context-degradation

Diagnose long-context LLM agent failures by analyzing context structure and estimating attention distribution to identify lost-in-middle, distraction, confusion, clash, and poisoning failure modes. This produces actionable recommendations and a composite health score to prevent cascading errors.

Context poisoning in LLM retrieval is caused by error accumulation, contradictions, and hallucination-leaning markers. Detect poisoning risk by running specific heuristics over the agent session context, allowing you to truncate or restart with verified context before failures cascade.

Monitor LLM agent context windows in production by building automated monitoring that calculates a composite health score from utilization, lost-in-middle risk, and poisoning signals. This score triggers automated mitigation strategies like context compaction or isolation when degradation is detected.

Fix LLM context assembly issues by applying attention-aware guidance that flags critical information buried in attention-degraded regions. The recommended mitigation is edge placement, alongside write, select, compress, or isolate strategies to ensure critical data maintains model attention.

Yes, you can debug long conversations without external dependencies. The skill requires no dependencies and operates by analyzing the existing context structure of your LLM agent sessions to identify specific failure patterns and return concrete mitigation steps.

Apply context compaction or isolation for LLM agents when automated monitoring detects high lost-in-middle risk, context poisoning indicators, or a low composite health score. These resilience patterns prevent long-context failures from cascading into incorrect or irrelevant outputs.