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Loop Engineering

Loop Engineering is the practice of designing recurring systems for AI agents and coding agents. Instead of prompting an agent turn by turn, you build a loop that discovers work, delegates it to one or more agents, verifies the result against tests or other deterministic gates, persists state outside the model, decides what happens next, and runs again on a cadence, an event, or until a verifiable goal is reached. It sits above prompt, context, and harness engineering: those improve a single run, while loop engineering governs repeated agent work over time, including budgets, retries, escalation to humans, and stopping conditions.

Here are 40 public repositories matching this topic...

open-tag

Open-source, self-hostable alternative to Claude Tag — a Slack-style workspace where your team and its AI agents (Claude Code, Codex, GitHub Copilot, and more) work as teammates in channels, threads, DMs, and shared tasks. Your data stays on your machines.

  • Updated Jul 10, 2026
  • TypeScript

OpenCode++: a Coding Agent Reliability Harness for OpenCode, adding context, edit boundaries, command evidence, verification gates, impact analysis, and repair loops.OpenCode++:面向 OpenCode 的 AI 编程可靠性增强框架,为其增加上下文管理、编辑边界、命令证据、验证门禁、影响分析与修复闭环能力。

  • Updated Jun 25, 2026
  • TypeScript

An open, natural-language DSL for self-correcting AI coding loops — say what an AI coding agent should build and how to verify it in plain English, and it loops until the check passes. Runs in Claude Code, Cursor, and Copilot.

  • Updated Jul 7, 2026
  • TypeScript
dxkit

Deterministic Stop-gate and code-graph context for AI coding agents: blocks only net-new findings and gives the loop a structural map of the codebase, locally, with no model in the gate.

  • Updated Jul 13, 2026
  • TypeScript