A quality model is an explicit, shared description of what “good” means for something you maintain. In QUALITY.md, that means the areas of a project, the quality factors that matter for each, and concrete requirements with checkable assessments — plus a rating scale for judging results and the context for why any of it matters. Instead of quality living in people’s heads and scattered review comments, the model makes it visible enough for teams and agents to evaluate, align on, and improve.The idea has a long lineage. Manufacturing quality management (Shewhart’s statistical quality control, Juran’s “fitness for use”) carried into software through McCall’s and Boehm’s models in the 1970s, HP’s FURPS, Dromey’s model, and the ISO 9126 and ISO 25010 standards. Across five decades the same pattern recurs: decompose quality into the characteristics that matter for a particular thing and its stakeholders, refine them into sub-characteristics, and tie those to assessable criteria.In practice, these models rarely spread beyond large organizations with formal quality programs: building and tailoring a detailed model was costly, and the metric-based rating methods meant to make quality measurable proved too reductive — or too complex — for everyday use. AI changes that. Agents make a tailored quality model cheap to establish and maintain, and inferential judging of quality requirements makes holistic evaluation accessible — good enough for many use cases where formal measurement never was.