Summary
AI Jason shares a month of hard-won lessons from running autonomous agent loops in production at Super Design, his AI-native company. Rather than abstract theory, the video walks through the exact architecture behind loops that wake up every 30 minutes to scan codebases, file PRs, triage customer support across languages, and manage CRM outreach — all without human initiation.
The core framework centers on a loop contract: a single markdown file that defines the goal, boundaries (what the agent can do autonomously vs. what requires escalation), and standard operating procedures. Below that lives a two-part state and log system — a compact durable state capturing current hypotheses and backlog, plus an append-only run log so agents don’t waste tokens rediscovering the same errors each cycle. Jason walks through a real example using the open-source React Doctor tool, showing how a daily loop scans the codebase for frontend health issues and autonomously ships fixes within defined risk boundaries.
The video also breaks down four trigger types — goal-based (continuous until complete), cron-based (Claude Code loops or Codex automations), event-based (webhook-driven demons for incident response or email triage), and combo/workflow triggers that check data sources programmatically before waking the agent to batch work efficiently. Jason notes that Claude Code and Codex support the first two natively but that event-based and combo triggers require a local daemon setup, and teases an internal tool they’re open-sourcing to manage this. A practical, production-tested reference for anyone building autonomous agent systems.
📺 Source: AI Jason · Published July 13, 2026
🏷️ Format: Tutorial Demo
