Three architectural patterns that recur across every large-scale infrastructure programme I have led. Abstracted from specific implementations, these diagrams represent the structural thinking behind lifecycle automation, Digital Twins, and observability at scale.
The engine behind Zero Touch Networks and Digital Twins. Infrastructure converges toward its intended state through a continuous cycle: declare intent, strategize the path, execute, observe the outcome, and assess the drift. Each phase is a distinct software system; the architecture is in how they compose.
Click any phase to explore its role. The forward path drives convergence; the feedback loop from Assess back to Intent ensures drift is detected and corrected. Three foundational sub-systems — the Domain Model, Simulation Engine, and Workflow Engine — underpin the entire cycle.
This pattern is explored in depth in The Automation Ladder and powers the Fragility Index feedback loop.
A Digital Twin is not a dashboard — it is a high-fidelity, computable model of physical infrastructure that supports simulation, prediction, and autonomous decision-making. The architecture is a layered stack, each layer building on the one below.
Layer 3 implements the Fragility Index methodology. Layer 5 implements the lifecycle automation pattern above. The Domain Model (L2) is the foundational concept discussed in the Automation Ladder.
You cannot control what you cannot observe. This pipeline transforms raw infrastructure telemetry into the signals that feed the lifecycle loop and the Digital Twin. Built for Google-scale volumes, the pattern generalises to any infrastructure at any scale.
Click any stage to explore. Raw telemetry from heterogeneous infrastructure sources flows through collection, enrichment with topology context, correlation for root-cause analysis, health assessment, and finally actionable signal generation. The output feeds the Domain Model, the Lifecycle Engine, and human operators.
This pipeline populates the Observe phase in the lifecycle loop and feeds the Fragility Index with the empirical MTBF/MTTR data needed for probabilistic simulation.