context-optimization

Reduce token costs for long-running AI agents by applying context compaction to summarize older conversation turns and applying observation masking to replace verbose tool outputs with concise references once their immediate utility has passed.

Context compaction extends effective context window capacity by summarizing older conversation turns or tool outputs to free up space, addressing fixed context limitations and reducing computational overhead in production AI systems.

Optimize KV-cache usage to improve AI inference speed and reduce cost by intelligently reusing cached computations, which minimizes redundant processing during long conversations or large document analysis tasks.

The best way to manage large document analysis within token limits is context partitioning, which divides complex tasks among sub-agents with isolated contexts for better management and improved performance.

Use observation masking for tool outputs in prompt engineering when immediate utility has passed, replacing verbose outputs with concise references to maintain performance and reduce token costs in long-running agent workflows.