Failure Modes Are Features, Not Exceptions
Defensive architecture starts by mapping everything that can fail. Most teams do this reactively. We do it at specification stage.
Writing on AI architecture, structured intelligence, and the engineering of complex systems. No tutorials. No trend pieces.
There is a belief, particularly among early-stage teams, that architecture can be deferred. That you first build something that works, and then you make it scale. This is not a strategy. It is an assumption — and one that carries compounding technical debt with every sprint.
Defensive architecture starts by mapping everything that can fail. Most teams do this reactively. We do it at specification stage.
When a team optimises for inference speed without a latency budget, they are optimising in the wrong direction. A constraint, not a target.
Standard microservice principles hold well for stateless compute. They do not hold for stateful inference contexts. Here is what changes when AI enters the stack.
Before any Canonix project enters development, we produce one document. This is what it contains — and why it eliminates the most expensive mistakes.
Logs tell you what happened. Observability tells you why. The difference determines whether your team can respond to production incidents in minutes or hours.
The context window of an LLM is not a capability — it is a boundary condition. How you design around it determines scalability at every layer above the model.
The most expensive bugs are caught at the specification stage. These are the questions that find them — before the build clock starts.
One essay per month. No noise. No product announcements. Published when it is ready.