context-optimization

To optimize context limits, you can apply compaction to compress information, use observation masking on verbose tool outputs, and partition tasks across sub-agents. This preserves essential signals while reducing overall token usage.

The best way to manage token costs is by applying context partitioning across sub-agents and reusing cached results. Observation masking also lowers latency and cost by hiding verbose outputs behind references.

KV-cache optimization improves repeatable requests by enabling stable caching for repeated context segments. This prevents redundant processing, lowers latency, and effectively manages token budgets during multi-step agent execution.

Use context compaction when nearing window limits to compress and preserve essential signals. Use context partitioning to scale large documents by splitting tasks across sub-agents, managing latency and budget more effectively.

Yes, you can mask verbose tool observations by replacing them with references. This technique lowers token usage and latency while keeping the core information accessible, preventing context window degradation.