context window saturation

Article explicitly identifies single-agent context saturation as the constraint that drove architectural change. This is a direct case study of the saturation problem.

Article explicitly diagnoses context bloat and 'dumb zone' as performance problem when context window fills—this is the mechanism of context saturation

Article demonstrates that LLM performance degrades with large context—specific failure modes (false confidence, ignoring test failures) are empirical evidence of saturation effects

Article explicitly describes agent losing context as context window fills with intermediate outputs—a direct instantiation of this failure mode.

Skill selection accuracy plummeting at 50-100 skill threshold is functionally equivalent to context window saturation—the model has too many options to reason about clearly within available context.