context-optimization

Reduce token costs at context window limits by applying context compaction, observation masking, and context partitioning to summarize history and isolate focused sub-agent contexts.

Context compaction optimizes AI performance by summarizing and shrinking conversational history and tool outputs, directly reducing the token count processed during inference to lower latency.

KV-cache optimization improves inference efficiency for long-running agents by strategically organizing cached data, which minimizes redundant processing and accelerates response times in production systems.

You can use context partitioning in production environments to manage large tool outputs by dividing work across sub-agents, ensuring isolated and focused contexts that maintain performance.

The best way to mask less critical tool outputs for prompt engineering is through observation masking, which strategically hides redundant data to save tokens without losing essential context.

Avoid context optimization strategies when your AI system processes minimal conversational history or operates well below token limits, as the overhead of compaction and partitioning may outweigh latency benefits.