I'm Archie Cortés, founder of AutoPilotPR. My job is a new one: I decide what an AI agent should do inside a business, deploy it on that business's own systems and data, and then run it in production, which means keeping it correct, accountable, and cheap month after month. Getting an agent to work once is easy. Keeping ten of them running real departments is the actual work.
Today I run ten agents in production across three companies. A content agent writes, lints, and publishes blog posts and social for an apparel brand's Shopify store, with a market-intelligence agent feeding it. A set of operations agents runs a villa rental business: daily anomaly checks, weekly health and reach reports, social, and guest-inquiry triage. A meeting agent records, transcribes, and turns calls into action items. And the agents that run this site publish its content, qualify its leads, and track its AI citations every week.
The stack is OpenClaw as the agent gateway, Anthropic Claude and OpenAI models routed by task, Next.js on Vercel, Supabase, Shopify, and Discord as the control surface where every agent reports and every approval happens. The parts that matter most are the ones you don't see on a demo: human approval gates on anything that touches money, clients, or compliance; a full activity log per agent; a linter and publish guard that block anything off-brand or legally risky before it ships; a daily cost sentinel with per-agent attribution and thresholds; prompt caching, per-run turn budgets, and loop detection so an agent can never quietly run up a bill.
Before this I spent 10+ years in marketing and analytics in Puerto Rico, where I was born and raised. I've worked with Act 60 founders and local businesses alike, and I operate the companies I automate, so every system here was built for a real P&L, not a slide.
Every client gets the same infrastructure we run ourselves, with a human approving anything that touches money, clients, or compliance.