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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 46 public repositories matching this topic...

Mobile AI Skills, Agents and workflow to help you build mobile apps and games faster. A collection of AI skills, agents, prompts, and workflows for Android, iOS, Flutter, React Native, Unity, and Unreal developers. Use these ready-made tools to write code faster, fix bugs, review code, improve performance, automate tasks, and ship faster with AI.

  • Updated Oct 5, 2026
  • JavaScript

An autonomous product loop for Claude Code that improves a real product overnight: a value gate before any code, an independent validator after, a deterministic driver that knows when to stop, and a one-command installer. Hardened by live runs on an iOS app and its backend.

  • Updated Sep 29, 2026
  • JavaScript

Harness-first agent research: a bounded act→verify loop runner with one-line adapters for 10 lean harnesses (Pi, Hermes, aider, Codex, Goose, Claude, Ollama…) + Crucible, a portable benchmark that scores the harness, not the model — gated Safety×Completion/Path/State Goodput, factorial harness×model×seed, cost & local-vs-cloud routing.

  • Updated Sep 22, 2026
  • JavaScript
pipeshape-claude-plugins

Claude Code plugin that builds deterministic multi-agent dev pipelines — define the graph in YAML, scaffold agents into your repo, run with spec gates, feedback loops, and resume.

  • Updated Sep 16, 2026
  • JavaScript

A best-practices template for AI-assisted development at scale. It turns your repository into institutional infrastructure where AI agents can work sustainably — converging around documentation as a stable attractor across sessions, long cycles, and multi-role collaboration

  • Updated Sep 6, 2026
  • JavaScript

Loop engineering for software development. Turn any repository into a self-organizing AI engineering team — 8 specialists, contract-driven, evidence-gated, parallel feature voyages.

  • Updated Aug 28, 2026
  • JavaScript