context-optimization

You can reduce token usage for AI agents by applying context compaction to summarize lengthy conversations and observation masking to replace verbose tool outputs with concise references, preserving signal while cutting costs.

KV-cache optimization designs prompts to maximize cache utilization, reducing redundant recomputation for repeated prompt segments. This lowers prompt engineering costs by minimizing the computational overhead of processing long context windows.

Manage large tasks exceeding finite context windows by using context partitioning, which divides work among sub-agents with isolated contexts. This extends effective context capacity and prevents token limit errors.

Yes, context compaction works with extended tool outputs by summarizing verbose observations. It condenses lengthy agent trajectories to fit within token limits, allowing the AI to maintain operational context without exceeding constraints.

Use context partitioning for large, parallelizable tasks requiring multiple sub-agents with isolated contexts, and use context compaction when you need to condense a single lengthy conversation or document to fit existing token limits.