context-surfing

To prevent AI context drift during long-running coding tasks, you need continuous context health monitoring that detects degradation signals like contradictions or hallucinations before they corrupt output. This ensures maximum output fidelity throughout multi-step execution.

Context window health monitoring for complex refactors is the continuous assessment of AI context coherence to ensure peak performance. It actively identifies degradation signals like contradictions and manages clean handoffs to maintain output fidelity.

To manage clean handoffs when AI context quality drops, the system saves the current execution state during graceful exits. This allows seamless resumption of long-running tasks without losing progress or introducing silent errors.

Yes, intent-framed-agent is required for context-surfing as it provides the wave anchor. You also need plan-interview for external grounding via plan files to ensure maximum output fidelity during multi-step execution.

AI output degrades in long-running tasks due to context drift, where the AI's understanding silently degrades causing contradictions or hallucinations. Fix it by applying continuous context health monitoring to detect degradation and ensure output fidelity.