Aglet

Learn From Repeated Stale Agent Context

Learning from stale context means making time and revision part of the workflow's evidence. Preserve the artifact that changed, the claim it invalidated, and the label or refresh that resolved it. Turn those observations into reusable manifests and review triggers.

Keep the lesson for the next incident

  1. Classify freshness failures

    Group incidents by delayed collection, missing revision, wrong source, unmarked historical context, changed requirement, stale data sample, or absent refresh cutoff. Keep one artifact and affected claim for each class. Distinguish intentional time-based analysis from an accidental currentness claim.

  2. Keep context exemplars

    Retain compact manifests showing source, timestamp, revision, scope, freshness rule, historical label, and refresh owner. Include a changed-case comparison that demonstrates when a claim must be reevaluated. Update examples when source boundaries or decision windows change.

  3. Review recurrence and ownership

    Assign owners for context collection, source revisions, and result review as needed. Sample current-facing tasks on a defined cadence and track stale-artifact classes. Reopen investigation for an unmarked old input, repeated revision mismatch, or a conclusion reused beyond its stated freshness.

What to carry forward

Close the learning record with freshness failure classes, context manifests, revision examples, owners, and a trigger for unmarked or over-aged inputs. Preserve historical analysis as explicit behavior. The durable result is a time-aware handoff that makes revalidation proportional to actual change.

Keep the decision with the work.

Use a Work Item in Aglet to record the problem, the evidence you have, and the next decision. Add an owner and priority, then keep updates in the discussion so the next person can follow the reasoning.

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