Looper: The AI Junior That Never Forgets the Backlog

I don’t want a vibe-coder.

I want a deterministic, auditable teammate that ships one task at a time, leaves a trail, and doesn’t stop until it delivers.

This obsession started last June. I built llm-loop, a plugin for Simon Willison’s LLM CLI that gave it the one thing it was missing: the ability to keep going. Published to PyPI, it turned a single-turn tool into something that could iterate autonomously.

Around the same time, I had a great chat with Geoffrey Huntley. We’d converged in the same universe—he was pioneering what he called the Ralph Wiggum Loop: autonomous agents that maintain codebases indefinitely. Geoff saw the future before most of us even knew there was a problem to solve.

In September, when Z.ai released GLM-4.5 (referral link that feeds my loops), I built loop.sh—the first version of a simple looping script that used skills to move work forward. It worked, but it was still missing something.

Now, with Codex 5.2 in xhigh mode, everything clicked. The new Looper is built entirely around it—harnessing the power of observability through logs, traceability through a JSON task list, and script flags for tail and status. It’s not just an autonomous coder anymore. It’s an auditable workflow.

Look, I know how this sounds. Others are off building entire orchestrations systems—Steve Yegge’s Gas Town is basically Kubernetes mated with Temporal, with seven worker roles, a tmux UI, and concepts called “Beads” and “Molecules.” It’s designed for running 20–30 Claude Code instances at once. That’s cool, but I wanted something very simple: true to the rough idea of just running a loop, but with some fancy bells and whistles.

There’s a deeper reason for building small wrappers instead of full orchestrators: the model makers themselves are building the best harnesses. Codex CLI comes from OpenAI; Claude Code from Anthropic. They know their models’ token patterns, thinking styles, and tool preferences better than anyone else. Even third-party models like GLM-4.7 on Z.ai feel eerily native in Claude Code—like they were trained or reinforced on Claude Code workflows itself.

Other companies are building their own harnesses too: Charm’s Crush brings glamorous terminal-native AI coding, while OpenCode and Pi Code offer their own takes. But none of this invites me to build a better harness. The ideal form is a small wrapper around something that already works—nothing extra, just structure on top.

Most AI coding tools give you a chatty assistant that’s helpful but forgetful, that re-explains context you’ve already established, that drifts when tasks get complex.

I wanted something else. So I built Looper.

# What Looper Actually Is

At its core, Looper is a tiny bash wrapper around Codex that enforces a strict loop:

• One task per iteration—no partial work, no multitasking, no drift
• JSON backlog as source of truth—the plan and the audit surface are the same file
• Schema-driven updates—every change flows through jq, so nothing is implicit
• JSONL logging—replay, diff, and measure every run
• Forced review pass—a senior-style gate that either adds work or marks the project done

The rule is boring on purpose. Boring scales.

# The Speed You Can Still Intervene At

Here’s what Gas Town and the 20-agent swarms miss: humans become the bottleneck.

When you’re juggling two dozen Claude Code instances, you can’t actually follow what’s happening. You’re along for the ride, hoping the factory doesn’t disembowel you. That’s not autonomy I can trust.

I want to move at a speed where I can still intervene while the system runs in complete autonomy. A day or two for a project? That’s bearable. It gives me space to do other stuff, let Looper chug away, and check in periodically with enough context to redirect if needed. If it’s been coding for 48 hours and I realize the direction is wrong, I can stop it and pivot. It hasn’t gone so far that everything is a loss.

Slow enough to follow. Fast enough to ship.

This speed mirrors the flow state formula: too fast causes anxiety and loss of control; too slow causes boredom and disengagement. A successful looper keeps the challenge level just barely above your ability to intervene manually—which is precisely where optimal experience lives.

# Why a Backlog Changes Everything

Most AI tools make you the bottleneck—constantly feeding them the next instruction. A backlog removes you from the critical path.

Here’s the problem with free-form AI coding: you become the project manager. You’re breaking down tasks, checking completeness, deciding what’s next. The AI is smart, but you’re doing the orchestration.

A backlog inverts this. The AI pulls tasks, completes them, and then—crucially—runs a review pass that either adds new work or marks the project complete.

The review pass behaves like a senior dev: read the whole repo, check against source specs, decide what’s missing. Only the review pass can append the project-done marker.

This means the system can run indefinitely, but still has a hard stop when the backlog is truly exhausted.

# The Shape of the Loop

From my local ~/.looper logs:

• 17 task iterations completed (status=done)
• 12 review passes completed (status=reviewed)
• ~300 command executions total
• Roughly 13 shell commands per task iteration, ~8 per review pass

These are local test runs, not production. But they show the shape: short, consistent loops with predictable tool usage.

# The Anti-Magic Approach

The gap between AI that demos well and AI that ships is in observability, not capability. Structure is how you bridge it.

When every task is explicit and every update flows through a schema, you get traceability for free. No task can sprawl because each iteration has a single objective. The system either completes the work or admits it needs more work.

You can always answer: what changed, why, and in which iteration?

It’s honest.

# From Prototype to Production

The first Looper prototype was built with Claude—you can see the original gist here. The live repo is on GitHub.

I wrapped the release flow into a project skill and a helper script so the whole process is repeatable: test, bump version, tag, push, publish release, update the Homebrew formula.

Because production is what you ship, not what you demo.

# What This All Means

If you’re building with AI, don’t give it free-form leeway. Give it:

• A backlog—so the work is explicit
• A schema—so the updates are mechanical
• A review gate—so completion is honest

Looper is the smallest working proof that this style is not only possible, it’s reliable.

The magic isn’t in the model. The magic is in the constraints.

# What’s Next: Model Interleaving

Here’s something becoming increasingly clear: iteration beats perfection.

A non-SOTA model that can iterate will outperform a SOTA model that can’t. The loop matters more than the model.

GLM-4.7 (referral link) is impressive—the speed, the interleaved thinking pattern, the token efficiency. I’m adding a feature to let you choose: use GLM for task iterations, then run the review pass with Codex xhigh.

This maps to the Oracle-Worker pattern from agentic-patterns.com: cheap models handle bulk work while an expensive model handles planning and review. It’s cost-effective because most compute happens on workers, but quality is preserved because the oracle sets the direction.

But there’s something deeper here. Cursor 2.0’s multi-model ensemble approach shows that combining predictions from multiple models significantly improves final output, especially for harder tasks. Different models have different failure modes, different strengths. When you alternate them, those blind spots cancel out.

The future of Looper isn’t just one model looping. It’s multiple models, interleaved strategically, each covering the others’ weaknesses.

Because reliability isn’t about having the best model. It’s about having the best system.