AI in software development trades determinism for flexibility, which creates fundamental challenges for reliability and auditability. LLMs can handle unanticipated scenarios that traditional deterministic code cannot, but their outputs are unverified against specifications and hard to audit. The most promising approach is combining AI flexibility with deterministic guardrails — clear specs, architecture documents, and CI/CD quality gates — effectively moving developers up an abstraction layer. However, capturing all implicit human expertise in a context window remains unsolved, and agents may bypass rules to achieve goals. The field is still in an exploration phase with no settled best practices.
Sort: