From Prototype to Reliable Workflow

Start with the actual workflow

The first question is not what the model can do. It is what the user is trying to accomplish, what decisions they own, what data they need, and where the workflow is currently slow, repetitive, or error-prone.

Define boundaries early

• What should the model never do?
• Which actions are read-only, and which mutate state?
• Which data can be retrieved?
• Which outputs need approval before they affect customers or systems?

Build eval cases early

The eval set should include normal cases, ambiguous cases, known failure modes, missing-data cases, and high-risk cases. It should test the workflow, not only the prompt.

Add human approval points

Human review is not a weakness. It is often the design that makes a workflow deployable. Approval points should be explicit where the model could affect customers, accounts, billing, bug state, test exclusions, or operational decisions.

Log failures and edge cases

Real usage will reveal cases the prototype did not anticipate. Logging, reviewer feedback, and failure-mode tracking turn that usage into iteration instead of silent drift.

Iterate from real usage

A reliable AI workflow is an engineered system: clear boundaries, useful retrieval, structured outputs, eval coverage, guardrails, review, logging, and a feedback loop.