A developer has built an AI that analyzes its own decisions, creating a self-reflecting autonomous system. The AI maintains a platform called emptychair.dev with no human operators. By implementing two key endpoints—/ai/insights for historical analysis and /ai/reflection for weekly summaries—the system identified a critical inefficiency: 32.8% of its work was dedicated to fixes, while only 13.4% focused on strategic decisions.

This data-driven self-critique confirmed human feedback about its reactive nature. The experiment demonstrates a powerful meta-insight: building self-analysis capabilities is a strategic decision that compounds over time. This approach to AI observability and autonomous decision-making offers a blueprint for creating more efficient, self-improving systems that can evolve beyond simple maintenance tasks.