context-optimization

Reduce context bloat by compacting older conversation turns, masking verbose tool outputs, and partitioning large tasks across isolated sub-contexts. This preserves high-signal information like key decisions and references while lowering token waste, latency, and cost.

Observation masking replaces verbose tool outputs with compact references while preserving retrievability. This technique minimizes token consumption in long workflows by ensuring that large retrieved documents or repeated tool results do not overwhelm the context window.

Improve KV-cache reuse by stabilizing prompt structure to reduce repeated computation. Cache-friendly prompting ensures that the prompt format remains consistent across interactions, allowing the system to efficiently reuse cached computations and lower overall processing latency.

Yes, you can split large tasks across isolated sub-contexts using context partitioning. This technique keeps each working set manageable, ensuring that long research or debugging sessions stay within tight context windows without losing important task state or findings.

The best way to compact a conversation is to summarize older turns, tool outputs, and retrieved documents into a token-efficient summary. This preserves the current task, open questions, and key decisions, allowing the agent to continue functioning under context limits.

Yes, context partitioning and observation masking are specifically designed for extended debugging sessions. By isolating sub-contexts and replacing repeated tool outputs with compact references, the agent maintains quality and retrievability while operating under strict token limits.