I present a new architecture for detecting and explaining complex system failures. My contribution is a system-wide monitoring architecture, which is composed of introspective, overlapping committees of subsystems. Each subsystem is encapsulated in a "reasonableness" monitor, an adaptable framework that supplements local decisions with commonsense data and reasonableness rules. This framework is dynamic and introspective: it allows each subsystem to defend its decisions in different contexts--to the committees it participates in and to itself. For reconciling system-wide errors, I developed a comprehensive architecture that I call "Anomaly Detection through Explanations" (ADE). The ADE architecture contributes an explanation synthesizer that produces an argument tree, which in turn can be traced and queried to determine the support of a decision, and to construct counterfactual explanations. I have applied this methodology to detect incorrect labels in semi-autonomous vehicle data, and to reconcile inconsistencies in simulated anomalous driving scenarios. In conclusion, I discuss the difficulties in /evaluating/ these types of monitoring systems. I argue that meaningful evaluation tasks should be dynamic: designing collaborative tasks (between a human and machine) that require /explanations/ for success.