context-degradation

Detect context degradation in long-running AI agents by analyzing attention distribution and calculating context health scores to surface degraded information regions. The tool identifies patterns like lost-in-middle and context poisoning to provide actionable remediation.

Lost-in-middle context degradation occurs when AI agents fail to process information located in the middle of long contexts, degrading performance in multi-agent systems. It is diagnosed via attention analysis to surface regions where information is being ignored or forgotten.

Prevent context poisoning in multi-turn dialogs by running contradiction detection and risk scoring on agent contexts. The tool identifies poisoned contexts and generates recovery strategies with actionable recommendations to maintain reliable conversation performance.

Yes, you can use this context degradation diagnostic tool with only numpy installed, as it is the sole runtime dependency required. The tool provides scripts and references to analyze agent health and mitigate degradation risks in your environment.

The best way to score context health across complex multi-agent task flows is to apply detection patterns for context distraction, confusion, and clash. The tool outputs a health score alongside actionable recommendations for resilient context handling and recovery.

Your AI agent fails to maintain context in long conversations due to context degradation patterns such as distraction, confusion, or clash. Diagnose these specific failures using context health scoring and attention distribution analysis to apply targeted remediation steps.