2 posts tagged with "schema-migration"

You shipped a better memory format on a Tuesday. The new schema splits a freeform summary string into structured fields — entities, preferences, last_verified_at — because the old blob was hard to retrieve against and impossible to update cleanly. The change is obviously correct. It passes review. It ships.

What you did not notice is that every memory written before Tuesday is now subtly wrong. Some records still have the old summary field and no entities, so the retrieval code that now keys on entities skips them. A few have a summary that the new parser interprets as an empty preference set. The agent didn't crash. It just quietly forgot a year of accumulated context, and nobody filed a bug because nothing looked broken — the agent still answered, just worse.

Your team ships a routine database migration on Tuesday — renaming last_login_date to last_activity_ts and expanding its semantics to include API calls. No service breaks. Tests pass. Dashboards update. But your AI agent, the one answering customer questions about user engagement, silently starts generating wrong answers. No error, no alert, no stack trace. It just confidently reasons over a world that no longer exists.

This is the schema migration problem that almost nobody in AI engineering has mapped. Your agent builds an implicit model of your data from tool descriptions, few-shot examples, and retrieval context. When the underlying schema changes, that model becomes a lie — and the agent has no mechanism to detect the contradiction.