Context & memory failure
Context Drift
Gradual loss or distortion of important task context as a conversation or workflow progresses.
What failed
Context drift occurs when an AI system gradually loses, distorts, or deprioritizes important task context. The system may begin with the correct objective but drift away from the original request, business constraints, policy requirements, or earlier facts as the conversation or workflow progresses.
Architecture context
Multi-turn assistants, long document analysis, agent planning, coding agents, customer support workflows, enterprise search, and workflows that combine retrieval, memory, and tools.
Impact
Context drift is especially harmful in long conversations and agent workflows. A system may appear useful step by step while slowly moving away from the actual task. This can lead to incorrect recommendations, wrong tool calls, policy violations, and user frustration.
Symptoms
- The system forgets the original user request.
- It ignores constraints stated earlier.
- It changes the task without user approval.
- It uses irrelevant retrieved context.
- It answers a nearby but incorrect question.
- It contradicts earlier assumptions or decisions.
Detection signals
- Low similarity between current action and original task intent.
- Repeated introduction of irrelevant context.
- Final answer missing required constraints.
- Tool calls unrelated to the task goal.
- User corrections such as “that is not what I asked.”
Mitigations
- Summarize and preserve task state.
- Reconfirm important constraints before action.
- Use explicit workflow state machines.
- Add intent checks before tool calls.
- Add recovery behavior when uncertainty rises.
- Limit irrelevant context and stale memory.