context-optimization

You can reduce token costs by applying context optimization techniques like content summarization, observation masking, and context partitioning to manage large context windows. These strategies distill lengthy conversations and isolate contexts for long-running agent systems.

KV-cache optimization involves designing prompts and managing context to maximize the reuse of cached computations. This reduces latency and processing costs by avoiding redundant calculations during long-running agent interactions.

You can mask verbose tool outputs by replacing them with concise references. This observation masking technique saves tokens without losing critical information, keeping the context window focused on the current issue.

Yes, context partitioning divides complex tasks into sub-agents with isolated contexts. This approach improves management for long-running agent systems by preventing context window bloat and maintaining processing efficiency.

Use context compaction to summarize and distill lengthy conversations or documents fitting within token limits. Choose context partitioning to divide complex tasks into sub-agents with isolated contexts for better management.

Context optimization requires careful application based on context composition and continuous performance monitoring. Misapplying techniques like summarization or observation masking might inadvertently drop critical information needed by the agent.