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Mariano Rodrigo

Mariano Rodrigo

AI Solutions Engineer building production systems with artificial intelligence, automation, and full-stack architecture. This is my public engineering lab: architecture decisions, implementation reports, experiments, and lessons from real systems.

From prototype to production: an AI architecture checklist

· ai-systems, architecture, operations

A demo proves a model can produce the right output once. Production requires that the system produces an acceptable output every time, observably, and recovers when it does not. That difference is almost entirely engineering.

The checklist

  • Failure paths first. What happens when the model times out, returns malformed output, or hallucinates a field? Define the fallback before the happy path.
  • Determinism where it matters. Pin model versions, log prompts and parameters, and make runs reproducible. “It worked yesterday” is not a debugging strategy.
  • Evaluation as code. A small, versioned eval set that runs in CI catches regressions a vibe-check never will.
  • Observability. Trace every request: inputs, retrieved context, tokens, latency, cost, and the decision taken. You cannot operate what you cannot see.
  • Human-in-the-loop. Decide which outputs need approval and build the queue for it — not as an afterthought, but as part of the data flow.

The point

None of this is about a better model. It is about treating the model as one component inside a system that has to be understood, tested, secured, and operated. That system is the actual deliverable.