context-optimization

You can reduce token costs for long-running AI conversations by applying context compaction, which summarizes older conversation turns to free up context window space and minimize expenses.

Observation masking is a prompt engineering technique that replaces verbose tool outputs with compact references, preserving essential information while significantly reducing the overall token count.

You can optimize the KV-cache to improve agent performance by reordering context to maximize the reuse of cached computations, which directly reduces latency and operational costs.

Yes, you can manage complex tasks by using context partitioning, which divides the workload among sub-agents with isolated contexts to maintain focus and improve processing efficiency.

The best way to extend effective context capacity for large documents is through strategic summarization and selective masking of tool outputs, allowing the AI to process more information without hitting token limits.

You should avoid context compaction when processing tasks that require verbatim recall of previous conversation turns, because summarizing older information discards the exact details needed for strict accuracy.