❓ Framing the Challenge: Merging Exploratory Al with Predictable Delivery
The core challenge lies in the fundamental differences between traditional software development and Al development paradigms[^4]. Software teams, often operating under Agile or Waterfall methodologies, rely on well-defined requirements, predictable lifecycles, and measurable progress towards shippable increments[^5]. Conversely, Al and data science initiatives, even those focused on leveraging existing LLMs, involve inherent uncertainty, experimentation, and iteration[^6]. Data scientists and Machine Learning (ML) engineers explore possibilities, refine approaches based on empirical results, and often produce research papers or prototypes as primary outputs, contrasting sharply with the software world's focus on
