Memory failure
Memory Drift
When AI systems rely on memory that is stale, incorrect, irrelevant, or misapplied across sessions and workflows.
What failed
Memory drift occurs when an AI system stores, retrieves, or relies on memory that is stale, incorrect, irrelevant, or misapplied. Unlike context drift, which happens within a current interaction, memory drift often spans sessions, users, workflows, or long-lived state.
Architecture context
Personal assistants, sales copilots, support agents, HR assistants, coding agents, enterprise workflow agents, and systems that store preferences, user history, task state, or organizational context.
Impact
Long-term memory can make AI systems more useful, but it also creates risk. Incorrect memory can personalize the wrong way, leak assumptions across contexts, bias future responses, or cause agents to act on outdated information.
Symptoms
- The system uses an outdated preference or fact.
- It applies memory from the wrong user, customer, or project.
- It treats a temporary instruction as permanent.
- It ignores updated information.
- It carries forward a mistaken assumption.
Detection signals
- Conflicts between memory and current context.
- User corrections about remembered facts.
- Memory retrieved from the wrong scope.
- Increased errors in personalized workflows.
- Actions based on old state after a change event.
Mitigations
- Scope memory by user, tenant, project, and purpose.
- Add memory expiration and review.
- Confirm sensitive memory before use.
- Support correction and deletion.
- Detect conflicts with current context.
- Avoid storing temporary instructions as durable memory.