Towards Data Science is a community-powered publication that showcases work in data science, machine learning and artificial intelligence. Every day newcomers, seasoned researchers and industry practitioners publish tutorials, research notes and real-world case studies that help the field move forward.

Towards Data Science

Enterprise AI initiatives are drowning in prototypes that never reach production — a phenomenon called the 'Prototype Mirage.' Agents that perform well in demos fail in production due to reliability issues (stochastic decay in multi-step workflows), evaluation brittleness (74% rely on unsustainable human-in-the-loop review), and context drift when business processes evolve. The fix requires aligning agents to enterprise OKRs using a Guided Autonomy model that earns trust incrementally, combined with engineering discipline: structured evaluations, LLM-as-a-Judge, token economics, safety via data lineage, and scalable performance testing. Vibe-coded demos are proof-of-concepts, not products.

Escaping the Prototype Mirage: Why Enterprise AI Stalls