Memora also introduces a policy-guided retriever that, rather than returning the top-k semantically similar items in a single shot, iteratively refines its query, expands through cue anchors to surface related-but-not-similar memories, and decides when to stop.
“The deepest flaw in current agent memory is that it mistakes retrieval for memory. A vector store is superb at finding text that looks relevant. An enterprise agent needs more than resemblance. It needs to know what has changed, what still holds true, and what should never be recalled in the task at hand,” said Sanchit Vir Gogia, chief analyst at Greyhound Research.
Memora is interesting precisely because it refuses that shortcut, Gogia noted. It separates the rich detail of a memory from the handle used to find it, indexing a stable abstraction and a set of cue anchors while keeping the full content intact beneath them. Retrieval then becomes an act of navigation rather than a single hopeful guess, as the system re-queries, widens its search, or stops once it has enough, he added.
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