Insight for: improvements to novelty

This issue directly questions the core value proposition of 'autoresearch': how adding agents *guarantees* novel architectures. It highlights a fundamental developer concern regarding the actual efficacy and innovation output of multi-agent systems. The pain point is the lack of clear, demonstrable mechanisms linking agent deployment to guaranteed novel outcomes, rather than mere optimization or iteration. Market implications include the need for AI agent platforms to articulate a stronger, evidence-based narrative around their capacity for true innovation and discovery, beyond efficiency gains. This suggests a demand for more sophisticated agent design that explicitly targets and measures architectural novelty.