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Computing and software intensive systems are now an inextricable part of modern work, life and entertainment fabric. This consequently has increased our reliance on their dependable operation. While much is known regarding software engineering practices of dependable software systems; the extreme scale, complexity and dynamics of modern software has pushed conventional software engineering tools and techniques to their acceptable limits. Consequently, over the last decade, this has accelerated research into non-conventional methods, many of which are inspired by social and/or biological systems model. Exemplar of which are the DARPA-funded Se1f-Regenerative-Systems (SRS) programme, and Autonomic Computing, where a closed-loop feedback control model is essential to delivering the advocated cognitive immunity and self-management capabilities. While much research work has been conducted on vanous aspects of SRS and autonomy, they are typically based on the assumptions that the structural model (organisation) of managed elements is static and exhaustive monitoring and feedback is computationally scalable. In addition, existing federated approaches to distributed computation and control, such as Multi-Agent-Systems fail to satisfactorily address how global control may be enacted upon the whole system and how an individual component may take on specified monitoring duties - although methods of interaction between federated individuals is well understood. Equally, organic-inspired computing looks to deal with event scale and complexity largely from a mining perspective, with observation concerns deferred to a suitably selective abstraction known as the "observation model". However, computing and mathematical science research, along with other fields has developed problem-specific approaches to help manage complexity; abstraction-based approaches can simplify structural organisation allowing the underlying meaning to be better understood. Statistical and graph-based approaches can both provide identifying features along with selectively reducing the size of a modelled structure by selecting specific areas that conform to certain topological criteria. This research studies the engineering concerns relating to observation of large-scale networks of autonomic systems. It examines methods that can be used to manage scale and generalises and formalises them within a software engineering approach; guiding the development of an automated adaptive observation subsystem - the Global Observer Model. This approach uses a model-based representation of the observed system, represented by appropriately attached modelled elements; adapters between the underlying system and the observation subsystem. The concepts of Signature and Technique definitions describe large-scale or complex system characteristics and target selection techniques respectively. Collections of these objects are then utilised throughout the framework along with decision and deployment logic (collectively referred to as the Observer Behaviour Definition - an ECA-like observational control) to provide a runtime-adaptable observation overlay. The evaluation of this research is provided by demonstrations of the observation framework; firstly in experimental form for assessment of the Signature and Technique approach, and then by application to the Email Exploration Tool (EET), a forensic investigation utility.