context-engineering

Optimize LLM context usage by analyzing token utilization and attention distribution to curate the smallest high-signal token set. Apply memory and multi-agent partitioning strategies to maintain reasoning quality and reduce token degradation in sustained interactions.

Middle-token loss and context degradation occur when attention distribution becomes skewed across long token sequences. Prevent this by detecting poisoning risks, assessing memory needs, and applying compaction or masking strategies to maintain high-signal context.

Generate a context health report by providing a context file to the evaluation engine. It analyzes utilization and risk factors, returning a structured evaluation with a health score, optimization recommendations, and config notes for budget thresholds.

Context engineering works for multi-agent workflows by recommending partitioning strategies and KV-cache optimizations. It maintains agent performance under tight token budgets by mitigating degradation and maximizing reasoning quality across distributed tasks.

The best way to mitigate context poisoning risk is to analyze the context for utilization and risk factors, then apply memory optimization and partitioning strategies. This isolates corrupted inputs and preserves the integrity of the high-signal token set.