I Cut an AI Agent's Token Use by 94%
SMRTR summary
A developer slashed AI token usage by 94% and cut latency by 87% by converting a natural-language agent workflow into mostly regular Python code. The original workflow used an AI model to plan and execute every step of drafting LinkedIn posts, but most steps were repetitive and didn't need AI reasoning at all. By studying historical traces, the developer identified only two steps requiring AI, candidate selection and writing, replacing everything else with deterministic code.
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