context-optimization

Optimize context windows by applying compaction, observation masking, KV-cache reuse, and context partitioning at runtime. This reduces the token footprint of long-context workflows, doubling or tripling effective capacity without requiring larger models.

Observation masking reduces token footprint by replacing verbose tool outputs with compact references. This minimizes context load in multi-agent orchestration, preserving processing capacity for essential information while maintaining workflow efficiency.

Manage context budgets by setting explicit limits with guardrails and trigger conditions. When context nears capacity, compaction strategies summarize contents to preserve signal, ensuring efficient token usage across constrained workflows.

Context partitioning works for multi-agent orchestration by splitting work across sub-agents to maintain isolated contexts. This prevents individual agents from exceeding context limits while keeping the overall workflow functional.

Use KV-cache optimization when processing requests with identical prefixes to reuse cached computations. This improves token efficiency in long-context workflows by eliminating redundant processing across repeated context segments.

Context optimization does not increase actual context window size but maximizes existing capacity. Limitations include potential information loss during compaction and the need for careful budget management to balance efficiency with information retention.