Crafting, adhering to, and maintaining standards is an ongoing challenge. This paper uses a framework based on common models to explore the standard problem: the impossibility of creating, implementing or maintain definitive common models in an open system. The problem arises from uncertainty driven by variations in operating context, standard quality, differences in implementation, and drift over time. Fitting work by conformance services repairs these gaps between a standard and what is required for interoperation, using several strategies: (a) Universal conformance (all agents access the same standard); (b) Mediated conformance (an interoperability layer supports heterogeneous agents) and (c) Localized conformance, (autonomous adaptive agents manage their own needs). Conformance methods include incremental design, modular design, adaptors, and creating interactive and adaptive agents. Machine learning should have a major role in adaptive fitting. Choosing a conformance service depends on the stability and homogeneity of shared tasks, and whether common models are shared ahead of time or are adjusted at task time. This analysis thus decouples interoperability and standardization. While standards facilitate interoperability, interoperability is achievable without standardization.