context-degradation

Mitigate context degradation patterns using compaction, masking, partitioning, and isolation strategies. The detector provides architectural guidance and monitoring hooks to implement reliable context management and recovery in production agent pipelines.

Monitor context health in long-running conversations by integrating modular scoring hooks into existing agent pipelines. This surfaces degradation patterns like distraction and clash, enabling reliable context management and recovery for multi-document environments.

Yes, you can use this context degradation toolkit with an existing agent pipeline. It is designed as a modular, pluggable system that provides monitoring hooks and architectural guidance for production agents requiring reliable context management.

Context clash is a degradation pattern affecting production agents by introducing conflicting information into the context window. It degrades attention distribution and reliability, requiring diagnostic detection and isolation mitigation strategies to resolve.

Yes, you need numpy installed to diagnose context degradation in multi-document environments. It serves as the underlying dependency for the toolkit to compute attention distribution, poisoning indicators, and context health scores.

The best way to recover from context distraction in agent systems is to apply partitioning and masking mitigation strategies. The toolkit diagnoses distraction patterns and provides actionable recovery recommendations to restore context health.