Author shows lossy AI memory compaction metrics

The author argues that standard AI memory compression techniques are inherently lossy, systematically destroying critical context like exact identifiers, negative knowledge, and goals, which leads to costly downstream re-fetches and repeated mistakes. To solve this, the article introduces a functional probe harness and engineering strategies—such as structured schemas, protected-set diff tests, and archiving raw segments to pointers—to measure and improve summarization fidelity for long-running agents. Open article →