Design the workflow before choosing the model.
Start with the user decision, data boundary, latency target, failure mode, and human handoff. The model is one component of the system.
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Build AI applications that belong in production.
Practical architecture notes for teams turning an AI prototype into a secure, observable, cost-aware cloud service—not just a convincing demo.
Keep data, identity, and deployment boring.
Use managed identity, private data paths, clear environment separation, infrastructure as code, and one repeatable path from change to production.
Make quality, cost, and risk observable.
Trace requests end to end, log meaningful evaluations, measure task success, bound spend, and know when the system should decline to answer.
A practical build sequence
From an idea to a service your team can support.
Make the outcome testable.
Define the task, the acceptable answer, the unacceptable answer, and a small evaluation set before integrating a model.
Build a thin, observable vertical slice.
Put one real workflow through the actual identity, data, model, and deployment boundaries. Instrument it from the first request.
Add guardrails where the risk lives.
Use permissions, retrieval boundaries, structured outputs, rate limits, approvals, and fallbacks that fit the specific action being taken.
Operate it like a product.
Review quality and cost on a regular cadence. Promote versions deliberately, keep a rollback path, and improve from real use.