LLM performance degrades as input context grows — non-uniformly and even on simple tasks. The same question answered correctly at 1k tokens of context gets missed at 100k, and the degradation is not a smooth tail: distractors, position, and semantic similarity of the surrounding text all modulate it. "The context window is supported" ≠ "the context window is used well."
Implication for agent harnesses: a long-running main agent's judgment quality is a resource that depletes as its context fills — so context hygiene (eviction, isolation, summarization) is not bookkeeping, it is capability preservation. This is the load-bearing fact behind certificate-is-the-subagent-boundary: what crosses back from a subagent must be small, both to be checkable and to keep the parent's context from rotting.
Source: Chroma Research technical report 'Context Rot' (2025) — needle-in-haystack-style evals showing non-uniform degradation with input length; referenced from certificate-is-the-subagent-boundary during the 2026-07 wiki promotion