Enterprise AI agents need governed memory, not larger retrieval stores

The author argues that agents working across a company fail because they lack the decisions and context recorded in threads, meetings, and DMs, not because the model is weak.

The approach stores distilled claims with source evidence and time, never overwrites facts, labels missing information explicitly, and resolves permissions before the model runs.

The report cites results on LongMemEval, including 99.8% top-ten evidence recall and $8.24 ingestion cost, and says an open-weight model can match frontier extraction quality.