Coding agent is under-specified (hsaghir.github.io)

Recent insights into coding agents reveal that while these AI tools have shown remarkable advancements in generating code, they suffer from significant under-specification issues that affect code quality over time. A year of building production systems with frontier models has indicated that although these agents can produce sophisticated code across multiple languages and frameworks, they often result in accumulating technical debt. This is primarily due to their tendency to fill in gaps in understanding from vague prompts, leading to hidden implementation choices that degrade codebases through a series of isolated yet interconnected changes. The findings emphasize that the problem lies not in the AI's capabilities but in the incompleteness of the specifications provided by human users. When given detailed instructions, coding agents produce high-quality code, but loose prompts can lead to poor architectural decisions and silent logical errors that only surface at scale. This suggests that improving coding agent performance hinges on creating more precise, comprehensive specifications that account for behavioral, architectural, and lifecycle considerations. As coding agents become more capable, the pressing challenge for developers will be crafting clear specifications that can guide AI systems effectively, ensuring that the trajectory of software quality does not degrade over time.