"LLMs inside business workflows: the pattern that actually works"
Most broken LLM automations share one design decision: the model is steering the workflow. The pattern that survives production is the opposite — deterministic orchestration with the model filling specific, bounded slots.
Most of the broken LLM automations I get asked to look at share the same design decision: someone put the model in charge. The workflow asks the model what to do next, the model decides which tool to call, and the whole system's behaviour depends on the mood of a probabilistic component. It works impressively in the demo. Then a slightly odd input arrives, the model takes a route nobody anticipated, and you are debugging a system whose control flow you cannot reproduce.
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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