the replaced portion of active context from over 1.1 billion characters to roughly 15.4 million — a 98.65% character reduction. Retained segments are classified as keep, compress, or drop, allowing exact source text to remain accessible outside the prompt without burdening the model on every turn. A controlled synthetic test showed that a model using handoff-plus-recall answered all 12 questions correctly, matching full-history performance while using significantly fewer input tokens. The authors caution that this is a limited controlled result and that retrieval calls were made by the test harness, not autonomously by an agent.

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