This paper identifies a number of metrics that could be important for assessing cognitive architectures across a range of applications domains. The metrics are organized according to a taxonomy of requirements for intelligent systems. These metrics together reflect our attempt to capture and measure many necessary components of general intelligent behavior, rather than solely performance metrics, which are often the primary means of evaluating intelligent systems in AI. We introduce two metrics novel to cognitive-architecture research, incrementality and adaptivity, which may prove to be useful for capturing and expressing the cumulative value of cognitive-architecture-based solutions across multiple tasks within a domain and across multiple application domains. Our approach is far from complete, in that several requirements include only notional metrics. However, this approach provides at least an empirical foundation for comparing work within and across the development cognitive architectures that can provide more objective measures of a cognitive architecture’s capabilities and utility as a platform for general intelligence.