Loop Engineering

The Problem

When an AI agent runs multi-turn autonomous work, the failure mode is often not a bad answer but missing infrastructure: no trigger, isolation, verification, durable memory, or circuit breaker. Manually patching prompts each round leaks in predictable ways: parallel edits conflict, the model scores its own work, constraints drift after many turns, and progress lives only in context.

How It Works

Loop Engineering treats prompt loops as production-line design. It organizes a loop around six components:

• Trigger: use /loop for periodic monitoring and /goal until a verifiable condition is true.
• Isolation: give each parallel task its own git worktree to avoid file-level conflicts.
• Knowledge: encode build commands, red lines, and past pitfalls in SKILL.md so every round reads the same project facts.
• Action: use connectors / MCP to read issues, databases, Slack, PRs, and other real workflows.
• Verification: separate maker and checker, with a stronger reviewer agent checking spec and skill.
• Memory: persist state to files, boards, or structured progress records so the next round can continue.

Boundaries

Use it for tasks with automatically verifiable outcomes: tests pass, lint clean, builds succeed, issues triaged, PRs reviewed. Avoid open-ended research or fuzzy goals. Validate one core step first, then set maximum rounds, maximum runtime, and a fallback path for failures.