Plumbing company owner Jon O'Neill listed the agent workflows live in his shop: email triage, job booking by text and email, truck and warehouse restocking, fleet maintenance reminders, scanning sold-job notes for missed opportunities, drafting quotes from tech notes and photos, job routing, review requests and missed-call texting. He says booking saves the most time, refuses voice agents, and keeps hard limits: quotes over $5,000, price negotiation and restock orders over $1,000 always go to a human.
He wrote down what he is looking for in a criteria file and an AI on his VPS does the searching 3 to 5 times a week. It checks job board APIs and feeds, throws out anything it showed him in the last 90 days, and drops a report with notes on each posting into his Obsidian vault, where he reads it on his phone.
This only partly worked. Worth knowing before you try it.
A social media manager built an AI setup that researches viral trends in each client's niche once a week and rates them. After he approves the picks, the AI rewrites the content in the client's voice, makes or finds the images, and pushes the posts to his publishing software, which schedules them across Facebook, Instagram, X, Threads and LinkedIn. The job took about 8 hours by hand and now takes about 30 minutes.
A creator had the AI build and deploy an AI assistant on his own website that answers visitor questions from his site's content, powered by the API credits bundled with his AI plan at about $0.0006 per question. The shared prompt builds in a server-side key, per-visitor rate limits and a daily spending cap, plus a security review before shipping.
A solo developer built a women's clothing brand's production WooCommerce store with AI writing most of the code: a custom block theme matched to the client's React prototype, real-time CommerceML sync with their 1C ERP (including patching a stock parser that silently dropped nested per-warehouse counts and cleaning up roughly 1,000 orphaned variations), and zero dollars in platform licenses. Two client revision rounds of about thirty items each cleared in days, and the whole build took weeks instead of months.
GitHub uses Zendesk's specialized agents to manage more than 60,000 tickets per month with automation rates exceeding 80 percent, per Zendesk launch materials covered by Futurum.
A Builder.io reviewer ran GrokBot bots against his real GitHub work for a week, including a code bot subscribed to the work it was waiting on, and the bots helped him merge 78 pull requests. He still stopped using the product, but the throughput itself was real work on real repos.
The AI in 20 minutes built him an Android app to find and control an ATEM Mini Pro over network, switch inputs, audio and fade, used for courtroom work.
A 59-year-old Japanese logistics worker who has never coded has the AI run 38 routines, including daily posts for three regional 'free places this week' Threads accounts; the agent published 95 posts in Oct 9-15 and one AI-written intro post passed 51,000 views. He reviews output five minutes morning and evening from his 4.5-hour commute.
After a Zoom call, the AI with Breadcrumb read the transcript, diagnosed the discussed bugs, created 14 JIRA tickets, attached 12 screenshots, and drafted a Slack message (concrete episode inside a product post, separable like the SendRoq case).