context-optimization

Reduce token usage by applying structured context optimization techniques like masking, compaction, and caching to maintain reasoning quality while shrinking context size in multi-agent workflows. This prevents context budgets from throttling performance.

Context partitioning is a technique to split tasks across sub-agents to manage context size. Use context partitioning when a single context window becomes too large, allowing you to optimize token distribution without losing critical state information.

Implement observation masking by replacing verbose tool outputs with compact references. This context optimization technique significantly reduces token consumption in agent workflows while preserving the ability to reference the original output if needed.

The best way to summarize long context segments while preserving critical state is through context compaction. Compaction reduces token usage by condensing historical context into a smaller, high-density format that retains essential information for reasoning.

KV-cache optimization improves token reuse by increasing the reuse of stable prefixes in prompts, which reduces recomputation. This context optimization technique directly lowers token costs and latency by avoiding the need to process identical prefix text repeatedly.

Yes, you can use context budget management to monitor token limits and trigger optimizations automatically. By tracking token usage, the system can proactively apply masking, compaction, or partitioning before context limits are exceeded.