I built an autonomous AI agent playbook — here are the patterns that actually work

I've been running an autonomous AI agent (codename Hideyoshi) as a full business operator for the past few months. Not a chatbot. Not a copilot. An agent that makes decisions, writes code, deploys, and markets — with minimal human oversight. Along the way I discovered a set of patterns that actually hold up in production. These are battle-tested. 1. Constrained Autonomy > Full Autonomy Giving an agent unlimited freedom sounds cool until it burns your production database. The pattern that works: Approve-free zone : formatting, linting, test runs, commits within scope, research Human gate : releases, billing changes, security-impacting changes, bulk operations (5+ items) Define the boundary explicitly in a config file. The agent reads it on every session. No ambiguity. 2. Verify Before You Act The single most important rule. Agents love to hallucinate solutions. Never guess. Read the code. Check the data. Bug fixes require: symptom evidence, root cause (file + line), a fix that addresses