"Shadow mode: running your AI feature in production without letting it act"
Offline evals tell you how the model performs on last month's cases. Shadow mode tells you how it performs on tomorrow's — live traffic, full pipeline, zero blast radius — and measures it against the humans currently doing the job.
There is a stage between "the evals pass" and "the feature is live" that most teams skip, and it happens to be the stage that catches what the other two cannot. Shadow mode: the AI feature runs in production, on live traffic, through the full pipeline — except the final action is suppressed. Web engineering has known the pattern for years as the dark launch; ML teams rediscovered it as shadow deployment. Outputs are logged, compared, and go nowhere. The humans keep doing the job exactly as before, and for a few weeks the system is silently judged against them.
Have an AI feature stuck between demo and production?
The gap — reliability, evals, cost control, the plumbing that keeps it running unattended — is exactly the work I do. If that sounds familiar, a short conversation is usually enough to point you the right way.
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