Robert Clapp(@RobertClapp):had an agent pull a 21-day-old architecture decision out of vector search and rebuild an entire module around a pattern we explicitly killed. vector similarity was high, so the model treated it as canon. the newer decision used slightly different phrasing, so it ranked lower in the query. because the memory had zero lineage attached, the agent couldn't tell whether the note was five minutes old or three weeks dead. without provenance metadata (timestamps, tool IDs, model versions) similarity search is just a confident liar. storing tool IDs and timestamps per write in sqlite fixed it. stale facts get expired by tool lane before retrieval even touches them.

2026.08.29 14:51

had an agent pull a 21-day-old architecture decision out of vector search and rebuild an entire module around a pattern we explicitly killed. vector similarity was high, so the model treated it as canon. the newer decision used slightly different phrasing, so it ranked lower in the query. because the memory had zero lineage attached, the agent couldn't tell whether the note was five minutes old or three weeks dead. without provenance metadata (timestamps, tool IDs, model versions) similarity search is just a confident liar. storing tool IDs and timestamps per write in sqlite fixed it. stale facts get expired by tool lane before retrieval even touches them.

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