context-degradation

AI agent performance degrades in long-running conversations due to context degradation patterns like lost-in-middle, context poisoning, and distraction. You can diagnose these issues by running a degradation detector to measure attention degradation and quantify risk.

To detect context poisoning and attention degradation in production agents, run a degradation detector script on your current context. It analyzes the session to provide a health score, surface risk indicators, and output actionable mitigation recommendations.

The best way to mitigate lost-in-middle and context clash issues is by applying architectural guidance such as context compaction, masking, partitioning, and isolation. These strategies, alongside practical recovery workflows, effectively resolve AI agent context degradation.

Yes, you need numpy installed to analyze AI context degradation. The degradation detector relies on this dependency to process your agent's context, measure attention degradation, and calculate a quantifiable risk health score.

Yes, you can use the degradation detector to monitor a long-running agent session. It surfaces degradation risks and provides actionable recommendations, allowing you to trigger mitigation strategies like context compaction or isolation when performance degrades.