Software Diagnostics and Observability Institute

Software systems no longer fail in isolation. They evolve, adapt, learn, and interact with environments that are only partially observable. As systems have grown more complex, the act of diagnosing them has remained largely artisanal, dependent on tools, experience, and intuition, but resistant to formalisation. My work began many years ago from a simple conviction: diagnostics deserves to be a discipline.

Pattern-Oriented Diagnostics is not a catalog of tools, nor a collection of debugging tricks. It is a way of reasoning about execution. It treats traces, logs, memory dumps, metrics, and observability signals as narratives produced by running systems, narratives that must be interpreted, compared, and sometimes distrusted. The diagnostician is therefore not merely an operator of tools, but an interpreter of stories told under uncertainty.

At the foundation of this approach lies a distinction between execution and its representations. Processes, threads, distributed services, and learning models do not speak directly. They leave artefacts. These artefacts (logs, traces, dumps, token streams) are incomplete and often misleading accounts of reality.

Patterns provide the bridge between artefacts and understanding. A diagnostic pattern captures an invariant structure and behaviour of execution that recurs across platforms, languages, and decades of technological development. Patterns do not belong to any single toolchain; they belong to the structure and behaviour of systems themselves. Pattern-Oriented Diagnostics elevates these recurring aspects of execution from heuristics to first-class analytical objects.

Between artefacts and diagnostic patterns lie analysis patterns. An analysis pattern captures a reusable mode of interpretation: a structured way of transforming execution narratives into problem-relevant representations. Analysis patterns describe how evidence is examined, compared, restricted, normalised, correlated, or abstracted before any diagnostic conclusion is drawn. They are neither raw data nor diagnoses, but disciplined methods of analysis that can be applied repeatedly across systems, platforms, and domains.

Memory analysis occupies a central position in this framework, and its relevance has only grown with the rise of AI systems. Classical memory dump analysis revealed that memory is not merely storage, but a structured state shaped by execution history. Corruption, leakage, aliasing, fragmentation, and latent structure manifest as patterns long before failure becomes visible at higher levels. The same perspective applies to AI systems: context windows, attention states, embeddings, and learned parameters form memory spaces with structure, evolution, and failure modes of their own. Pattern-Oriented Diagnostics treats AI memory not as an opaque substrate, but as an analysable state, subject to the same structural reasoning, invariants, and pattern-based interpretation as process memory.

Pattern-Oriented Observability extends this perspective upstream. Rather than treating observability as the accumulation of signals, dashboards, and metrics, it treats observability as the intentional construction of narratives that preserve diagnostically meaningful structure. Observability, in this sense, is not about seeing everything, but about seeing the right invariants. Instrumentation choices shape which patterns can subsequently be recognised; poorly structured observability introduces noise, whereas pattern-aware observability enables explanation. Diagnostics begins long before failure. It begins at the moment systems are made observable.

Diagnostics can also be formal. By framing analysis as mappings between artefacts and problem spaces, and by recognising transformations between analyses, we can reason not only about failures but also about the space of possible explanations. Mathematical ideas are introduced here not as abstractions for their own sake, but as languages for expressing change, invariance, and equivalence in execution.

The scope of diagnostics extends beyond classical software. Machine learning systems and large language models are often treated as opaque, probabilistic entities, exempt from traditional engineering scrutiny. This is a mistake. These systems execute. They have memory, state, drift, and failure modes. Their outputs form narratives just as logs and traces do. Pattern-Oriented Diagnostics applies the same analytical discipline to AI systems, refusing to treat novelty as an excuse for obscurity.

This perspective aligns naturally with AI engineering as an emerging discipline. AI engineering is not merely model training or optimisation; it is the systematic design, deployment, operation, and evolution of AI systems in production. As such, it inherits the same diagnostic challenges as any complex engineered system: partial observability, emergent behaviour, stateful execution, and failure under scale. Pattern-Oriented Diagnostics provides AI engineering with a language for reasoning about model behaviour, context, memory, and drift: not as isolated metrics, but as structured execution narratives that can be analysed, compared, and understood over time.

Equally important is the recognition that diagnostics is irreducibly human. Understanding does not emerge automatically from data. It emerges through judgment, through the weighing of evidence, the comparison of narratives, and the recognition of structure. Automation assists diagnostics, but responsibility remains with the engineer. In this sense, diagnostics is not merely a technical practice. It is epistemology in action. Interpretation, intent, and meaning are central to engineering practice. This reframing connects software diagnostics with literature, philosophy, and history without sacrificing technical rigour.

2025 didn’t attempt to close the field. It opened it. Pattern-Oriented Diagnostics is offered as a vocabulary, a set of principles, and a framework for thinking that can be extended and refined. As systems continue to grow in scale and autonomy, the need for disciplined understanding will only intensify.

Observation is a preparatory activity for gathering evidence. Observability and diagnosability shape execution narratives to expose meaningful aspects of behaviour. Diagnostics applies patterns to these narratives to make complex systems intelligible.