I design AI systems
that don't break.

Most teams blame the model. The actual failure mode is almost always architectural: no state contract between steps so the agent loses context mid-workflow, no validation gate so hallucinated output flows downstream unchecked, no checkpointing so a crash at step 7 means starting over from step 1. The model isn't the weak link. The plumbing around it is. The STATE framework exists to diagnose exactly this.

LLMOps handles the deployment and evaluation layer — model hosting, versioning, prompt tracking, evals. AI Reliability Engineering is the architectural layer below that: the state contracts, full execution traceability, fault tolerance that resumes from where it crashed, and explicit validation gates that catch bad LLM output before it becomes a real-world action. You can run excellent LLMOps tooling on top of a system that still fails silently. That's the gap.

Observability isn't a dashboard. It's the ability to reconstruct, in full, what your agent did on any specific past execution — which prompt ran, which model version, what the output was at each step, where in the workflow it was when it failed or succeeded. Most teams log inputs and outputs. That tells you what went in and what came out. It doesn't tell you why the agent made the decision it did at step 4. If you can't answer that question for a run from last Tuesday, you don't have observability yet.

When a decision affecting an individual is made exclusively through automated processing, organizations must notify that person. On request, they must disclose the personal data used, the principal factors that influenced the decision, and the individual's right to human review. In practice this means your system needs structured decision records — not log files, but explicit records tying each outcome to the data and model version that produced it. Penalties reach C$10M or 2% of global revenue. This isn't a compliance checkbox. It's an architecture requirement your system either satisfies or it doesn't.

Only the Auditable pillar connects directly to regulatory requirements like Law 25 or OSFI. The other four — Structured, Traceable, Tolerant, Explicit — address engineering problems that affect every production AI system. Non-determinism, context rot, full restarts after partial failures, hallucinations flowing through unvalidated: these aren't compliance problems. They're production reliability problems. Regulation is one reason to build stateful, observable systems. The more common reason is that systems without these properties are expensive to debug and dangerous to trust.