Agent memory compaction condenses an AI agent's stored context by summarizing or pruning redundant data, preserving key facts while freeing space. It's used in long-running assistants and autonomous workflows to cut costs, speed responses, and avoid context overflow. Developers, researchers, and businesses running persistent AI agents benefit most.
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