Computing has evolved from isolated machines, providing calculative support of applications, toward communication networks that provide functional support to groups of people and embedded systems. Perhaps, one of the most compelling feature and benefit of computers is their overwhelming computing efficiency. Today, we conceive distributed computational systems of an ever-increasing sophistication, which we then apply in various settings – critical support functions of our society just to name one important application area. The spread and impact of computing, in terms of so-called information society technologies, has obviously gained a very high momentum over the years and today it delivers a technology that our societies have come to depend on. To this end, concerns related to our acceptance of qualities of computing, e.g., dependability, are increasingly emphasized by users as well as vendors. An indication of this increased focus on dependability is found in contemporary efforts of mitigating the effects from systemic failures in critical infrastructures, e.g., energy distribution, resource logistics, and financial transactions. As such, the dependable function of these infrastructures is governed by means of more or less autonomic computing systems that interact with cognitive human agents. However, due to intricate system dependencies as well as being situated in our physical environment, even the slightest – unanticipated – perturbation in one of these embedded systems can result in degradations or catastrophic failures of our society. We argue that this contemporary problem of computing mainly is due to our own difficulties in modeling and engineering the involved system complexities in an understandable manner. Consequently, we have to provide support for dependable computing systems by means of new methodologies of systems engineering. From a historical perspective, computing has evolved, from being supportive of quite well defined and understood tasks of algorithmic computations, into a disruptive technology that enables and forces change upon organizations as well as our society at large. In effect, a major challenge of contemporary computing is to understand, predict, and harness the involved systems’ increasing complexity in terms of constituents, dependencies, and interactions – turning them into dependable systems. In this thesis, we therefore introduce a model of open computational systems, as the means to convey these systems’ factual behavior in realistic situations, but also in order to facilitate our own understanding of how to monitor and control their complex interdependencies. Moreover, since the critical variables that govern these complex systems’ qualitative behavior can be of a very elusive nature, we also introduce a method of online engineering, whereby cognitive agents – human and software – can instrument these open computational systems according to their own subjective and temporal understanding of some complex situation at hand.