Because a context window works like a fixed-size queue, retrieved documents, tool outputs, and conversation history all compete for the same space, and models get worse at prioritizing earlier instructions as clutter builds up. Research across many frontier models has found this degradation starts well before the stated context limit is reached, which is why long agent sessions benefit from periodically summarizing or resetting context.