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Applied AI · 02

An AI feature needs an operating model, not only a model

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Why prompts and model choice are only one part of a reliable AI-enabled workflow.

KERNYRA1 min read

Core idea

Treat model output as a proposal inside a system of evidence, permissions, evaluation, and accountable review.
01

Define the consequence of being wrong

The same model behaviour can be acceptable for drafting an internal summary and unacceptable for approving a payment. Reliability begins with the decision around the output: who uses it, what evidence is visible, and what happens when confidence is low.

02

Build the control loop

A production workflow needs more than a successful demo. It needs traceable inputs, bounded tools, access controls, fallbacks, review points, and a way to compare changed behaviour over time.

  • Representative evaluation cases
  • Retrieval and source boundaries
  • Human escalation paths
  • Trace and feedback retention
  • Cost and latency budgets
03

Measure the job, not the spectacle

Model benchmarks rarely describe whether a workflow improved. Measure the operational job: corrections required, unresolved cases, review time, failure categories, and whether users can recognise uncertainty before acting on it.

A real system

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