Old knowledge goes wrong quietly
Knowledge an auto research loop stores once gradually diverges from fact over time. The problem is that this staleness does not surface suddenly but proceeds quietly. Without freshness management, the loop keeps using old facts with confidence.
The key is not to try to refresh all knowledge on the same cycle. Distinguish frequently changing knowledge from rarely changing knowledge and set different refresh cycles.
TTL and staleness detection
TTL assigns different validity periods by knowledge type. Set it short for frequently changing information like prices or policies and long for stable information like definitions or principles. Staleness detection catches it as a freshness-decay signal when newly collected information conflicts with a stored value.
Full guide: from planning to operations
In planning, define freshness goals as numbers. For example, set an expired-fact usage rate of 2% or lower, a delay from staleness detection to re-investigation within 24 hours, and a re-investigation budget ceiling. TTL should be tuned by type, not fixed, and a rule that automatically shortens TTL for types whose recent change frequency rises is useful. This lets you allocate refresh cost by type.
Failure patterns usually come from missed expiration handling and re-investigation storms. Using expired facts as-is lets old errors pollute responses, while re-investigating everything at once spikes cost. To prevent this, immediately exclude expired facts from use but schedule re-investigation by importance priority. For recovery, if a re-investigation result conflicts with the stored value, also invalidate inferences that depended on the old value and log the update history.
On the operations checklist, record each fact's collection time, TTL, and last re-investigation time in standard fields. Use expired-fact ratio, re-investigation queue wait time, conflict rate, and per-type refresh frequency as observability fields. Keep masking rules so originals collected during re-investigation contain no personal data.
The continuous improvement loop analyzes facts used after expiry and facts whose values changed on re-investigation weekly. Shorten TTL for types shown to change often and lengthen it for rarely changing types to save re-investigation cost. Freshness policy should be a living rule that keeps adjusting to real change patterns, not a fixed expiration rule.
Key takeaways
In short, because knowledge ages quietly, active freshness management is needed. Apply per-type TTL, detect conflicts as staleness signals, and schedule re-investigation by importance priority. Observe expired-fact usage and per-type change frequency to keep tuning TTL so old knowledge does not pollute responses.