Setting Up Local Routines with AI Agents: What Worked and What Didn't
Over the past few months I have moved a handful of recurring back-office jobs from "a person does this every morning" to "an AI agent does this every morning, and a person checks the summary". Not chatbots, not a one-off prompt: routines that run on a schedule, touch real databases, and have to be right. This post is about the setup work behind that — the boring parts that decide whether an agent routine is trustworthy or just impressive in a demo. The running example is a small tool I built for a domain-name business: every day it pulls a few thousand domain roots that still lack keyword metadata, has an AI agent split each root into words ( worldcup2026update → world cup 2026 update ), validates the result, and writes the keywords back to SQL. Simple on paper. Most of the lessons below came from making it boring in practice. Start with the shape of the job, not the agent My first instinct was to hand the agent the whole task: "connect to the database, ...