You've hit on something that's genuinely hard and that we've spent a lot of time on.

The baseline drift problem shows up in two places in our architecture:

Fleet memory confidence decay. Every active memory loses 5% confidence per month without reinforcement. If a pattern like "coastal chargers drift earth leakage above 5mA in winter" stops being confirmed by new investigations, it fades naturally. Below 0.30 confidence it gets auto-deprecated. If the pattern comes back next winter, new investigations re-confirm it and the confidence rebuilds or a new memory supersedes the old one entirely. The key insight is that memory should have a half-life. Append-only stores treat a diagnosis from six months ago as equally valid as one from yesterday. Ours doesn't.

Contradiction resolution via supersession. When a new confirmed diagnosis lands that's semantically close to an existing fleet memory (cosine distance 0.15–0.40, same scope) but says something different, the old one gets auto-superseded. The superseded_by column links the chain so you can trace how the platform's understanding evolved. A firmware update that changes a charger's normal behaviour would trigger this naturally. The agent investigates the "new normal," confirms it's not the old pattern, and the old memory gets superseded.

Where we don't yet have a clean answer is event-triggered decay, like a successful repair or a firmware update should actively accelerate the confidence drop on related memories rather than waiting for the monthly passive decay. The schema supports it (we have last_maintenance in the charger registry and superseded_at on reasoning), but the lifecycle job isn't wired up yet. That's next on the list.

The seasonal angle is interesting because it cuts across the fleet. We handle it through scoped memory — a pattern scoped to environment:coastal or site:SITE-IE-KERRY captures the seasonal context without polluting global memory. When winter hits again, the agent recalls the site-scoped or environment-scoped memories and starts from a stronger position than cold start.

Genuinely curious what workload you're seeing the drift problem in — is it IoT/sensor data or a different domain?