Artificial intelligence context compaction replaces or reorganizes part of a growing context with a smaller representation. The process may summarize older messages, retain selected constraints and move detailed outputs into referenced artifacts.
Compaction can reduce token cost and latency, but it may also remove important evidence or instructions. Systems should preserve original history, validate the compacted context and apply the intervention only within approved policy boundaries.
