AI coding tools are getting better at helping individual developers produce code (within a local context), but enterprise software delivery still breaks down across the broader lifecycle. Most requirements become tickets, tickets become architecture decisions, and those decisions become code changes. The code changes made by AI affect testing, documentation, releases, downstream systems, and audit requirements. In regulated industries like healthcare, lifecycle continuity matters more than raw code generation. So my question is: Are we over‑indexing on AI that writes code and under‑building AI systems that preserve engineering context across the lifecycle? What would the ideal enterprise AI development tool or workflow look like, and what particular features should it have? submitted by /u/Suspicious-Bug-626
Originally posted by u/Suspicious-Bug-626 on r/ArtificialInteligence