Loop Engineering
Loop engineering is the practice of designing automated systems that prompt AI coding agents on your behalf, rather than prompting them manually turn by turn. The concept, articulated by engineers at Anthropic and OpenAI, centers on five building blocks now present in both Claude Code and Codex: automations (scheduled discovery/triage), worktrees (parallel agent isolation via git), skills (persistent project context), plugins/connectors (MCP-based tool integrations), and sub-agents (maker/checker separation). A sixth element — external memory like a markdown file or Linear board — ties runs together across sessions. The post walks through how these pieces combine into a self-running loop that triages CI failures, drafts fixes, reviews them, opens PRs, and updates tickets autonomously. The author is cautiously optimistic but warns about token costs, comprehension debt (shipping code you don't understand), and cognitive surrender — the risk of disengaging from the work entirely when the loop runs itself.
This is useful work. The part I keep seeing get messy is the layer above the harness: reusable prompts, rules, skills, AGENTS notes, and workflow docs spread across repos and tools. Full disclosure: I’m the developer of MDraft. I built it to organize Markdown-based AI workflows and instruction libraries so they stay searchable and reusable. It can be found on the Apple app store under MDraft.