Agent Memory in the Wild: Reliability, Efficiency, and Evaluation

Large language model (LLM) agents are moving from single-shot conversations to long-horizon work. Support agents follow a customer for months, assistants accumulate preferences, and coding agents revisit one repository across hundreds of sessions. Context windows cannot hold such histories, and wholesale replay is slow, costly, and degrades reasoning, so a dedicated memory layer has become standard agent infrastructure. Memory systems extract salient facts from interactions, consolidate them into vector indexes, temporal knowledge graphs, or paged context, and retrieve them into future contexts.